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(3 MB)
New Residential Thermostat for Transactive Systems
by
David P. Chassin
B.Sc., Building Science, Rensselaer Polytechnic Institute, 1987
A Thesis Submitted in Partial Fulfillment of the
Requirements for the Degree of
MASTER OF APPLIED SCIENCE
in the Department of Mechanical Engineering
c David P. Chassin, 2014
University of Victoria
All rights reserved. This thesis may not be reproduced in whole or in part, by
photocopying or other means, without the permission of the author.
ii
New Residential Thermostat for Transactive Systems
by
David P. Chassin
B.Sc., Building Science, Rensselaer Polytechnic Institute, 1987
Supervisory Committee
Dr. Nedjib Djilali, Supervisor
(Department of Mechanical Engineering)
Dr. Yang Shi, Departmental Member
(Department of Mechanical Engineering)
Dr. Panajotis Agothoklis, Outside Member
(Department of Electric Engineering)
iii
Supervisory Committee
Dr. Nedjib Djilali, Supervisor
(Department of Mechanical Engineering)
Dr. Yang Shi, Departmental Member
(Department of Mechanical Engineering)
Dr. Panajotis Agothoklis, Outside Member
(Department of Electric Engineering)
ABSTRACT
This thesis presents a residential thermostat that enables accurate aggregate
load control systems for electricity demand response. The thermostat features a
control strategy that can be modeled as a linear time-invariant system for short-term
demand response signals from the utility. This control design gives rise to linear timeinvariant models of aggregate load control and demand response, which is expected
to facilitate the design of more accurate load-based regulation services for electricity
interconnections and enable integration of more highly variable renewable electricity
generation resources. A key feature of the new thermostat design is the elimination
of aggregate short-term load control error observed with existing real-time pricing
thermostats as they respond to price signals.
iv
Contents
Supervisory Committee
ii
Abstract
iii
Table of Contents
iv
List of Tables
viii
List of Figures
ix
List of Abbreviations and Symbols
xi
Acknowledgements
Dedication
xvi
xvii
1 Introduction
1
1.1
Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
1.2
Main Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5
1.3
Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6
2 Demand as a System Resource
2.1
8
Bulk Power System Operations . . . . . . . . . . . . . . . . . . . . .
8
2.1.1
Electric Power Grid Ancillary Services . . . . . . . . . . . . .
9
2.1.2
Ancillary Services Using Load Resources . . . . . . . . . . . .
11
v
2.2
2.3
2.1.3
Demand Response Aggregation Strategies . . . . . . . . . . .
22
2.1.4
Environmental Impacts of Demand Response . . . . . . . . . .
27
Responsive Heating/Cooling Systems . . . . . . . . . . . . . . . . . .
35
2.2.1
Control Automation . . . . . . . . . . . . . . . . . . . . . . .
37
2.2.2
Hierarchical Control . . . . . . . . . . . . . . . . . . . . . . .
40
2.2.3
Transactive Control . . . . . . . . . . . . . . . . . . . . . . . .
43
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
50
3 The New Transactive Thermostat
3.1
3.2
3.3
3.4
3.5
53
Building System Model . . . . . . . . . . . . . . . . . . . . . . . . . .
54
3.1.1
Building Thermal Model . . . . . . . . . . . . . . . . . . . . .
58
Residential Heat-pump Systems . . . . . . . . . . . . . . . . . . . . .
64
3.2.1
Heat-Pump Systems . . . . . . . . . . . . . . . . . . . . . . .
65
3.2.2
Thermostat Control . . . . . . . . . . . . . . . . . . . . . . . .
66
3.2.3
Forced Air System Delays . . . . . . . . . . . . . . . . . . . .
67
Conventional Thermostat Performance . . . . . . . . . . . . . . . . .
68
3.3.1
Deadband Value
. . . . . . . . . . . . . . . . . . . . . . . . .
69
3.3.2
Deadband Overshoot . . . . . . . . . . . . . . . . . . . . . . .
70
3.3.3
Occupancy Schedule Set-Up/Set-Back . . . . . . . . . . . . .
71
3.3.4
Auxiliary (Supplemental) Heating Operation . . . . . . . . . .
73
3.3.5
Emergency Heating Operation . . . . . . . . . . . . . . . . . .
73
New Thermostat Design . . . . . . . . . . . . . . . . . . . . . . . . .
74
3.4.1
High-Pass Filter . . . . . . . . . . . . . . . . . . . . . . . . . .
78
3.4.2
Comfort Gain Parameter . . . . . . . . . . . . . . . . . . . . .
78
3.4.3
House Price Response . . . . . . . . . . . . . . . . . . . . . .
79
Control Performance Metrics . . . . . . . . . . . . . . . . . . . . . . .
81
3.5.1
81
Comfort Control Performance . . . . . . . . . . . . . . . . . .
vi
3.5.2
Energy and Cost Performance . . . . . . . . . . . . . . . . . .
82
3.5.3
Compressor Wear and Tear . . . . . . . . . . . . . . . . . . .
83
4 Experiment Design
4.1
85
Location and Weather . . . . . . . . . . . . . . . . . . . . . . . . . .
86
4.1.1
Reference Cities . . . . . . . . . . . . . . . . . . . . . . . . . .
87
Reference House Design . . . . . . . . . . . . . . . . . . . . . . . . .
87
4.2.1
End-Use Load Shapes . . . . . . . . . . . . . . . . . . . . . .
88
4.2.2
Occupancy Schedules . . . . . . . . . . . . . . . . . . . . . . .
90
4.2.3
Indoor Air-Temperature Set Point . . . . . . . . . . . . . . . .
90
Heat-Pump Sizing . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
91
4.3.1
Cooling Capacity . . . . . . . . . . . . . . . . . . . . . . . . .
91
4.3.2
Heating Capacity . . . . . . . . . . . . . . . . . . . . . . . . .
92
4.3.3
Auxiliary Heating Capacity . . . . . . . . . . . . . . . . . . .
93
4.3.4
Design Capacity . . . . . . . . . . . . . . . . . . . . . . . . . .
93
Electricity Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
94
4.4.1
Fixed Price Tariff . . . . . . . . . . . . . . . . . . . . . . . . .
96
4.4.2
Time-of-Use Tariff . . . . . . . . . . . . . . . . . . . . . . . .
97
4.4.3
Real-Time Price Tariff . . . . . . . . . . . . . . . . . . . . . .
97
4.4.4
Revenue Neutrality . . . . . . . . . . . . . . . . . . . . . . . .
98
4.5
Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . .
99
4.6
Summary of Experiment Design . . . . . . . . . . . . . . . . . . . . .
101
4.2
4.3
4.4
5 Evaluation of New Thermostat
103
5.1
Energy Use Impacts
. . . . . . . . . . . . . . . . . . . . . . . . . . . 103
5.2
Comfort Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
5.3
Economic Impacts
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
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5.4
Feeder Load Control Impacts . . . . . . . . . . . . . . . . . . . . . . 113
5.5
Open Issues and Opportunities . . . . . . . . . . . . . . . . . . . . . 114
5.6
Summary of Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
6 Conclusions
118
6.1
Main Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . .
121
6.2
Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
A Tables in SI Units
125
B Simulation Models
127
B.1 Common Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
B.1.1 Demand Response Controllers . . . . . . . . . . . . . . . . . . 127
B.1.2 Market Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
B.1.3 Occupancy Schedules . . . . . . . . . . . . . . . . . . . . . . . 129
B.1.4 End-use Load Monitoring . . . . . . . . . . . . . . . . . . . . 130
B.2 Study Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
B.2.1 Seattle Winter . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
B.2.2 Seattle Summer . . . . . . . . . . . . . . . . . . . . . . . . . . 135
B.2.3 Phoenix Summer . . . . . . . . . . . . . . . . . . . . . . . . . 138
B.2.4 Miami Summer . . . . . . . . . . . . . . . . . . . . . . . . . . 142
B.2.5 Feeder Response . . . . . . . . . . . . . . . . . . . . . . . . . 145
B.3 Modifications to GridLAB-D . . . . . . . . . . . . . . . . . . . . . . . 146
C Glossary
158
Bibliography
163
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List of Tables
Table 2.1 Emission reductions relative to no wind generation . . . . . . . .
30
Table 3.1 Fast response of load control for t < ts . . . . . . . . . . . . . .
79
Table 4.1 Cities and climate conditions . . . . . . . . . . . . . . . . . . . .
87
Table 4.2 IECC end-use energy for model home in study cities . . . . . . .
88
Table 4.3 House design . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
88
Table 4.4 ELCAP loadshapes update with RBSA results . . . . . . . . . .
90
Table 4.5 Occupancy and thermostat set point schedule . . . . . . . . . .
90
Table 4.6 Heatpump design criteria and capacities for study cities . . . . .
93
Table 4.7 Salt River Project (SRP) inclining block rates in Phoenix . . . .
94
Table 4.8 Fixed price tariffs for study cities . . . . . . . . . . . . . . . . .
96
Table 4.9 Seasonal time-of-use rates . . . . . . . . . . . . . . . . . . . . .
97
Table 4.10Residential energy cost with demand response inactive . . . . .
99
Table 4.11Residential energy use with demand response inactive . . . . . .
99
Table 4.12Summary of Experiment Model Features . . . . . . . . . . . . . 102
Table 5.1 Heating and cooling relative set point errors . . . . . . . . . . . 106
Table 5.2 TOU demand elasticity . . . . . . . . . . . . . . . . . . . . . . .
111
Table 5.3 RTP demand elasticity . . . . . . . . . . . . . . . . . . . . . . . 113
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List of Figures
Figure 2.1 Conceptual model of modern electricity operations . . . . . . .
9
Figure 2.2 Basic heating/cooling system (Source: www.progreencompany.com) 37
Figure 2.3 Classic Honeywell “round” thermostat . . . . . . . . . . . . . .
38
Figure 2.4 Capacity market clearing (left) and thermostat bid/set for cooling
conditions (right) mechanisms . . . . . . . . . . . . . . . . . . .
46
Figure 2.5 Example of drift in demand response using transactive control
(Data courtesy Jason Fuller, Pacific Northwest National Laboratory) 50
Figure 3.1 Conventional building air temperature control system . . . . . .
55
Figure 3.2 Direct load control by interruption (left), by thermostat offset
(center), and by incentive signal (right) . . . . . . . . . . . . . .
56
Figure 3.3 Thermostat set point control aggregate demand response event,
showing aggregate load (top) and heating system state evolution
for the population (bottom) . . . . . . . . . . . . . . . . . . . .
57
Figure 3.4 Equivalent thermal circuit for a residential building . . . . . . .
59
Figure 3.5 Air-source heat pump system diagram . . . . . . . . . . . . . .
64
Figure 3.6 Conventional thermostat wiring diagram . . . . . . . . . . . . .
66
Figure 3.7 Conventional thermostat design (top left) and application (bottom
left), deadband control (top right) and refractory state control
(bottom right). . . . . . . . . . . . . . . . . . . . . . . . . . . .
68
Figure 3.8 Proposed new thermostat design . . . . . . . . . . . . . . . . .
75
x
Figure 3.9 Slow response controller design . . . . . . . . . . . . . . . . . .
76
Figure 3.10Fast response controller design (ts = 5 minutes for all discretetime control elements) . . . . . . . . . . . . . . . . . . . . . . .
77
Figure 3.11Diagram of subsampling response of the new thermostat . . . .
79
Figure 3.12House heating (left) and cooling (right) design response for system
on (red/blue) and off (black) with neutral-mass response condition
(dotted) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
80
Figure 4.1 Single house model (left) and utility feeder model (right) . . . .
85
Figure 4.2 1993 ELCAP loadshapes adjusted with 2013 RBSA demand levels 89
Figure 4.3 Columbus demonstration project comfort settings (Source: Steve
Widergren, Pacific Northwest National Laboratory) . . . . . . .
91
Figure 4.4 Short-term RTP volatility (top) and RTP means (bottom) . . .
98
Figure 4.5 Cooling and heating discomfort degree-hours . . . . . . . . . .
101
Figure 5.1 Total home energy use with demand response active . . . . . . 104
Figure 5.2 Heating and cooling discomfort degree hours for 1◦ F deviations
107
Figure 5.3 Energy cost with demand response active . . . . . . . . . . . . 108
Figure 5.4 Time-of-use demand, revenue neutral and revenue expansion paths109
Figure 5.5 Feeder open-loop load control response to price . . . . . . . . . 114
xi
List of Abbreviations and Symbols
Abbreviations
ACE
Area control error
AEP
American Electric Power [Company]
AGC
Automatic generation control
BA
Balancing authority
BPA
Bonneville Power Administration
CPS
Control performance standard
CPP
Critical peak price
DSM
Demand side management
ED
Economic dispatch
ELCAP
End-use load consumer assessment program
FERC
Federal Energy Regulatory Commission
GENCO
Generation company
HVAC
Heating ventilating and air-conditioning
ISO
Independent system operator
LMP
Locational marginal price
LOLP
Loss of load probability
LSE
Load serving entity
LTI
Linear time-invariant
xii
NEEA
Northwest Energy Efficiency Alliance
NERC
North American Electricity Reliability Corporation
NIST
National Institute of Standards and Technology
PNWSG
Pacific Northwest Smart Grid [demonstration project]
RBSA
Residential Building Stock Assessment
RTO
Regional transmission operator
RTP
Real-time price
SCADA
Supervisory control and data acquisition [system]
TEC
Time-error correction
TOU
Time-of-use [price]
TRANSCO
Transmission company
UC
Unit commitment
UFLS
Under-frequency load shedding
US
United States
UVLS
Under-voltage load shedding
Symbols
Btu.h/◦ F
a
The second-order term of the house transfer function.
AH
The total area of horizontal glazing surfaces.
ft2
An
The area of the nth glazing surface.
ft2
AV
The total area of vertical glazing surfaces.
ft2
b
The first-order term of the house transfer function.
c
The zeroth-order term of the house transfer function.
CA
The heat capacity of the air volume in the house.
Btu/◦ F
CM
The heat capacity of the mass of the house.
Btu/◦ F
Btu/◦ F
Btu/◦ F.h
xiii
d
The magnitude of the constant heat term of the house transfer
Btu/h
function.
D
The thermostat deadband.
◦
F
E
The indoor air temperature control error.
◦
F
f
The magnitude of the heat unit-step term of the house transfer
Btu/h
function.
ID
The diffuse global irradiance.
W/m2
IN
The direct normal irradiance.
W/m2
k
The consumer’s comfort setting
K
The comfort control gain
M
The heating/cooling system mode (−1 = cooling, 0 = off, 1 =
(p.u.)
$/◦ F
(p.u.)
heating, 2 = auxiliary).
ND
The number of days in a simulation.
NO
The number of occupants in a house.
NT
The number of temperature samples taken.
p
The pole associated with the indoor air temperature of the house.
P
The energy price signal.
q
The pole associated with the mass temperature of the house.
Q
The total heat gain (loss) to the air in a house.
Btu/h
QA
The heat gain (loss) to the indoor air in a house.
Btu/h
QC
The primary heat-pump cooling capacity.
Btu/h
QE
The heat gain from gas and electric end-use systems in the house.
Btu/h
QI
The internal heat gains to the air in a house.
Btu/h
QS
The solar heat gains to the air in a house.
Btu/h
QH
The primary heat-pump heating capacity.
Btu/h
QM
The heat gain (loss) to the mass of the house.
Btu/h
/h
$/MWh
/h
xiv
QO
The heat gain from occupants in the house.
Btu/h
QV
The heat gain (loss) from ventilation air changes through the
Btu/h
house.
QX
The auxiliary heating capacity.
Btu/h
s
The complex Laplace variable.
/h
t
The real time variable.
T
The indoor air temperature relative to the equilibrium tempera-
h
◦
F
ture.
Ṫ
The first derivative of T .
T̈
The second derivative of T .
T0
The initial indoor air temperature relative to the outdoor air
◦
◦
F/h
F/h2
◦
F
temperature.
Ṫ0
The initial rate of change of the indoor air temperature relative
◦
F/h
to the outdoor air temperature.
◦
TA
The measured indoor air temperature in a house.
ṪA
The measured indoor air temperature change in a house.
TD
The desired indoor air temperature in a house.
◦
F
TM
The mass temperature of the house.
◦
F
ṪM
The rate of change of the mass temperature of the house.
tmin
The minimum system runtime.
h
tmax
The maximum system runtime before auxiliary heating is en-
h
◦
◦
F
F/h
F/h
gaged.
TO
The outdoor air temperature.
◦
F
Tp
The initial air temperature response of the house.
◦
F
Tq
The initial mass temperature response of the house.
◦
F
ts
The temperature sampling or price signaling interval.
h
xv
◦
T∞
The equilibrium air temperature of the house.
F
u(0)
The unit-step function applied at time t = 0.
UA
The conductance of the house envelope.
Btu/◦ F.h
UM
The interior mass surface conductance.
Btu/◦ F.h
V̇
The ventilation rate of the house.
W
The measured power demand of the house.
(p.u.)
/h
kW
Greek Symbols
αn
The direct beam incidence angle of the nth glazing surface.
∆T
The thermostat setback temperature offset.
ρG
The glazing shading coefficient.
deg
◦
F
(p.u.)
xvi
ACKNOWLEDGEMENTS
I am most thankful to Ned Djilali for encouraging me to pursue my dream of
completing my graduate studies after such a long hietus and for his guidance during
this research. My thanks also to Pan Agathoklis and Jakob Stoustrup for their
technical advice and insight. Finally I am grateful for the support from Pacific
Northwest National Laboratory with special recognition to Mark Morgan, Paul Skare,
Ron Melton, Steve Shankle, Rob Pratt and Suresh Baskaran for their support and
encouragement in the pursuit of my goals.
I would like to thank all those who contributed to the development of GridLAB-DTM ,
the open-source grid simulation tool used in this thesis. I offer special thanks to Jason
Fuller, the current manager of GridLAB-D who valiantly agreed to take over management of the development team during the time I worked on this thesis. GridLAB-D
was developed at Pacific Northwest National Laboratory under funding from the US
Department of Energy’s Office of Electricity.
I offer very special thanks to my family for their support and patience, especially
Norma, Isaac, Forrest, and Nik Chassin and Ann and Jeffry Mallow for always being
ready with a helping hand and covering for me while I was away.
Victoria BC, 2014
xvii
DEDICATION
To my beloved Norma
1
Chapter 1
Introduction
1.1
Motivation
In 2011 the United States Federal Energy Regulatory Commission issued FERC
Order 745, which amends its regulations under the Federal Power Act to ensure that
demand response resources with the capability to balance supply and demand can
participate in organized wholesale energy markets as an alternative to generation
resources. The order introduced requirements that a) dispatched demand response
resources satisfy a net-benefit test, and b) demand response resources are compensated
for the services they provide to the energy market at the locational marginal price
(LMP). This approach for compensating demand response resources was intended to
“ensure the competitiveness of organized wholesale energy markets and remove barriers
to the participation of demand response resources, thus ensuring just and reasonable
wholesale rates” [1].
The US Court of Appeal’s recent decision to vacate the order calls into question
FERC’s entire approach to demand response [2]. The court found that FERC does
not have jurisdiction in matters regarding demand response even when they affect
2
wholesale markets. The decision does nothing to solve the problem that FERC
was trying to address. Furthermore it remains to be seen whether Judge Brown’s
dissenting view can prevail and FERC will retain its jurisdiction in all matters relating
to wholesale energy markets, including demand response.
Regardless, critics of FERC Order 745 have pointed out that demand response is
essentially unlike generation because it is exercised as a call option on the spot energy
market, the value of which is the LMP minus the strike price. In the case of retail
consumers this price is the tariff rate [3]. Others contend that the value of demand
response is the marginal forgone retail rate [4]. However it is valued, the question
remains whether FERC Order 745 effectively guarantees double compensation for
demand response by providing to responsive load both the cost savings from energy
not provided by the retailer and an LMP payment for not using same increment of
energy. Such a signal might lead a firm to halt operations even though the marginal
benefit of consuming electricity exceeds the marginal cost at LMP. In his comments
to the commission during prosecution of the rule-making process for the order Hogan
argued that“the ideal and economically efficient solution regarding demand response
compensation is to implement retail real-time pricing at the LMP, thereby eliminating
the need for [wholesale] demand response [compensation].”
These arguments are academic if demand response cannot be employed broadly
for technical or economic reasons. To resolve the technical questions regarding the
large-scale feasibility of near real-time demand response the US Department of Energy
funded the Olympic Peninsula [5] and Columbus Ohio [6] demonstration projects.
The objective of both projects was to address the open technical questions regarding
the so-called “price-to-devices” challenge [7] by demonstrating the transactive control
approach to integrating small-scale electric equipment with utility electric power
distribution system operations as a first step toward integrating distributed generation
3
and demand response into wholesale operations. Transactive control in this context
refers to a distributed resource allocation strategy that engages both electricity
suppliers and consumers using market-based mechanisms at the retail level for the
purpose of enabling demand response by the utilities at the wholesale level [8].
Without mechanisms like transactive control, price-responsive load requires engaging a very large number of very small participants in the unit-commitment and
economic dispatch process. The computational complexity of the optimal dispatch
problem makes this impractical for anything more than the thousands of larger suppliers already involved. Strategies extant for addressing this challenge generally involve
aggregation at the distribution retail level that enables the integration of demand
units by proxy of a reduced number of larger representative units. Private entities
such as Enernoc have based their business models on this approach. These are used
primarily on commercial buildings where the control systems are more amenable to
this integration and the number of control points per Watt of resource is lower than it
is for residential buildings. Unfortunately, this leaves nearly half the available building
load untapped as a demand resource for utilities.
Using markets to solve electricity resource allocation problems at the wholesale
bulk system level is well-understood [9]. But transactive control takes the idea to
the retail level by solving the resource allocation problem at the distribution level
first before integrating it at the wholesale level. These retail markets are designed
to find an allocation of distribution capacity, distributed generation and demand
response to resolve how much wholesale energy resource is required and determine
how much distributed generators should produce and customers can consume in the
coming time interval. Transactive control systems use distribution capacity markets to
determine the energy price that minimizes the imbalance between supply and demand
for electricity for participating equipment during the next operating interval [10]. The
4
system computes a 5-minute retail real-time price (RTP) for energy that reflects the
underlying wholesale LMP plus all other distribution costs and scarcity rent arising
from distribution constraints. In cases where large amounts of renewable resources
are available the real-time price can be less than the LMP. Negative prices are even
possible when a surplus of must-run generation is available. The RTP comes under
a new tariff presumably designed to be revenue neutral in the absence of demand
response.
Distributed generation, load shifting, demand curtailment, and load recovery can
be all induced by variations in real-time prices. Given these responses transactive
control systems can reduce the utility’s long-term exposure to price volatility in the
wholesale market and the costs of congestion on the distribution system [11]. These
can reduce the long-term average cost of energy for consumers who are willing to
forgo consumption in the very short-term. Short-term retail prices are discovered
using a feeder capacity double auction and these prices can help manage distribution,
transmission or bulk generation level constraints. Distributed generation and demand
response are dispatched based on consumers’ preferences, which they enter into an
advanced thermostat that acts as an automated agent bidding for electricity on their
behalf. Transactive thermostats both bid for the electricity and modulate consumption
in response to the market clearing price. By integrating this response to a price signal
that reflects anticipated scarcity, the system closes the loop on energy delivery and
improves resource allocation efficiency by ensuring that consumers who value the
power most are served prior to those who are willing to forgo it for a short time. At the
same time, consumers provide valuable services to the wholesale bulk power system
and experience reduced energy costs at times of day when they express preferences for
savings over comfort.
5
1.2
Main Contributions
The success of demand response programs is highly dependent on the human, technical,
and economic behaviors that affect the individual devices that participate in them.
Historically human behavior has largely been addressed through conservation and
efficiency education and marketing programs. So-called “smart grid” technologies
have focused primarily on the technical aspects of system-device communication and
aggregate load control designs, typically for “one-shot” demand response to meet
peak-load reduction objectives. Transactive control has also focused on the economic
aspects of engaging devices in the short-term demand response such that some of the
benefits accrue to consumers who offer more flexibility and control over when and how
much of their device’s capabilities are deployed.
The most significant objection utilities have to demand response is that demand
resources are unreliable and unpredictable. None of the approaches extant have addressed the degree to which individual device controls support reliable and predictable
aggregate fast-acting demand response. This thesis presents a new control strategy
that addresses these concerns and applies it to an important class of load, namely
the residential heating, ventilating and cooling (HVAC) equipment, which dominate
demand response programs in certain key regions of the United States. In doing so,
this thesis offers the following main contributions:
1. A new thermostat design that enables more reliable and predictable aggregate
demand response resources and makes them available to utilities for shortduration fast-acting reliability services. This overcomes the concerns that utilities
have with using demand response, particularly in resource planning when they
have the greatest financial impact and in system operation when they have the
greatest technical impact.
6
2. A comprehensive set of performance metrics for aggregate demand response
control using HVAC. This gives utilities the ability to rigorously design, monitor,
and optimize the performance of aggregate demand response control systems.
Consequently, utilities are able to provide more reliability services based on
demand response to bulk system operators and derive economic benefits that
can be passed on to the consumers who provide the underlying resources.
3. An economically and technically robust design for residential HVAC equipment
controls that supports aggregate demand response. This gives consumers the
ability to better control when and to what degree their systems are participating
in demand response services provided by the utility to the bulk system operators.
The application of these results to HVAC should be construed to limit their
generality for all thermostatic loads or thermal systems in general, particularly those
for which electric demand is influenced by price signals.
1.3
Thesis Outline
This thesis is structured as follows. Chapter 2 provides a critical assessment of demand
response in bulk electric power systems and reviews current approaches to delivering
demand response resources to wholesale power markets. Chapter 3 introduces a new
design for HVAC controls that greatly facilitates the aggregation and delivery of
demand response resources by load serving entities to bulk power system operators.
Chapter 4 examines this new HVAC control approach using classical control theory,
with particular attention to the comfort and cost response to various inputs and
disturbances commonly experience in residential buildings. Chapter 5 examines the
aggregate impacts of using the new thermostat design by comparing the price-response
performance of the new thermostat controls to the transactive designs tested in
7
previous field demonstrations. Chapter 6 presents the conclusions and recommends
directions for future research. Appendix A presents the tables from Chapters 1-5 in
SI units. Appendix B contains the source code used to run the numerical experiments
in GridLAB-D. Appendix C contains a glossary of terms of art used in this thesis.
8
Chapter 2
Demand as a System Resource
This chapter discusses the background of bulk power system operations, the role of
demand response in providing energy, capacity, and reliability services, and how the
concept of transactive control enables loads to provide these services. Some challenges
associated with transactive control were uncovered by two field projects designed
and deployed to demonstrate transactive control. These projects are discussed and
problems arising from the existing design are examined.
2.1
Bulk Power System Operations
Responsibility for the reliability of electricity interconnections is shared by all the
operating entities within each interconnection. In a traditional power system these
entities are vertically integrated. A committee process involving all the entities
within each power pool establishes the reliability criteria utilities use for planning and
operations. The operating entities typically belong to larger regional coordinating
councils so that they can coordinate their criteria with neighboring power pools. These
regional councils have been organized since 1965 under what is now called the North
America Electric Reliability Corporation (NERC), which establishes the recommended
9
Figure 2.1: Conceptual model of modern electricity operations
standards for system reliability [12]. In a restructured system the responsibility for
various aspect of planning and operation are divided among various entities that do
not typically fall within the traditional vertically integrated utility operation, as shown
in Figure 2.1. The result is a complex network of interacting controls operating over a
wide range of temporal and physical scales that requires a very complex information
flow to sustain it.
2.1.1
Electric Power Grid Ancillary Services
With the evolution toward market-based operations in recent decades vertically integrated operating entities have been broken up into generation companies (GENCOs),
transmission owners (TRANSCOs), load serving entities (LSEs), and energy traders
that do not own assets. Collectively these are referred to as the market participants
10
[13]. The responsibility for ensuring the reliability of a control area is delegated to
independent system operators (ISO) or regional transmission operators (RTO). In
general market participants have the duty to provide accurate data about their assets
and prices as well as follow the dispatch orders of the ISO/RTO. The ISO/RTO
has the duty to ensure that each market participant meets the reliability rules and
determines the dispatch orders necessary for the electricity supply and demand to
match according to NERC’s reliability standards. This system is predicated on a
successful competitive market in which private decentralized trading and investment
design work to allow substantial commercial freedom for buyers, sellers, and various
other types of traders [14].
The method used to implement this dispatch model uses a two-stage process
referred to as the unbundled or two-settlement approach:
1. Unit-commitment (UC) is a days-ahead process that determines the hourly
operating set points of the generation assets based on their bid energy prices
and the forecast system load.
2. Economic-dispatch (ED) is an hours-ahead process that determines the real-time
generation schedules and procures additional supply to ensure system reliability.
This two-settlement approach can be used for both regulated and unregulated
markets and the analysis method is similar for both short-term operations and longterm planning with the only caveat that ISOs must perform the system studies for
deregulated markets to determine whether additional generation and transmission
may be required.
The timeframes for planning and operations can be separated into the following
security functions [15]:
11
1. Long term planning (> 2 years) determines needed investments in generation
and transmission.
2. Resource adequacy (3-6 months) secures generation to serve expected load and
sets long-term maintenance schedules.
3. Operations planning (1-2 weeks) coordinates short-term maintenance schedules
and long-lead generation.
4. Day-ahead scheduling (12-24 hours) performs a security-constrained UC using
energy bids.
5. Real-time commitment and dispatch (5-180 minutes) performs real-time securitybased economic balancing of generation and load.
6. Automatic control (< 5 minutes) performs output control of generating resources
and actuation of protection system.
For time-intervals shorter than 5 minutes, the reliability of the system is delegated
entirely to the generators and load-serving entities according to reliability standards
promulgated by NERC and coordinated separately by each interconnection.
2.1.2
Ancillary Services Using Load Resources
Modern bulk electric interconnections are constrained by the physical requirement
that electric energy is not stored in any substantial way during system operations.
Any mismatch between generation and load will result in a rapid change in frequency
over the entire interconnection. These frequency changes can damage equipment
and increase electrical losses if left unchecked. Historically utility operations used
controllable generation to provide the ancillary balancing services needed to “follow”
load to ensure that at every moment supply precisely matches demand and losses. To
12
make electric utility planning and operation economical and manageable the industry
divides generation resources into three principal categories: base-load, intermediate
load, and peak load [16].
Base-load generation is the bottom portion of the supply stack that essentially
runs uninterrupted throughout the year (except during maintenance or unplanned
outages). Intermediate generation runs continuously but only seasonally as the diurnal
load nadir rises and falls. Peak generation is the supply that must be started and
stopped daily to follow the diurnal load variations and meet the annual peak load.
Each of these types of generation may also provide regulation and reliability resources
to help control frequency and respond to contingencies and emergencies in generation
and transmission operations.
For decades load had not been considered part of the overall planning and operations
model of electric interconnections except to the extent that its growth sets the
conditions for capacity planning. But in recent years increasing attention has been
directed to understanding the role that load can play as a resource beyond conservation
measures that reduce the need for new conventional generation resources. Load is now
seen as a potential resource that avoids using generation resources in inefficient ways
and enables the addition of generation resources that exhibit substandard performance
characteristics when operating under the conventional load following paradigm [17].
Today the term “demand resource” encompasses a wide range of products, services,
and capabilities related to the control and management of load in electric systems.
Prior to the advent of “smart grid” technology demand resources were primarily
considered for planning purposes, such as demand-side management (DSM) programs,
and very limited operational purposes such as in extremis under-frequency or undervoltage load shedding programs (UFLS/UVLS). DSM programs are planning programs
that focus on energy efficiency and other long-term demand management strategies to
13
reduce load growth so that the need for significant new generation capacity investments
can be reduced or deferred. Generally these programs pay for themselves by reducing
capital costs for a number of years, possibly indefinitely. DSM programs helped
the industry transition from its pre-1970s 7% annual capacity growth to the sub-3%
growth prevalent today in modern electricity interconnections.
But DSM programs have a number of long-term limitations that prevent their
application to other system planning or operations objectives. First, energy efficiency
generally has a diminishing marginal return because every additional dollar invested
replaces more efficient load than the last dollar invested. In addition, DSM programs
can give utilities a perverse incentive to substitute investments in a few larger, presumably more efficient, base load units with numerous smaller, generally less efficient,
intermediate units or even very inefficient “peaker” units. Finally, DSM programs
generally do not provide the capabilities and controllability needed to address emerging planning and operations challenges such as generation intermittency, the lack of
transmission capacity investments, evolving load characteristics, new ancillary service
market designs, and short-term/real-time energy price volatility [18].
On the other end of the spectrum, UFLS and UVLS are strictly operations programs
that focus on very short-term load curtailments under severe contingencies. They
are used when all or part of the electric interconnection is threatened by a large
unexpected loss of generation or system separation that creates a power imbalance
which can only be remedied by drastic and immediate reductions in load.
These protective load shedding programs have important limitations because they
are pre-programmed actions armed to respond to specific circumstances identified
during planning studies. They are not the flexible and graduated responses needed
for more general and frequent regulation and balancing operations. Load shedding
programs also tend to indiscriminately disconnect loads and do not have the ability
14
to affect only less critical end-uses such as air-conditioners and water-heaters. Some
recent trends and developments in more advanced load control address a wider range
of short-term operational and economic system needs. These actively controlled loads
are called demand response. The focus of current interest is on the benefits and costs,
as well as understanding and mitigating the limitations of demand response systems.
Demand Response Resources
Demand response programs have generally been divided into two major categories:
incentive-based programs and price-based programs. Both categories recognize that
there is an important economic component to developing demand response capabilities
in electric systems, but achieve the economic benefits in very different ways. As
a general rule, incentive programs are contractual, typically bilateral arrangements
between customers and system operators to provide direct load control, interruptible
load, or market-based control strategies for emergency reserves and ancillary services.
In contrast, price-based programs use utility rate structures and energy prices such
as time-of-use rates, critical-peak pricing, or real-time pricing to drive demand to be
responsive to system conditions through economic signals as a proxy for direct control
signals [19].
The ability of load to provide planning or operations resources is limited by
1) our lack of understanding of the intrinsic nature of the devices and equipment
composing the end-use loads and the constraints arising from consumer behavior and
expectations; 2) our inability to control those end-use loads in an appropriate and
dependable manner, and 3) our inability to validate, verify and meter each rate payer’s
contributions to system planning and operation.
15
Load Modeling
The electric utility industry is extremely risk-averse because such a high value is placed
on system reliability. As a result new technology is often limited by the ability of
planners to model its effect in the planning studies used to establish system operating
limits, and by the ability of operators to control those technologies when deployed in
real-time. In both cases, the challenge is not only modeling the technology itself, but
also more critically simulating how the technology interacts with other resources in the
bulk power system. In the case of loads as resources for system planning and operation
this modeling issue centers on three fundamental questions: 1) How do electric loads
behave at various times of day, week, year? 2) How does end-use composition evolve
over these time frames? And 3) how does the control of loads affect these behaviors
in shorter time horizons?
Load behavior is determined by both the electro-mechanical properties of the
devices and equipment connected to the electric system and by the behavior of the
consumers of the services they provide. As a general rule utilities categorize residential
loads by end-use, such as cooling, heating, refrigeration, lighting, cooking, plugs,
washing, and drying. In commercial buildings other end-uses such as computing,
process pumping, conveying, and other services are also considered. Daily, weekly, and
seasonal load-shapes are associated with each of these end-uses to provide analysts
with an empirical data set from which to estimate load under different conditions. Load
shapes have the advantage of capturing in a single data set both the electro-mechanical
behavior and the consumer behavior that gives rise to the overall shape of loads [20].
Unfortunately these load shapes have a serious drawback when one attempts to
determine the degree to which a load changes in response to short-term signals such
as dispatch commands, real-time prices, frequency or voltage fluctuations: load shapes
contain no information about the temporal transfer of demand for energy, power and
16
ramping behavior. Devising load models that incorporate these remains an ongoing
area of research and tools such as LOADSYN [21], the WECC Composite Load Model
[22], and GridLAB-D [23] partly address this problem.
Load composition models were developed to address electromechanical questions
that generally do not arise when considering load behavior over hours or more. Each
load is composed of electrical subcomponents that have independently changing subhourly electro-mechanical characteristics. Induction motors of different types, sizes
and control may start and stop, electronic power drives may be used, and the overall
mix of static power, current, and impedance may change very quickly in response to
dynamic frequency/voltage events, economic or dispatch signals, whether due to the
normal internal control behavior or equipment protection subsystems. Although the
overall energy consumption on the hourly timescale may be described well using load
shape data, the sub-hourly dynamics of power demand may be quite volatile and is
often poorly understood. This lack of understanding can present system planners and
operators with challenges for which few tools exist, as has been noted in the case of
fault-induced delayed voltage recovery [24].
Load diversity is an emerging challenge when external control signals are applied
to devices and equipment. Under normal operating conditions loads that cycle on
and off are assumed to have high diversity, meaning that the start and stop times
are independent of bulk system conditions and thus uncorrelated to each other. The
difficulty is that diversity is a property of loads similar to entropy; it is difficult to
directly observe but can be influenced by external forces, such as load-shift or load-shed
control signals. Because diversity is not a property of individual loads it can only be
measured meaningfully relative to a reference state, such as the equilibrium state of
a class of loads. Conventional models of loads assume the diversity is maximal, i.e.,
at equilibrium. But in practice load control strategies reduce diversity, sometimes
17
to a significant degree. In spite of these challenges models that indirectly consider
the entropic properties of certain load classes have been developed and applied to
load control problems with some success [25] [26] [27]. But a comprehensive and
theoretically sound model for diversity continues to elude load modelers and this
remains an open area of research.
Human behavior is a critical factor affecting load that must be considered when
designing load control programs. Utilities must consider two distinct aspects of human
behavior to determine the viability and success of a load control program. The first is
customer recruiting and retention and the second is real-time consumer participation1 .
Demand response program marketing is primarily based on economic claims but
often includes an environmental component. Customer expectations are set during
the recruiting phase when utilities make a cost-benefit case for customers to opt-in
to demand response programs. After customer acquiescence, technology is usually
deployed in the customers’ facilities and consumers are presented with behavioral
choices by the technology. The frequency of these choices can range from daily,
such as postponing a load of laundry, to seasonal, such as resetting a thermostat.
Expecting consumers to make choices more than once a day for any particular end-use
is generally regarded as impractical. It is also usually ineffective to ask consumers
to make choices less frequently than seasonally [5]. Mitigating consumer fatigue and
providing continuous education have also been observed to be factors in ensuring
that demand response programs are cost-effective and sustainable [28] [29]. Finally,
utilities frequently face fairness and “free-rider” questions when customers sign-up
for programs but provide no marginal benefit to the utilities because either they
already exhibited the behavior sought, or the utility never calls on them to exhibit
the desired behavior [30]. Ultimately the long-term effectiveness of demand response
1
The customer pays for electric services but may not be the same person as the consumer who
uses the end-use service.
18
programs and the technologies that supports them hinges on whether the individual
and aggregate value outweigh the individual and aggregate impacts. Any disconnect
between customers/consumer short-term/long-term value/impacts and they will not
remain in the program long enough for the program to pay for itself let alone provide
the anticipated system benefits to the utilities and system operators [31].
Until the advent of utility deregulation, demand response programs were the
exclusive purview of utilities and regulated accordingly. However, in regions where
vertical integration has been overcome, third-party aggregation has become a viable
business model for providing demand response from many smaller customers as a single
homogeneous capability that is easier for a utility or an ISO to interact with. By using
on-site control technology, utility service contracts, and rebate programs aggregators
can create both arbitrage and value-added opportunities from which to generate
sufficient revenue. In some cases, monopsony/monopoly conditions can emerge as a
result of regulatory intervention, technology locked-in, high front-end equipment costs
and high back-end system integration costs [32]. A recent additional concern is that
demand response aggregation is potentially subject to FERC jurisdiction to the extent
that aggregators acquire and deliver resources across FERC jurisdictional boundaries
or interact with ISO and RTO entities subject to FERC oversight. Indeed FERC orders
affecting how demand response is compensated in energy markets raise the question
of whether and how it might intervene regarding demand response compensation in
ancillary service markets [1].
Load models ultimately are embodied in the simulation tools utilities use in planning
studies and operational analysis. These include forecasting models and even billing
systems where baseline load models are part of the service contract. But new load
models can take a very long time to be adopted by industry and become commercially
available in planning and operations products. For example, the Western Electricity
19
Coordinating Council (WECC) Load Modeling Task Force began developing a new
load model in 2001 but it was not adopted until the WECC Summer 2013 studies. In
the interim a flat 20% induction motor load model was used after it became apparent
that the standard load model was in part responsible for the discrepancies observed in
the August 1996 outage studies [33]. Such delays can significantly reduce the impact
and potential benefits of load control technology, and approaches to faster load model
validation and adoption are still needed.
Load Control
Demand response as a tool for providing ancillary services relies on the ability to deliver
fast-acting control of aggregate loads. The timescales over which loads can respond
to dispatch signals and then return to a “ready” state determine the frequency and
magnitude of load response as it performs desired ancillary services. The models for
such control of loads, as opposed to load response behavior, have yet to be developed.
Work to describe the frequency and amplitude response of modern loads and load
controls has only recently been undertaken and significant research remains to be done
in this area [34].
A fast emerging obstacle to effective deployment of large-scale load control systems
is the lack of a comprehensive theory of control for distributed systems. Understanding
how we regulate devices and systems in our environment is a prerequisite to managing
those devices and systems. That understanding is largely captured in classical control
theory, the body of mathematical formalisms that explain how we observe, control
and verify key performance characteristics of linear time-invariant (LTI) systems. The
challenge today is that although controllability and observability are well-defined
for LTI systems through the Kalman rank condition and stability can be studied
using the analytic methods of classical control theory, the emergent behavior of
20
interconnected systems has yet to be fully described formally. As a result, ad hoc
models of robustness, security, and stochastic behavior have been overlayed on existing
control theory. Physical constraints are often ignored, information flow is assumed
instantaneous, and evolving network topologies are not well treated so that only trivial
problems are solved [35].
The paradigm for larger more complex and realistic systems continues to elude
system engineers. We have yet to understand complex engineered systems well enough
to design and control them to the same level of precision we do for smaller self-contained
systems, let alone exploit the new behaviors and possibilities inherent when linking
previously independent systems into a more heterogeneous multi-technical complex of
systems. In short, we need a new approach to controlling the large interconnected
multi-technical complex that is emerging. The new approach must allow systems
to adapt and evolve without individual components being redesigned, retested, and
redeployed every time relevant parameters change. Ultimately a new paradigm of
control is needed for these complex systems.
Validation, Verification and Metering
Using demand response as a resource for planning and operation depends on our
ability to ensure that the tools we use for bulk power system control are accurate.
Demand response programs must work as designed for all foreseeable events and be
robust to unforeseeable conditions. Utilities must be able to monitor the performance
and meter the billable usage of demand resources for both operational and business
objectives.
Model and simulation validation for very complex models such as the load models
currently in use is a daunting challenge in itself. Empirical end-use and load composition data collected by utilities degrades quickly and unpredictably as end-use
21
technologies change, efficiency standards take hold and consumer habits evolve. Although utilities know that consumer assessment surveys are essential to maintaining
accurate load models, the typical cost of conducting these surveys has been prohibitive.
Many utilities and advocates of automated meter reading technology frequently cite
improved consumer behavior data as one of the principal long-term benefits of automatic metering infrastructure. However, these benefits have yet to be documented
and demonstrated in practice, particularly as data privacy and security concerns begin
to emerge [36].
Tool validation presents additional challenges, particularly when tools become
multi-disciplinary and rely on hybrid numerical methods, such as agent-based solvers.
These analysis tools are highly realistic over wide ranges of time scale and can
incorporate a wide variety of model order reductions. However they rarely have a
reference model or baseline data to compare against. As a result confidence in these
tools builds more slowly and the rate of adoption of advanced simulations is slower
than has historically been true from more conventional power system analysis tools
[37].
Control system verification remains an open research area for distributed control
systems such as the large-scale demand response systems being designed and tested
today. Utilities historically relied on strictly hierarchical direct load control programs
that used isolated and simple control structures and were easy to verify. Systems
that rely on autonomous responses or price signals are more likely to exhibit random
deviations that raise concerns regarding their reliability under extreme events when
they may become critical to maintaining system integrity [38].
Monitoring and metering are closely related to the question of verification and
present additional challenges. Utilities must monitor resource availability in real-time
to ensure that sufficient resources are deployed to provide the required contingency
22
response. So-called “transactive” systems have the notable advantage that they
provide resource status and availability data concurrently with the required resource
cost data. Moreover when events occur utilities need to determine which resources
were actually deployed before compensating customers for their participation. To
date most of the advanced demand response systems deployed have largely failed to
satisfactorily address either of these issues [39].
2.1.3
Demand Response Aggregation Strategies
One of the most significant obstacles to using demand response to simultaneously
displace generation-centric reliability services and mitigating generator market power
is the mismatch in the characteristic size, time, and uncertainty of loads relative to
generators: there are relatively few easily observed generators and their characteristic
response times are relatively slow compared to overall system dynamics. Loads in
contrast are far smaller, far more numerous and difficult to observe. But loads
are potentially much faster acting than the overall system dynamics [16]. Demand
aggregation services can be employed in electric power systems operations to enable
energy conservation, peak load reduction and load-based reliability services.
Bulk power system planning, operation and control have generally been designed to
consider the characteristics of generators and treated loads as a “noisy” but forecastable
boundary condition. Thus load control remains quite difficult to incorporate into bulk
system planning and operation. In general the approach to addressing this fundamental
mismatch is to devise demand aggregation strategies that collect numerous small
and fast-acting devices with high individual uncertainty into fewer larger-but-slower
aggregations with reduced uncertainty. Demand aggregation does not require that
every electric customer participate in wholesale markets but it does provide a means of
more cost-effectively increasing consumer participation in system resource allocation
23
strategies, whether market-based or centrally controlled, and can mitigate price
volatility for energy, capacity, or ramping services [40].
From an economic perspective aggregating electricity customers can be viewed as a
means of converting consumer surplus to producer surplus by segregating consumers2
into groups with different willingness to pay. Three general approaches are usually
employed to create load aggregates for either operational or economic objectives:
1. Technical aggregation creates technical structures that either directly aggregate
consumers, or indirectly enable economic or social aggregation using technical
means. Technical aggregation can be accomplished using service aggregators,
creating technological lock-in with high barriers to entry or exit, or constructing
local retail markets independent of the wholesale energy, capacity, and ancillary
service markets.
2. Social aggregation is achieved using various subsidy programs and other social
group identification strategies, such as environmental, green, or early-adopter
programs.
3. Economic aggregation is achieved using price discrimination methods such as
different tariff rates for different customer classes, product differentiation, and
product or service bundling strategies.
Technical customer aggregation strategies are less common in the electric utility
business than might be expected for such a technology-intensive industry. Only a
few types of technical customer aggregation strategies can be readily discerned in
modern utilities operations. Most notable are direct and indirect load control, service
aggregators, retail markets, and technology lock-in strategies.
2
We sometimes must distinguish between customers who pay for the energy from consumers who
use the services that require energy: customers do not always exhibit the demand response behaviors
of consumers and consumers do not always exhibit option/strike decisions of customers.
24
Technical customer aggregation strategies usually support the economic, social
and business objectives of utilities and the government oversight that protects the
public good portions of their operations. Technical customer aggregation is rarely an
objective in itself but for various practical reasons research into technical aggregation
is often divorced from these objectives. Indeed some aggregation technologies are
criticized for not recognizing these considerations and falling far short of expectations
given the costs [41].
Social aggregation is based more on human behavior than economic theory and
is consequently less well understood in general. Utilities typically base their social
customer aggregates on four types of social differentiators: income class, behavioral
cross-subsidies, environmental awareness and early adopters.
Price discrimination is an economic strategy used by sellers to capture additional
consumer surplus. Surplus is the economic benefit derived by bringing buyers and
sellers together to trade electricity products and services. As long as a consumer’s
reservation price exceeds the producers’ they are both overall better off economically if
they complete the trade. The net difference between the consumers’ economic welfare
with electricity and their welfare without electricity is defined as the consumer surplus.
Similarly, the net economic benefit to the electricity producers is the difference in
profit derived from producing electricity and that of not producing electricity and is
defined as the producer surplus. It is the objective of both consumers and producers
to maximize their respective surpluses, which in an efficient market results in the total
surplus being maximized as well [42].
However producers recognize that some consumers have a greater willingness to
pay for products and services. Consequently producers can devise pricing strategies
that divide the consumers in a way that increases their surplus while not increasing
the total surplus. This happens when producers simply capture some of the consumers’
25
surplus. The most common of these strategies is to create different rate structures for
each customer sector, residential, commercial, industry, municipal, and agricultural.
In theory such strategies have been shown to maximize producer surplus only when
the demand curve is strictly convex toward the origin (P, Q) = (0, 0). In practice this
limitation is often ignored and price discrimination is nearly ubiquitous in the electric
utility industry even when there is little or no direct empirical evidence that consumer
demand always has the right characteristics. Even though it may seem unfair to
consumers that some pay less for the same product or service, price discrimination
is regarded as a standard practice justified by the cost recovery needs of a capital
intensive industry and by socio-technical trade-offs/cross-subsidies such as differential
service quality for low-income consumers and industrial customers. Such practices are
widely supported by utility regulators [43].
Volume discounts are another common form of price discrimination that serves to
aggregate consumer behavior. In the case of electric utilities, the most common form
is the declining block rate, which recognizes that customers with a higher demand
also have a more predictable peak demand than smaller customers. The cost of
operating electric power systems is driven in large measure by the cost of serving
unpredictable peaks so more predictable customers are offered discounted rates for
this “good” behavior. In effect these customers are consuming more of a less valuable
product because it does not vary as much relative to the total load, and therefore
costs less to produce and deliver. An unfortunate side effect of declining block rates is
that they can be a disincentive to conservation and many utilities are moving away
from such rate structures. Inclining block rates do promote conservation but this
approach requires very careful analysis to predict the seasonal peak load variations.
When significant numbers of customers come under such a rate, utility revenues can
become much more sensitive to weather fluctuations than they already are [44].
26
Very likely the most well known form of price discrimination employed by utilities
is product differentiation, i.e., charging residential customers for energy usage and commercial/industrial customers for power capacity. This form of customer aggregation
recognizes that residential and small commercial customer behavior, e.g., individual
appliance and equipment purchases, is more closely correlated with energy consumption and large commercial/industrial/agricultural customer behavior, e.g., increasing
production capacity, is more closely correlated to peak power demand. Utilities seek to
have behavior and bills as strongly correlated as possible and therefore prefer energy
rates for residential and small commercial customers and power or demand ratchet
rates for large commercial, industrial and agricultural customers [45].
The final form of economic customer aggregation, service bundling, is the most
ubiquitous in electricity delivery. The strongly regulated nature of the utility business
means that product bundling isn’t thought of as a business strategy to increase
revenues per se as in the telecommunications business. Instead the capital-intensive
nature of the business combined with the desire for simple billing means that energy
or power rates must include capital costs in a simple “blended” fixed energy price.
Service bundling is considered an appropriate net revenue volatility risk mitigation
strategy and regulated as such. Most customers pay for only one product composed
of several underlying services, such as energy with capacity and reliability bundled, or
capacity with energy and reliability bundled. All the underlying services that utilities
provide, such as fuel price volatility hedging, capital financing, administration and
maintenance are blended into the simple price that each customer pays. There is
some discussion of utility business models that unbundle these services to achieve
more economically efficient operations by revealing the customers’ separate demand
elasticities and reservation prices for each service. Utilities would then be able to
serve customers with differentiated reliability services, for example. Most likely the
27
technical and regulatory obstacles to this model are why it has not gained much
more than academic interest. Perhaps we can expect growing interest in areas where
distribution reliability is a significant issue for some customers or technical solutions
like microgrids are prevalent. But that has yet to be adequately researched at this
point.
Although many of these aggregation methods have existed for decades, recent
technological advances have enabled some of them to be revisited and enhanced. In
particular early adopter strategies offer utilities the opportunity to test new technologies to meet regulated research program investment obligations and avoid the
risk of significant capital investments. Meanwhile operators and customers have to
opportunity to learn how to maximize the benefits of new programs before utilities
commit to and regulators approve of full-scale deployment.
Price-based strategies provide a balance of economic efficiency and risk mitigation
by allowing utilities to transfer some costs more explicitly to customers and reducing
the need to engage in more costly price-volatility hedging on their behalf through
opaque rate design processes. But regulators remain wary of price-based aggregation
strategies until they can be shown to be cost-effective and fair to all customers.
2.1.4
Environmental Impacts of Demand Response
In the previous sections the role of ancillary services, the potential for demand response
to provide such services and the strategies available to aggregate demand response
services were discussed in detail. We found that 1) ancillary services provide a critical
capability for interconnection reliability; 2) demand response has the potential to
provide such services; and 3) demand response resource aggregation is necessary to
integrate diverse technical capabilities into interconnection planning and operations.
In this section we consider the environmental impacts of increased demand response
28
resources in electric systems.
A comprehensive study of smart-grid technology completed for the US Department
of Energy in 2010 found that a potential for 12% direct and 6% indirect reduction
of electricity sector energy and CO2 emissions [46]. These included conservation
impacts of consumer information feedback system (3% direct impact), deployment
of diagnostics in residential and commercial building (3% direct impact), support
for additional electric vehicles (3% direct impact) and advanced distribution voltage
control (2% direct impact). But the most significant impact was a 5% indirect impact
from the support of renewable wind and solar generation.
Variable or intermittent generation is a growing fraction of the resource base for
bulk power systems. The variable character of certain renewable resources in particular
is thought to undermine the overall reliability of the system insofar as forecasts of wind
and solar generation output have greater uncertainty than more conventional fossil,
nuclear or hydroelectric generation resources. As a result the expectation is that while
variable renewable generation resources do displace the energy production capacity of
fossil power plants, they may “consume” a significant fraction of the reserve power and
ramping capacity of the plants they are supposed to replace. Consequently renewable
resources do not offer as much emissions benefit as expected if one were to assess their
impact simply on energy production capacity [47] [48].
It seems intuitive that demand response should be able to mitigate the reserve
capacity and ramping impacts of variable generation by reducing the need to build and
commit fossil generation to substitute for reserves or ramping required by intermittent
renewable generation. But this substitutability is constrained by 1) the nature of
variable generation, the role of forecasting, and the impact of resource variability on
the emissions and economics of conventional resources; 2) the nature of load variability
and how demand response is related to load variability; and 3) the characteristics of
29
end-use demand and the impact of demand response on energy consumption, peak
power and ramping rates over the various time horizons that are relevant to the
variable generation question.
Taken together these constraints and interactions provide the basis for assessing
the economic and environmental impacts of controllable load and demand response
resources on various timescales. The economic principle of downward substitutability,
i.e., faster ramping ancillary services are more valuable, provides the basis for our
assumption that fast-acting demand response resources have greater value than slower
generation response resources, all other things being equal. In this case the economic
cost of mitigating renewable intermittency at any given timescale using generation
resources must be greater than the cost of using demand response with the capability.
We may then conclude that when environmental costs are internalized the environmental benefit mitigating renewable intermittency using demand response must be
greater than if we used generation resources.
Generation Variability
On the supply side of the reliability equation we find that variability in renewable
resources is the most significant contributor to uncertainty in the overall generation
production scheduling process. Current renewable generation forecasting tools are
based on five technologies—numerical weather prediction, ensemble forecasts, physical
models, empirical modeling and benchmarking—that are combined in a 3-step process
to produce a forecast. These steps are 1) determine weather conditions, 2) calculate
power output, and 3) scale over different time horizons and regional conditions [49].
In general, the root-mean square errors (RMSE) of renewable forecasting methods
grow asymptotically as the time horizon is extended with the best models having an
RMSE of less than 5% for 1 hour forecasts to over 35% for 3-days forecasts. There is
30
Table 2.1: Emission reductions relative to no wind generation
Emissions reduction
Wind penetration CO2 N2O CH4 CO NOx
10%
12%
9% 12% 10%
13%
20%
21% 11% 17% 15%
22%
30%
28% 10% 21% 19%
29%
40%
33%
4% 23% 20%
34%
Source: Valentino et al. (2012)
SOx PM
8% 11%
17% 22%
24% 32%
30% 40%
high variability in the reported performance of different forecasting tools. Because
generation resources are dispatched based on these forecasts, the principal component
of unscheduled generation deviations is the error in the forecasts of renewable resources
[50].
Variable resources do help reduce the need to operate fossil-based power plants
and thus reduce emissions to a first order. But this benefit is not on a one-to-one
basis because the need to continually adjust fossil plant output can cause second-order
increases in emissions due to decreased plant efficiency. For every 3 MW of wind
capacity added, only 2 MW of fossil capacity is decommitted. Additional startups
reduce the emissions benefits of wind by 2%. Part-load operation reduces the emissions
benefits by an additional 0.3% in WECC [51]. In addition at high variable generation
levels, some energy may need to be spilled because there are no consumers for it under
light load conditions. The effective emissions rate for wind due to these secondary
effects relative to a typical interconnection fossil generation mix is about 1-2%/MWh
[52].
The overall emissions reductions for wind generation are shown in Table 2.1. Based
on the variable resource impacts inequality assumption, we should assume that demand
response benefits could not exceed these values.
There are a number of considerations that limit the equivalence between variable
generation impacts and controllable load benefits. In particular, the geographic disper-
31
sal of variable generation supports diversity, which is a key assumption in estimating
their collective reliability impacts. For demand response such assumptions may not
hold. In addition, certain regulatory practices such as defining gate closures (the lead
time required to procure reserves) may differentially affect how well improvements in
forecasting of variable generation reduce reliability impacts relative to changes in load
forecasting as more load becomes responsive.
Load Variability
Time-series load data is the foundation of all load analysis. The most commonly
available data on load is metered balancing area, substation, feeder, premises, and
end-use load data, in decreasing order of availability. Utilities have measured balancing
area to feeder-level load using SCADA systems for decades and this provides a very
clear picture of the aggregated behavior of load. Most obvious in this data are the
weekday, weather and diurnal sensitivities of load, which are the basis of system-level
load forecasting tools [34].
Until recently, premises load data was only measured monthly and depending
on the rate paid by the customer it might be only energy use (e.g., called interval
metering) or peak power (e.g., ratchet demand rates). However the advent of advanced
metering technology has offered the possibility for significantly more detailed subhourly premises load data that allows analysts to examine many shorter term behaviors
such as device and equipment cycling at the sub-hourly horizon. Although end-use
metering is still very limited, it does provide additional insights that contribute
important sub-hourly information to the study of load variability [53].
Recent work has identified a distinctive spectral signature for power from wind
turbines [54]. The technique was successfully applied to sizing storage for variable
generation mitigation [55], reducing variable generation forecast uncertainty [56], and
32
studying load control for variable generation mitigation [34]. It particular, there
appears to be an opportunity to use variability spectra to create a library of end-use
load signatures that will enable the study of both load and generation variability and
support the design of demand response control programs that are better suited to
mitigating variable generation. This area appears to be a potentially fruitful topic for
research with numerous opportunities, including
• End-use signature development for load decomposition;
• Model identification for both duty-cycle phase and amplitude of sub-hourly load
behavior;
• Identification of human-driven behavior and demand response sensitivities; and
• Identification of non-cyclic load variability phases and amplitudes for diurnal
and seasonal behavior.
The response sensitivities based on spectral variability functions in particular
appear to simplify the evaluation and analysis of variability generation and demand
response impact questions. For example, the computation of the overall emissions or
cost impact of a load shift of t hours can be estimated by the convolution
Z
∞
c(τ )l(t − τ )dτ = (c ∗ l)(t)
v(t) =
(2.1)
−∞
where c(t) is the cost or emissions at the time t and l(t) is the load. While in time
domain this can be difficult to compute, in frequency domain it is comparatively
simpler if the data is available:
V (f ) = C(f ) · L(f )
(2.2)
33
where V (f ), C(f ), and L(f ) and the Fourier transforms of v(t), c(t), and l(t) respectively. Given a library of both generation variability and load control signatures in
frequency domain, the optimal demand response design problem might be significantly
more tractable than previously thought.
Demand Response Characteristics
Loads exhibit a peculiar characteristics that is often not considered in benefits analysis
but is highly relevant to the analysis of load control. The relationship between energy,
load, and ramping is actually quite robust. Most demand response programs can
exclusively affect either power demand in the short term or energy consumption in
the long term. In every other respect energy, power, and ramping are strictly related
to each other as
d
Energy(t) = Load(t) =
dt
Z
Ramp(t)dt
(2.3)
and this relationship is not affected by conventional demand response control strategies.
For example a DSM program may reduce energy consumption of the long term but
the power and ramping impact are strictly a function of how the demand response
program affects energy use. Similarly an air-conditioning load curtailment program to
cut peak may reduce power during peak hours but the natural tendency of thermostatic devices to make up for short-term deficits over the long run means that long
term energy use may be relatively unchanged. The characteristic time of a demand
response control strategy and how the systems it controls respond are essential to
understanding how well demand response will mitigate variable generation resources
and the degree to which the demand response impact inequality will apply. From
a controls perspective we recognize that full observability is achieved by measuring
energy, while full controllability can only be achieved by actuating ramping.
The argument can be made that resources with greater ramping capabilities should
34
be considered higher quality reserve resources. In ancillary services markets this
characteristic places a premium on faster resources with downward substitutability.
For this reason demand response resources that control the power of loads are at
least as valuable as generation resources with the same net power response and often
more valuable because of their greater ramping response and stronger downward
substitutability. In fact it seems the principal and perhaps the only limiting factor
on the ramping rate of demand response resources is the telecommunications latency
of the control signals. The real-time market in the Olympic Peninsula had a typical
delay of about one or two seconds in response to the market clearing event, but the
market itself cycled only once every five minutes [5].
Summary of Impacts
The impacts of generation variability hence the benefit of load controllability may be
summarized as follows:
• Long term load forecasts have lower relative RMSE than long term variable
generation forecasts. Thus load can be expected to outperform the generation it
mitigates, all other things being equal.
• Load control can be scheduled with greater reliability than variable generation
and thus can be expected to outperform the generation it mitigates, all other
things being equal.
• The loss-of-load probability impacts of variable generation are mitigated by
load control in part by moving all controllable load out of the load impacted by
outages.
• The capacity credit for controllable load can be expected to be comparable to
the capacity credit for variable generation, if not better because for every 1 MW
35
of load that is controllable, 1 MW of generation reserve can be decommitted.
• The standby capacity reduction associated with controllable load should in
principle be 100% of the responsive load under control.
• When controllable load is dispatched under liberalized markets, consumers
become the providers of resources. This tends to divert revenue from generators
to savings by consumers. Based on the cost of variability on the supply side,
this can be expected to be about 10-20% of the direct cost of electricity and
mitigates the need to provide 5-10% additional installed capacity [57].
• The secondary emissions benefits for avoiding startup and part-load fossil generation are expected to be 10-20% for modest levels of variable generation (i.e.,
< 20%) but may be significantly lower for some bulk systems, depending on
conditions.
• The geographic sensitivity of load is different and very likely less than it is for
variable generation. Loads tend to be more uniform and better diversified than
variable generation.
2.2
Responsive Heating/Cooling Systems
This section discusses the characteristics and operation of heating cooling and ventilation loads in residential buildings. These systems are designed to perform several
key functions in a home. The most important is to provide a steady and comfortable
indoor air temperature. Modern forced-air systems also filter air, sometimes provide
replacement fresh air, and if necessary a comfortable level of humidity. There are
many different types of equipment that provide heating and cooling using forced air
systems. Heating strategies vary more than cooling and can include in-floor radiant
36
heating, baseboard heaters, and radiators. In all cases there is a source of or sink for
heat and a way to transfer heat throughout the home.
In parts of North America where humidity levels can be extreme, i.e., over 90% or
less than 20%, it is common for systems to include humidification or dehumidification
equipment. This equipment can be zoned but typically humidification is done at the
whole house level.
Homes less than 2500 ft2 (230 m2 ) usually only use a single zone served by a single
heating or cooling heating/air-conditioning unit. Larger homes, especially those over
4000 ft2 (370 m2 ), typically have multiple zones, e.g., upstairs and downstairs. Each
zone can be served by a separate unit, or two or more zones might share a single
heating or cooling unit. Often the equipment installed varies for each zone and can
include dampers, mixing boxes, and other devices to individually heat, cool, ventilate
and (de)humidify the zones independently.
Residential heating and cooling systems are single speed gas or electric heating
units with electric air-conditioning in about 60% of homes. In climates where the
heating conditions are less severe heat pumps are preferred and compose about 30%
of the market. About 10% of residential systems also support two speeds.
Forced air systems are the preferred method of conditioning homes because of
the cost and efficiency advantages they present. These have been installed in homes
for decades and have been the standard for homes since the early 1950s. Forced
air systems can provide four major services in a single package: heating, cooling,
ventilation/circulation/filtration, and humidity control. The most common are heating
and cooling, although it is not uncommon to find cooling only or heating only in
climates where this is possible. Circulation is a natural byproduct of forced air systems
and is particularly useful for destratification to avoid hot or cold areas in homes.
Ventilation is not very common except in homes that are very well sealed against
37
Figure 2.2: Basic heating/cooling system (Source: www.progreencompany.com)
leakage air. Filtration is becoming more common as homeowners become cognizant of
the adverse health effects of dust and pollen. Humidification and dehumification is an
option that is only used in cases of extreme humidity. The basic system operation
shown in Figure 2.2 has not changed since it was first introduced. But the system
complexity and efficiency have increasingly significantly over the years.
2.2.1
Control Automation
Automation has played an important role in the evolution of forced-air systems by
providing better strategies for deciding when to turn various system components on
and off. The thermostat is a key component of the automated control of heating and
cooling systems and provides home occupants with control over the trade-off between
38
Figure 2.3: Classic Honeywell “round” thermostat
comfort and energy cost.
The earliest thermostat models had one temperature setting. If needed a mode
switch was provided to specify whether the thermostat was to control heating or
cooling based on this one set point. The classical Honeywell “round” thermostat
shown in Figure 2.3 is one of the most commonly installed of these.
Over the years many features were added to thermostats to increase the convenience
and comfort of occupants and improve the overall efficiency of heating and cooling
operations. Today’s thermostat often include the following features.
Dual set points Two separate set points are supported, one for heating and one for
cooling. These set points must be separated so that simultaneous heating and
cooling cannot occur. Typically the minimum separation is twice the deadband
or about 2◦ F (about 1.1◦ C).
Auto mode In this mode, the thermostat automatically chooses the heating or
cooling mode based on the temperature set-points. In some thermostats, the fan
runs at a lower speed in Auto mode when neither cooling nor heating is active.
Heat/Cool-only mode The dual set points make it easier to determine when heating
mode and cooling modes are required, as with Auto mode. However occupants
39
may want to prevent heating or cooling from operating. Selecting heat-only
or cool-only mode prevents the excluded mode from operating. As with Auto
mode, the fan may run at a lower speed when the system is not active.
Fan mode This mode disables both heating and cooling but keeps the fan running,
possibly at a reduced speed.
Supplemental heat This mode engages the second stage heating system and raises
the fan speed to maximum and it useful when the primary heating system
has failed. This is sometimes referred to “emergency” heating, as opposed to
“auxiliary” heating, which is automatically engaged when the primary heating
system’s capacity is insufficient for conditions such as when the outdoor air
temperature is too low for the heat-pump’s capacity.
Occupancy schedules Occupants can assign different set-points and fan mode for
specific hours of the day and days of the week. At a minimum two occupancies
are supported, such as home and away. Some thermostats have additional
occupancies such as sleep and vacation, or customizable occupancies.
Overrides Sometimes the occupant wishes to directly set the temperature or mode
temporarily. Overrides are usually set until the next scheduled occupancy begins
or 3 hours if no occupancy schedules are defined.
Anticipators Some homes experience overshoot problems because of lags in the
response. Older thermostat provided a heating feed-forward signal by running
the current to the relay through a small coil placed near the thermostat coil but
this strategy didn’t work for cooling. Modern thermostats sometimes include
simple model learning components that compensate for lag in the thermal
response and turn components on and off earlier, if necessary.
40
Some recently added features that are found today now include
Communications Communicating thermostats usually support wireless local area
networks (e.g., 802.11 family of protocols). While the wireless protocols are
standardized, the software protocols are not as standardized. Some better-known
protocols are Honeywell, Nest, Zigbie, and OpenADR (1 and 2).
Home automation One of the key lessons from early advanced thermostat marketing
was that people didn’t really use them or understand them. Manufacturers like
Nest seek to overcome this problem by making thermostats that detect and
learn occupancy patterns instead of relying on consumer inputs. An alternative
approach is to integrate the thermostat into a comprehensive home energy
management system that is linked to the utility bill, taking advantage of the
fact that homeowners are far more likely to make “good” energy management
decision when they pay the bill.
2.2.2
Hierarchical Control
Participation of thermostatically controlled loads in demand response has to be
considered in the context of existing and emerging electricity market mechanisms.
The electric power industry has undergone a fundamental restructuring over the past
30 years, transforming from regulated to a market oriented system. Restructuring has
entailed unbundling of vertically integrated organizations into independently managed
generation, transmission and distribution systems. As a result electric power markets
have been divided into wholesale and retail systems that interact according to a
well-defined, albeit ad hoc design.
Most wholesale electric power markets are based on the design proposed by the
U.S. Federal Energy Regulatory Commission (FERC) in its April 2003 white paper
41
[58] encompasses the following core features: 1) central oversight by an independent
system operator (ISO); and 2) a two-settlement system consisting of a day-ahead
market supported by a parallel real-time market to ensure continual balancing of
supply and demand for power. The objective of an ISO/RTO is to ensure that supply
equals demand at every instant, while maintaining system security and reliability
and minimizing the total cost of serving the load. Optimization is performed on
multiple time-scales. The day-ahead settlement system is a pure financial market for
generators and load serving entities to create financially binding operating schedules.
The real-time energy market allows for the physical exchange of power and addresses
deviations between actual real-time conditions and contracted day-ahead agreements.
The ISO solves security constrained unit commitment (SCUC) and economic dispatch
(SCED) problems in both day-ahead and real-time markets to determine cleared
supply and demand, and corresponding locational marginal prices (LMPs), which
are reported to market participants. The ISO runs a balancing reserve market in
parallel with the energy markets to calculate the cleared reserve capacities and the
corresponding reservation prices needed to sustain operational balance at any every
time interval.
Retail markets have not gone through such a restructuring process. Hence there is
limited participation by distributed assets in wholesale markets through aggregation
and there is no direct participation by smaller assets at all. However this can be
expected to change with accelerated deployment of new smart grid infrastructure such
as digital meters and advanced distribution control systems under the Smart Grid
Investment Grants. Additionally, FERC Order 755 now requires grid connected shortterm storage devices to be treated equitably as conventional generation units when
providing regulation services. Similarly, FERC Order 745 required energy payment of
demand response resources at nodal LMPs. But its status is somewhat uncertain in
42
the wake of the US Court of Appeals decision limiting FERC authority to regulate
demand response insofar as it impacts wholesale markets.
As a result a number of wholesale markets now allow distributed assets limited participation in energy markets. Usually these assets are used to meet peak load reduction
or emergency services by drawing from large-scale demand response programs that
serve commercial and industrial users. Feeder level resources still do not participate
in wholesale markets, except when provided by demand response aggregators or in a
limited number of pilot demonstrations projects. To realize the vision of an integrated
demand response system at the wholesale level we must consider changes to wholesale
market designs, deployment of a full-fledged system of retail markets, and linking
those with wholesale markets in way that provides suitable incentives for participation
by distributed assets.
There are two key elements to any proposed infrastructure that will facilitate robust
and reliable electric power operations. The first is inter-scale infrastructure that allows
devices at various topological levels to cooperate in determining the efficient allocation
of the available resources. The second is the inter-temporal infrastructure that allows
devices to distribute over subintervals of time the allocations they have received within
a given time horizons. In its embodiment in DOE-funded pilot demonstration projects,
transactive control addressed primarily integration over the structural hierarchy and
left the problems of the temporal hierarchy to future research.
The transactive control infrastructure addresses this resource allocation and dispatch problem and is used to reconcile supply resource constraints with demand
requirement, e.g., feeder constraints versus consumer comfort settings at the retail
level. This is accomplished by using real-time prices as demonstrated in the Olympic
Peninsula GridWise Demonstration project [5], and is also employed by American
Electric Power in the Northeast Columbus gridSMART demonstration project [6].
43
These systems established retail markets that discovered the short-term price at which
supply equals demand at each feeder in the distribution system given the current
day-ahead prices and prevailing supply and demand conditions on the feeder and in
the homes equipped with price-responsive devices. The Pacific Northwest Smart Grid
Demonstration Project (PNWSG) uses a variant of this design for resource allocation
that relies on mid-term forecasting usage instead of committing to short-term usage.
The system also substitutes an index for a price to avoid the issues associated with
trying to link with wholesale markets and use LPMs in regions that have neither at
present. The PNWSG project also differs from AEP gridSMART project in the way
the formulated signal is presented to the devices.
2.2.3
Transactive Control
The mismatch in the characteristic size, time, uncertainty of loads relative to generators
of loads is a significant obstacle to using demand response to simultaneously displace
generation-centric reliability services and mitigating generator market power: there
are relatively few easily observed generators and their characteristic response times
are relatively slow compared to overall system dynamics. Loads in contrast are far
smaller, far more numerous, and for more difficult to observe but potentially far faster
acting than the overall system dynamics.
The Olympic and Columbus demonstration were successful in achieving their
primary objectives, i.e., they used transactive control to show 1) that thermostatic
demand resources could contribute to short term capacity control using economic
signals, and 2) that financial benefits would accrue to both utilities who installed and
consumers who participated in such a control system.
Households recruited to participate in the Olympic and Columbus transactive
control systems under the RTP tariff were equipped with home automation devices
44
including a smart thermostat and a home-energy management system to integrate
thermostats and other energy demand controllers with the utility metering system.
The utility was equipped with a market-based dispatch system and communications
links were established between the various components of the system. For both the
Olympic and Columbus experiments an operations plan was developed to test the
system and observe the response to price fluctuations resulting from wholesale price
variations, distribution congestion and critical peak pricing (CPP) events.
Various scenarios were designed to elicit demand response such that one could
estimate the technical and economic properties of the transactive system. Various
utility value streams such as peak-load capacity deferment, reduced wholesale power
purchase costs and revenues from operating reserves markets were estimated. Consumer
impacts such as benefit, surplus, comfort and billing impacts could then be recovered.
The operating scenarios generally involved continually exposing customers to small
fluctuations in price as well as changing feeder congestion limits at various times to
induce large price changes. The Olympic experiments were conducted from May 2006
to March 2007 in Clallam County and Port Angeles, Washington. The Columbus
experiments were conducted from June through September 2013 in the northeastern
area of Columbus, Ohio. Various combinations of feeder congestion limits and durations
were tested. These were selected at various time of day, day of week, and weather.
Additional critical-peak-pricing (CPP) responses were tested using selected CPP
events.
Households who were recruited to participate voluntarily to the Olympic Peninsula
experiment were offered the choice of two new tariffs: a time-of-use (TOU) price or
real-time price (RTP). All customers received the same in-home equipment, including a
smart thermostat and home-energy management wireless hub to establish connectivity
to the utility’s demand response dispatch system and provide 15-minute interval
45
energy use metering. Some homes also received controllers for electric waterheaters
and electric clothes dryers. Customers were then randomly assigned to the control
group, a fixed price tariff, TOU or RTP. Regardless of the assignment, customers were
promised on average $150 benefit for participating 1 year. But they were told that the
exact amount was uncertain and would be based on the tariff and how “well” they
played the demand response “game”. Customers were given an income based on their
energy consumption prior to the announcement of the program to which an additional
$37.50 incentive was added quarterly. The monthly energy bills under the experiment
tariff were then deducted from that income. Any positive balance remaining at the
end of each quarter was paid to them. During the experiment, customers continued to
pay their normal bill to the utility and if customers overspent their quarterly income,
they were not required to pay it back or carry the deficit into the following quarter.
Columbus customers were recruited from a pool of homes that already had smart
meters installed. The smart meters provided 5 minute interval energy use data both
to the utility’s metering system and to the home energy management system, which
was installed to maintain connectivity with the utility’s demand response dispatch
system. Customers were placed on an experimental RTP tariff approved by the Public
Utility Commission of Ohio. Power was billed to consumers based on a commissionedapproved seasonal linear function of the wholesale LMP, plus feeder congestion costs,
less a congestion rebate or a demand responsive incentive payment. All other taxes
and fees remained unchanged.
In both projects, the demand curve was constructed from the bids received from
the responsive equipment in households on the RTP tariff, as shown in Figure 2.4.
Unresponsive load corresponds to all the other load on the feeder, including unresponsive equipment under RTP tariff, all other customers on non-RTP tariffs, services and
losses. Bids were computed by the thermostats based on measurements of the indoor
46
Figure 2.4: Capacity market clearing (left) and thermostat bid/set for cooling conditions (right) mechanisms
air temperature such that
PB =
kPD
(TA − TD ) + PA
TM − TD
(2.4)
where PA is the long-term average price over the past 24 hours, PB is the bid price, PD
is the long term price standard deviation, k is the customer’s comfort control setting,
TA is the measured indoor air temperature, TD is the customer’s desired indoor air
temperature, and TM is the maximum cooling TH or minimum heating TL indoor air
temperature allowed. The quantity K =
kPD
TM −TD
is referred to as the demand response
control gain or comfort gain in $/◦ F.
The supply curve was constructed from bids received by the various resources
available, although in the case of the Columbus demonstration there was only the feeder
supply. In the Olympic demonstration, supply included distributed generation with
“hot” capacity representing must-run units that are already running and presumably
can’t or won’t stop and any units that have zero marginal production cost, such as
47
photovoltaic units. So called “cold” units are those that have start-up costs included
in the marginal cost and therefore are held off until the demand is sufficiently high to
justify starting them.
In all existing embodiments of the transactive control system the clearing price
and quantity are found at the intersection of the supply and demand curves. The
clearing price is then used to change the thermostat set point such that
TC = TD + K −1 (PC − PA )
(2.5)
where PC is the cleared price, and TC is the load control set point used until the next
market clearing.
The total surplus is the left-side area between the supply and demand curves. The
consumer surplus accrues to consumers not willing to forgo consumption at the cleared
price. The producer surplus only accrues to those producers whose costs are below the
cleared price. When the price clears above the feeder supply price, the utility collects
a producer surplus from the feeder congestion. In the Columbus demonstration a
congestion rebate returned the entire feeder surplus directly to the consumers while the
incentive rebate compensated consumers who were curtailed as a result of congestion
by diverting some of the utility’s feeder surplus to pay the consumer’s share of the
deadweight loss caused by the withheld capacity.
Some criticize this congestion rebate as self-defeating in the long run. But it was
deemed necessary as a compromise that would satisfy regulators and utility managers
who were concerned about whether the tariff would be revenue neutral and unfair to
the participating customers. In principle producer surplus from congestion on feeders
is used to finance capacity expansion. However congestion charges only occur for those
customers who reside in congested neighborhoods during congested periods. Utilities
also laterally switch homes from one feeder to another to manage feeder loading so it
48
may not be possible for a customer to “choose” where to live to avoid such charges.
This introduces a potential issue of fairness in the sense that customers who sign up
for the tariff may perceive they are paying a greater share of capacity expansion costs
through scarcity rents than other customers. The congestion rebate was introduced
to avoid charging only customers on chronically congested feeders for the cost of
expanding capacity, which is an asset growth cost that is normally redistributed using
blended tariffs. Customers who provide highly responsive resources are additionally
compensated for curtailing under congestion through the incentive payment.
Although this compensation strategy does not seem likely to provide the desired
long-term incentives to customers, it was hoped to have the desired effect in that it
makes the bills “feel” more like a fixed price tariff in the long term while preserving
the desired short-term incentives through savings opportunities not available to other
customers. While this is consistent with the spirit of FERC Order 745, it is also quite
evident that this is effectively a form of double compensation as critics of the order
point out. In addition it would seem also to not be incentive compatible because a
rational consumer would indicate a willingness to forgo that is higher than the true
demand and one would observe a corresponding decrease in demand by the anticipated
congestion fee. The incentives would seem to be wrong both in the short and the long
term.
Long-term consumer preferences played an important role in determining the shortterm outcomes of the demand response system. Newly-installed household equipment
was configured with neutral defaults and customers were instructed how to enter their
preferences. These preferences were an expression of the consumer’s willingness to
forgo comfort in the very short-term for the benefit of a decrease in cost. Preferences
could be set to a variety of values by participants depending on time of day and day of
week. The preference setting resulted in a discrete choice to consume or not consume
49
at a given price and thus formed the basis of both the bid price selection based on the
prevailing conditions, i.e., higher bid prices for more comfortable conditions and lower
bid prices for less comfortable conditions in the home. The aggregate effect of these
comfort settings give rise to a logit-shaped demand curve that changes every 5 minutes
as the states of the heating and cooling systems change in response to fluctuations in
the price and other endogenous behavior in the home [6].
Detailed simulations of load control using thermostats revealed some potentially
significant technical problems with the first embodiment of the transactive control
system used in these demonstrations. Among these was demand response dispatch
control drift. When the markets cleared the measured load was initially very close
to the cleared load. However, during the five minutes that followed, before the next
market clearing, the total load drifted away from the cleared load. This suggests
that the 5-minute market implemented did not work well as a load dispatch “control”
system. The prevailing hypothesis is that the drift is the result of changes in the
diversity of thermostat states induced by a common exogenous signal. These changes
in the state diversity of the loads were caused by the aggregate load’s initial response
to the change in price [59]. Because diversity always increases in the absence of an
external forcing signal, the aggregate load tends toward the equilibrium load given
the initial price signal and the prevailing conditions at the time the load is being
observed. Under peak load conditions, this drift can be very significant, as illustrated
in Figure 2.5, and can only be mitigated by a) minimizing the degree to which diversity
is changed by the control signal, or b) preventing the devices from changing state
during the 5 minute interval between price clearings. Because option (a) would defeat
the purpose of the load control system, it would seem if diversity changes are the
cause of the problem then option (b) is the only mitigation strategy available.
50
Figure 2.5: Example of drift in demand response using transactive control (Data
courtesy Jason Fuller, Pacific Northwest National Laboratory)
2.3
Conclusions
The trend toward a more integrated and interconnected complex energy system is
inexorable. Progress on the 21st Century’s infrastructure of complex interlocking
network of technical, social and economic energy systems is challenging our current
understanding of these systems and our ability to design and control them. Significant
challenges and research opportunities remain in load modeling and simulation, understanding the impact of consumer behavior on demand response, the fundamental
theory for controlling widely dispersed demand response resources, and the verification,
validation, monitoring and metering of demand response systems in utility operations.
Overall, it is clear that we are entering a period of increased electric utility
receptiveness and growing innovation in the methods and strategies for turning a
largely passive customer base into an active part of electric system operation. Technical
innovation based on sound economic and social objectives as well as robust engineering
design will be instrumental in bringing about this transformation.
The impact of controllable load on system operation can be deduced from studies on
the impact of variable generation. The studies to date suggest that variable generation
51
has both costs and benefits, and that the benefits outweigh the costs for reasonable
mixes of variable generation relative to conventional resources. Many of the adverse
impacts of variable generation are positive impacts for controllable load in the sense
that the magnitude of the cost or impact as a function of generator variability is a
cap on the magnitude of the benefit of load as a function of load controllability.
Controllable load exhibits the further advantage of high downward substitutability.
Demand resources can be significantly favored under liberalized ancillary service
markets. This feature of controllable load suggests that well-designed ancillary service
markets along with market-based load control strategies could be a very powerful
combination, provided the technical means of reliably controlling and aggregating load
resources are available and employed.
The transactive control concept provides a market-like mechanism to aggregate and
coordinate control of all the necessary resources, both supply and demand, at every
level from transmission to end-use devices. This applies also to the various resource
capabilities, energy, capacity, and ramping, at the necessary time-horizons from daysahead to real-time. The comprehensive nature of the structure should alleviate the
concerns of present day system planners and operators regarding controllability of
distributed smart grid assets, allowing them to be fully incorporated into system
operations to achieve multiple objectives:
• Higher utilization of generation, transmission, and distribution assets, by changing load behavior on peak;
• Lower wholesale market costs and power production costs, especially during
high price periods;
• Lower ancillary service costs by engaging distributed assets to supply them;
• Lower cost for integrating new solar and wind generation them into system
52
operations by mitigating their variability and uncertainty; and
• Higher environmental benefits from more efficient asset utilization and the
potential to easily internalize environmental costs.
• Increased reliability at both the bulk grid and distribution levels, from coordinated engagement of distributed assets across multiple operating entities by a)
providing increased available reserve margins, b) incorporating them into bulk
grid wide-area control schemes and c) integrating them with distribution level
voltage control and reconfiguration schemes.
The transactive control concept increases the penetration of demand response and
other distributed assets resulting from their significantly enhanced economic viability
by allowing them to provide a complete set of services on par with traditional large-scale
transmission-level resources. This control mechanism also helps sustain utility revenue
requirements, stabilizes utility customer costs at low rates made possible by lower cost
distributed assets that displace the need for additional conventional infrastructure.
Thus the vision of enabling overall cost effectiveness and environmentally sound grid
infrastructure is realized, while minimizing the information content of data transferred
enhances overall cyber-security and customer privacy.
But in the short term transactive systems will be limited by the ability of the
individual devices to respond quickly and correctly in a coordinated manner to signals
emanating from the bulk system within the constraints imposed by the users of those
devices [60]. By appropriate design of these devices we can enhance the speed and
tracking of bulk control of demand response. The remainder of this thesis examines
the design of devices that are linear time-invariant in the aggregate and therefore
facilitate integration with other bulk system controls.
53
Chapter 3
The New Transactive Thermostat
In this chapter we use models to derive the requirements and specifications of individual
thermostatic load controllers that can be described using linear time-invariant (LTI)
models from a single input signal, e.g. a price, which is sent to all such loads
periodically, e.g., once every 5 minutes.
The non-linearity of the aggregate load control model arises from the Schmitttrigger behavior caused by the deadband or differential gap control in the household
heating/cooling system thermostat. As an alternative to studying the non-linear
behavior of aggregate load control systems, two possible approaches can be considered
to create a load control system that is modeled as a linear time-invariant aggregate
system. The first is to employ proportional feedback, e.g., by varying the fan-speed in
place of differential gap control. This approach is very simple and lends itself well to
treatment using classical control theory for continuous-time systems. But it has the
disadvantage of being less efficient under part-load conditions because the reduced
air-flow changes the temperature gradient through the indoor heat-exchanger.
The second approach is to use a discrete sampling interval and apply zero-deadband
control only periodically, such as every 5 minutes when a new price signal is received.
54
The second approach is appealing because in every other respect the system performs
the same as with a conventional non-zero deadband thermostat. Key elements of the
system model become discrete but the system overall is nonetheless modeled as a
linear time invariant system.
In this design we use a price PC as the utility control signal and ts = 5 minutes as
the sampling interval, but it is understood that the solution is fundamentally the same
for any single load control signal and sampling interval. To meet this design objective
we propose a new thermostatic control architecture for residential buildings. We
develop the building thermal model, the system model and various control elements
for individual home heating/cooling loads. We show that houses equipped with the
new thermostat are hybrid discrete-time control, continuous-time response linear
time-invariant (LTI) systems that gives rise to an aggregate load control system that
is a discrete-time system with a linear time-invariant response for time intervals less
than 5 minutes.
3.1
Building System Model
The typical single-zone building system is illustrated in Figure 3.1. The building
occupant sets a desired indoor air temperature set point TD which is compared to air
temperature TA , given the indoor temperature control error E. When the temperature
is too low or too high, the heating or cooling mode M , respectively, is selected by the
thermostat contoller. The heat QH is added to or removed from the air in addition
to other sources/sink of heat to/from the air, including the internal gains QI from
appliances and occupants, the solar gains QS , and the envelope gain/loss UA TO . The
building’s air and mass respond to the heat QA added to the air, which we model
using a second-order response model described below, and the air temperature TA is
55
Figure 3.1: Conventional building air temperature control system
observed to close the feedback loop.
When a conventional thermostat is retrofitted for demand response two methods
are generally used. The first is to simply interrupt the signal M and override the
control of the heating/cooling system with a mode control signal MC sent directly
from the utility, as shown in Figure 3.2 (left). This direct load control method is
widely used for emergency “one-shot” load relief programs and has the advantage
of providing a relatively predictable amount of load relief for a population of loads
based on the overall duty cycle. The disadvantage is that customers do not like giving
up control of their comfort. Sometimes customers sign up just for the rebates and
unsubscribe the first time the program is called, creating a potentially significant
“free-loader” phenomenon. In addition, utilities do not like the load rebound that
occurs when the load control program ends and hesitate to use direct load control for
peak load management because the new peak loads can exceed the original peak that
was to be mitigated.
A more consumer-friendly approach is to send a temperature offset signal TC
that raises or lowers the indoor temperature set point TD to provide load relief for
the time it takes for the house to reach the shifted set point’s deadband, as shown
in Figure 3.2 (center). This provides consumers with a limited degree of control
insofar as the temperature will not go completely out of a given comfort band. It
56
Figure 3.2: Direct load control by interruption (left), by thermostat offset (center),
and by incentive signal (right)
also provides utilities with a modulated control signal, but utilities still suffer from
the load rebound problem when the program ends. In addition utilities observe load
control drift when the house indoor air temperatures moves into the regime of the
new set point’s deadband, which is likely to occur under peak load conditions or when
prices are volatile, as illustrated in Figure 3.3.
Aggregate load drift and rebound arise from the evolution of individual device
states during a demand response event. Prior to calling the demand response during
period (a), the loads are fully diversified with their states uniformly distributed over
the deadband range T ± 21 D and over the on and off states according to the duty
cycle (here shown at 50%). This condition corresponds to an equilibrium regime with
the overall “flow” of devices moving in a clockwise direction in heating mode and
counterclockwise in cooling mode. When a demand response event occurs (here shown
for heating mode), a signal is sent to all the devices that causes a reduction of the set
point TD by 12 D. The population of loads that ends up outside the upper bound of
the deadband is immediately turned off while simulatenously an empty region of the
deadband that was outside the lower bound is immediately opened up. The population
is instantly redistributed according to the control strategy and a new significantly
decreased load is observed (b). This condition is maintained for a time until the
population begins to return to the new thermal and state equilibrium (c). When the
new equilibrium is reached (d) it is sustained indefinitely until the thermostat offset
57
Figure 3.3: Thermostat set point control aggregate demand response event, showing
aggregate load (top) and heating system state evolution for the population (bottom)
58
is released and the thermostat deadband reverts to its original position (e) and the
process is reversed (f). Over a population of devices, the aggregate setpoint control
strategies interact adversely with load deadband controls to cause uncontrolled drift
in overall load during the periods (b) to (d) following dispatch and (e) to (f) following
release.
Transactive thermostat designs improve on direct load control methods using
incentive price-based control elements where the utility sends a real-time price signal
PC as often as once every 5-minutes, as shown in Figure 3.2 (right). The consumer
sets a load control gain K that converts this price signal into a temperature offset
that is added to the indoor temperature set point TD . This has the advantage that
it provides the consumer with a great deal of control over how much response they
provide, as well as providing them a real-time economic incentive to respond. It also
provides the utility a well-modulated control signal. However, depending on the type
of controller used, the utility may still experience load control drift before the next
price signal and/or load rebound when the next price signal is sent. This design will
address these potential problems by modifying the controller’s behavior to eliminate
drift between updates of the incentive signal while maintaining full customer control
of comfort.
3.1.1
Building Thermal Model
To evaluate the performance of building space conditioning systems in response to
changes in the thermostat design we require a suitable model of the building thermal
response. The typical residential building with a single-zone heating/cooling system is
modeled using a general equivalent thermal circuit model, as shown in Figure 3.4. For
sufficiently short time intervals the outdoor temperature, internal gains to the air and
mass, and in particular the heating or cooling from the system are assumed constant.
59
Figure 3.4: Equivalent thermal circuit for a residential building
For durations less than 5 minutes the model is derived from the air-mass heat balance
equations
QA + UA [TA (t) − TO ] + UM [TA (t) − TM (t)] + CA dtd TA (t) = 0
(3.1)
QM + UM [TM (t) − TA (t)] + CM dtd TM (t) = 0.
(3.2)
where
UA is the constant conductance from the indoor air to the outdoor air (in Btu/◦ F.h),
CA is the constant heat capacity of the indoor air (in Btu/◦ F),
UM is the constant conductance from the building mass to the indoor air (in Btu/◦ F.h),
CM is the constant heat capacity of the indoor air (Btu/◦ F),
TA is the time-varying indoor air temperature (in ◦ F),
TM is the time-varying building mass temperature (in ◦ F),
TO is the constant outdoor air temperature (in ◦ F),
QA = QI + QS + M QH is the heat added to (or removed from) the indoor air (in
Btu/h), and
60
QM = 0 is the heat directly added to (or removed from) the building mass (in Btu/h)
and is assumed to be zero.
Solving these for T = TA − TO −
QI +QS +M QH
UA
requires that we derive the standard
second-order differential equation for the air temperature in the house after a change
in the heat output from the heating/cooling system1
aT̈ + bṪ + cT = d + f u(0)
(3.3)
where
a =
CA CM
,
UM
b = CM + CA + CM UUMA ,
c = UA ,
d = QI + QS + M QH is the heat gain to the air and mass from all sources including
the HVAC system in the mode M = −1, 0, 1, or 2 for cooling, off, heating and
auxiliary, respectively,
f = ∆M QH is the magnitude of the step change in heat output from the HVAC
system as it changes mode, and
u(0) is the unit step function applied at the time t = 0 modulating the change in
output from the heating/cooling system resulting from a change in the mode
∆M .
The internal gains QI = QV + QO + QE should include the following sources or
sinks of heat.
1
It is convenient to also offset TM although we do so without changing its notation.
61
Ventilation Air exchange with the outdoors can occur as a result of leakage due
to wind and stack effects, forced ventilation from bath and kitchen fans and
from dryer operation, and from open doors and windows. As a general rule, the
number of air-changes per hour (ac/h) can be estimated from the vintage of
the house, and is typically not less than 1/2 ac/h. This heat loss/gain can be
included using
QV = V̇ CA (TO − TA )
(3.4)
where V̇ is the number of air-changes per hour for the building volume.
Occupants The presence of occupants generates both sensible and latent heat loads,
roughly estimated at 120 Btu/h (≈ 35 W) per person [61] or
QO = 120NO
(3.5)
where NO is the number of occupants present in the house.
Enduse Loads Almost all energy consumption by equipment inside the house is
converted to heat and should be added to the internal heat gains. When
considering only electricity demand
QE = 3.412 W
(3.6)
where W is the measured power consumption. Under certain conditions heat
loss or gain goes directly to the envelope mass but we will assume that at peak
conditions these are small relative to the conduction and other heat flows so
that QM ≈ 0.
Solar gains are separated from internal gains because they are weather dependent.
Simulations generally obtain these values from external modeling sources such as
62
typical meteorological year (TMY) data. The total daytime solar gains can be
determined based on glazing area and orientation
#
"
QS = ρG ID ( 21 AV + AH ) + IN
X
An cos αn
(3.7)
N
where
IN is the normal direct beam irradiance on the N insolated glazing surfaces (in
Btu/sf.h),
An is the area of each of the N glazing surfaces in the direct beam (in sf),
αn is direct beam incidence angle on each of the N glazing surfaces in the direct
beam,
ID is the diffuse sky irradiance (in Btu/sf.h),
AV is the total vertical glazing surface area (in sf),
AH is the total horizontal glazing surface area (in sf), and
ρG is the glazing shading coefficient, including exterior (e.g., shading), intrinsic (e.g.,
transmissivity) and interior shading factors (e.g., window treatments).
The denominator of solution for the indoor air temperature T in s-domain takes
the usual form for a second-order system, i.e., as2 + bs + c [62]. The thermal response
of the house is always overdamped as the roots of denominator are real because
2
b − 4ac =
2
CM
2
UA
CA
UA
CA
2
1+2
+2
+ CM
−
>0
UM
CM
UM
CM
(3.8)
63
for all physically realizable values of UA , CA , UM , and CM . The solution to Equation (3.3) in the s-domain is thus
T0 s2 + (Ṫ0 + ab T0 + ad )s +
s(s + p)(s + q)
T (s) =
f
a
(3.9)
where T0 and Ṫ0 are the initial temperature conditions and p and q are the negative
real poles of the house’s thermal response, with magnitudes
r
p=
h
2
1 + 2 UUMA +
CM
CM UUMA + CM + CA −
CA
CM
2
+ CM
h
UA
UM
+
CA
CM
i2
(3.10)
CA
2 CM
UM
r
q=
i
CM UUMA + CM + CA +
h
2
CM
1 + 2 UUMA +
CA
CM
i
2
+ CM
CA
2 CM
UM
h
UA
UM
+
CA
CM
i2
,
(3.11)
for the air and mass, respectively. In time domain we obtain the solution to the air
temperature
TA (t) = Tp e−pt − Tq e−qt + T∞
(3.12)
where
Tp =
aṪ0 +bT0 +d−aT0 p−f p−1
,
a(q−p)
Tq =
aṪ0 +bT0 +d−aT0 q−f q −1
,
a(q−p)
T∞ =
f
apq
and
+ dc .
As a matter of convention q is chosen so that its magnitude is always greater than
that of p. Then p represents the non-dominant pole for the response of the air and q
represents the dominant pole for the response of the mass. In the typical house the
mass response is sufficiently large with respect to that of the air that we find q >> p
by an order of magnitude.
64
Figure 3.5: Air-source heat pump system diagram
This time-domain solution of Equation (3.12) is implemented in GridLAB-D
Version 3.1 [23] used for simulations as discussed further in Chapter 4.
3.2
Residential Heat-pump Systems
The standard system design for a conventionally controlled residential heat-pump is
shown in Figure 3.5. In general a thermostat monitors indoor air temperature which
it uses to signal the heat-pump which mode to operate, depending on the occupancy.
In conventional homes the thermostat operates as a standard Schmitt trigger with
a roughly 1◦ F deadband which gives the hysteresis that is common to all residential
thermostats.
65
3.2.1
Heat-Pump Systems
In normal operating modes for heating and cooling, the heat-pump’s reversing valve
and expansion valves are set to correctly direct the gas flow. When the thermostat
calls for heating, the valves are set so that the indoor coils act as condensors and the
outdoor coils act as evaporators. When the thermostat calls for cooling, the valves
are set the other way around. Although the time required to reverse a heat pump
is relatively short, it is not desirable because cycling between the two can lead to
inefficient performance.
Auxiliary Heating
Auxiliary heating is supplementary heat provided when the lift of the heat pump is
insufficient due to low outdoor air-temperature. It is unfortunate that auxiliary heating
is employed when the temperature difference ∆T exceeds a certain limit, typically 2 or
3◦ F. This “trick” increases energy use during recovery following night-time set-backs.
This increased demand and lower efficiency can be avoided if progressive set-ups are
used or auxiliary heating is suppressed, but such “smart” recovery features are not
always present in conventional thermostats. Note that while auxiliary heating is
running, the normal heat pump continues to operate.
Emergency Heating
Emergency heating is provided when the primary heat pump fails. When emergency
heating is employed the only source of heat is the auxiliary heating system and the
heat pump system is disabled.
66
Figure 3.6: Conventional thermostat wiring diagram
Defrost Cycle
Defrost is required to remove ice build-up on the outdoor coils during heating when
operated above 20◦ F and 60% relative humidity. During defrost, the system is briefly
turned on in cooling mode while the outdoor fans are turned off. The defrost cycle
can be controlled using a timer, measuring the refrigerant condition or by measuring
the pressure drop across the coils [63].
3.2.2
Thermostat Control
Simple heat pumps only require separate fan, heating, and cooling control from the
thermostat, as shown in Figure 3.6. The fan is operated when circulation, heating or
cooling are required. Heating is operated when the indoor air temperature is too low
and cooling is operated when the indoor air temperature is too high. Most thermostats
can also control auxiliary or emergency heating directly (not shown).
67
Multi-Speed Control
Some residential indoor air handling units are equipped with a variable-speed drive
that allow the air flow to be controlled continuously. In general this control allows low
velocity circulation of air when the system is not heating or cooling. This continuous
circulation help avoid air stratification and is done at reduced speed to save energy
and reduce noise. When the heating or cooling is turned on, the fan speed is raised
to deliver the optimal air flow across the indoor coil. Some system also use variable
speed control to slowly accelerate and decelerate air flow when heat-pump heating or
cooling is started and stopped. However, when auxiliary heating is started the fan is
usually run immediately at full speed to avoid damage to the heating element. These
multi-speed strategies are not modeled in this thesis.
Proportional Control
Variable speed fans in central air heat-pump systems offer the opportunity to provide
continuously controllable heating and cooling to conditioned spaces in homes. However this is not generally done because of the efficiency loss resulting from part-load
operation of the compressor and indoor coil heat exchanger. It remains an option
for proportional control or part-load control of residential heating and cooling systems, particularly in circumstances where full-power operation can violate control
performance standards. This mode of operation is not explored in this thesis.
3.2.3
Forced Air System Delays
One important modeling consideration are the delays associated with three aspects of
forced air systems. These include the minute or so required for the heat-pump vapor
circuit to reach thermal equilibrium, the delay associated with air ducting from the
coil to the discharge registers, air mixing in the volume of the home, mass effects in
68
Figure 3.7: Conventional thermostat design (top left) and application (bottom left),
deadband control (top right) and refractory state control (bottom right).
the immediate vicinity of the thermostat, and return air plenum delays. These delays
can cause overshoot and many thermostats come equipped with so-called “anticipator”
circuits to improve occupant comfort. These aspects of the new thermostat will not
be examined in this thesis.
3.3
Conventional Thermostat Performance
The basic control design for a standard residential heating/cooling system is shown in
Figure 3.7. This control strategy maintains the house air temperature close to the
desired temperature chosen by the occupant. It is usual to find that the set point
temperature changes depending on day of the week, the occupancy mode, e.g., home,
sleep, away, vacation, or the operating mode, e.g., circulation, heat, cool, supplemental.
The main feature of this type of control is the differential gap D, also called the
deadband, used to prevent the heating/cooling system from cycling too quickly as the
house responds to the heat flows QH in and out of the air depending on the operating
69
mode. The deadband is almost always constant for a particular thermostat design and
it is usually large enough to prevent short-cycling of air-conditioners and heat-pumps,
which have minimum run-time requirements to allow for pressure equalization before
the next start and reduce motor wear and tear that occurs during compressor start-up
with with non-zero vapor back-pressure.
When implemented using digital circuits, it is typical to find the set points are
implemented with a deadband to synthesize the behavior found in analog thermostats
rather than time-outs and lock-outs for refractory states, as shown in Figure 3.7.
3.3.1
Deadband Value
In principle the deadband is based on how much the indoor air temperature can be
expected to change before the minimum runtime has elapsed, i.e.,
D = ṪA × tmin
(3.13)
However, under mild outdoor conditions, the magnitude of ṪA can be very large
when the system is running. Similarly high magnitudes of ṪA can occur under peak
conditions when the system is not running. So this approach doesn’t usually yield
consistent values for reasonably likely conditions. For historical reasons stemming
from how old mechanical bimetal and coil thermostats worked a differential gap of
about 1◦ F (0.5◦ C) is typically used and most digital thermostats use that value even
though they also have a minimum time lock-out of about 2 to 3 minutes to prevent
short cycles resulting from direct user input of the set point.
In efficient digital thermostat designs one would expect that the deadband is
not determined by the temperature bounds but by the optimal runtime required to
maintain conditions within the comfort band provided. However, this does not appear
70
to be a common practice.
Setting the deadband to nearly zero obviously results in a minimum runtime that
is also nearly zero. Were it not for the lock-out time, the system would “fast cycle” at
roughly a period equal to the feedback lag of the overall system.
3.3.2
Deadband Overshoot
A common problem is that the indoor air temperature is not uniform and it is possible
for the thermostat measurement to lag the average air temperature enough to cause
occupant discomfort. This problem used to be addressed using a small resistor placed
under the sensor coil to create a feed-forward signal. While the heater is running the
resistor was energized and would heat the coil causing the thermostat to turn the heat
off earlier than it normally would. However, this feed-forward strategy didn’t work for
cooling modes. Modern thermostats use PID control loops and can avoid this problem
by estimating system parameters using previous responses to minimize overshoot and
determine how much lead time is needed to reach the desired set point at the specified
time for a set-up or set-down of the temperature. If we assume that the mass is very
near the air temperature, then from air heat balance in Equation (3.1) we have
Ṫ =
Q
UA
−
T.
CA CA
(3.14)
and we find that for control during steady state the maximum overshoot for the
minimum runtime is
TM O = TD +
Q
UA
−
T
CA CA
tmin .
(3.15)
Another way a thermostat can overshoot the deadband is if the mass temperature
of the building is not close to the air temperature. The mass then is heating (or
cooling) the air while the system is heating or cooling, or conversely if it is also cooling
71
or heating while the system is not heating or not cooling. This overshoot results
from non-equilibrium conditions at the time the system changes state. Under steady
operation and long-term setbacks/setups, conventional thermostats do not encounter
this situation. If the thermostat set point is moved before the mass temperature
reaches equilibrium then overshoot becomes more likely. This behavior is likely to
contribute to some of the non-linearity seen in conventional thermostats that are
retrofitted for fast demand response. In such cases we have
Ṫ =
UA
UM
Q
−
T−
(T − TM ).
CA CA
CA
(3.16)
and the non-steady maximum overshoot for the minimum runtime is
TM O
3.3.3
Q
= TD +
−
CA
UA UM
+
CA
CA
UM
T+
TM tmin .
CA
(3.17)
Occupancy Schedule Set-Up/Set-Back
As noted above, conventional thermostats without setbacks use a temperature set point
deadband that is typically on the order of D ≈ 1◦ F and we expect that |TA −TD | < 12 D.
Under normal heating/cooling conditions the mass temperature is very close to the
air temperature so the likelihood of overshoot due to the initial condition Ṫ0 is very
small. When the heating system turns off Q = 0, TA = TD + 12 D and we have
Ṫ0 ≈ −
UA D
.
2CA
(3.18)
When the cooling system turns on Q = −QC and
Ṫ0 ≈ −
QC
UA D
−
.
CA
2CA
(3.19)
72
When the heating system turns on Q = QH and TA = TD − 21 D
Ṫ0 ≈
UA D
QH
−
.
CA
2CA
(3.20)
When the cooling system turns off (Q = 0) we have
Ṫ0 ≈ −
UA D
.
2CA
(3.21)
Future work can take advantage of these relationships to estimate the key building
performance parameters UA and CA based on temperature and electric usage measurements. Since the value of COP is relatively well known, the values of QH and QC
can be estimated quite easily.
When a long-term temperature set-back (i.e., more than 1 hour since the last
set-up) is started we have Q = 0. The temperature difference T − TM is typically not
greater than
1
2
the deadband which is presumably significantly less than the set-back
offset ∆T , so in general
Ṫ0 ≈
UM
∆T.
CA
(3.22)
Similarly, when a long-term set-up is engaged to recover from a set-back, we
have Q 6= 0 and the air and mass temperatures are approximately the old desired
temperature TD , while the new desired temperature is TD + ∆T . The initial condition
Ṫ0 can then estimated as
Ṫ0 ≈
UM
Q
∆T +
CA
CA
(3.23)
where Q is either QH or −QC depending on whether the set-up is for heating or
cooling, respectively.
Future work can take advantage of these relationships to estimate the additional
key building performance parameter UM based on the response to set-back and set-up
73
events.
Modern digital thermostats that implement setback/setup based on occupancy
modes typically include so-called “anticipator” control elements to account for the
lag in reaching the desired set point at the desired time. These control elements use
estimates of the heating/cooling rate Ṫ such as those described above, typically using
proprietary parameter estimation techniques. Occupancy set-back/set-up lead time is
not modeled in this thesis.
In addition, to avoid less efficient operating modes thermostats should not engage
supplemental heating during large heating season set-ups. Naturally, the thermostat
must not engage heating of any kind during large cooling season set-backs.
3.3.4
Auxiliary (Supplemental) Heating Operation
Supplemental heating should be engaged only when outdoor conditions cause the
heat-pump efficiency to fall below the 1.0 efficiency of the resistance heating coils,
or when the lift of the heat-pump becomes insufficient to maintain the set point.
Ideally the determination of this outdoor temperature should be based on system
parameter identification, but even modern thermostats do not have such a capability,
and often do not even measure outdoor temperature, so in general the decision to run
supplemental heating is based on temperature differential exceeding a limit, e.g., 2◦ F
(1◦ C) or runtime, e.g., ton > 1 hour.
The impact of supplemental heating is to increase the value of QH . This behavior
is modeled in this thesis and the electric load impact is modeled as well.
3.3.5
Emergency Heating Operation
The emergency operating mode is only engaged when the heat-pump system has
failed. In general, this is done through manual input or occurs normally by virtue of
74
supplemental operation without heat-pump operation. This behavior is not modeled
in this thesis.
3.4
New Thermostat Design
The proposed thermostat design in its present embodiment is shown in Figure 3.8.
Three inputs are provided to the thermostat. The consumer’s indoor air temperature
set point TD and comfort preference K are set every few hours from an occupancy
schedule established by the consumer. The real-time price PC is sent by the utility
every 5 minutes and is derived from various sources such as the hourly wholesale
energy price signal, the real-time imbalance, and local capacity constraint prices, if
any. The price signal is then filtered to separate the component with a time-constant
that matches the building mass response and the component with a time-constant that
matches the air’s response, denoted as the slow response and fast response components,
respectively. Both of these signals are then converted to a temperature offset using
the consumer’s comfort preferences. The slow response temperature offset signal is
compared to the estimated2 mass temperature TM and added to the offset from the
fast response signal. These are added to the consumer’s desired temperature set point
TD , which is finally compared to the observed indoor air temperature TA to determine
the control temperature TC . The signal is updated only when a new price received,
which in the current embodiment is once every 5 minutes.
Once the control temperature TC is determined, the remainder of the system is
implemented in a manner that is consistent with conventional thermostats, and thus
could be used to replace existing thermostat without changing the design of the rest
of the HVAC system. The only difference with conventional thermostats systems is
2
The mass temperature TM is estimated from the system mode M , the air temperature TA and
its derivative ṪA using Equation (3.3), but the specifics of this observer are not in the scope of this
thesis.
75
Figure 3.8: Proposed new thermostat design
1) the controller output M is held for 5 minutes, and 2) there is no deadband in the
controller input E and therefore no hysteresis in the mode output M .
This design recognizes that a house has two fundamental responses, a fast one for
the air and a slow one for the mass. In addition, it recognizes that any price signal
from the utility may have multiple components, including a short term price signal
emanating from distribution capacity or ancillary service markets, and a long term
price signal from bulk energy markets. The purpose of the new thermostat is to control
the long-term response of the house based on the bulk energy price independently
of the short-term response of the house, which is based on the distribution capacity
or ancillary service price signal. The focus on this thesis is on the fast response
component of the thermostat design. The slow response control will be considered in
future work.
There are potentially other components to the response of a house, such as the
ramping response, that are not controlled by this design. At this time the price signals
are not expected to arrive frequently enough (e.g., ts < 1 minute) to allow control
of a house’s ramp response anyway. Such signals are not expected to include any
primary regulation components for the foreseeable future. So both this signal and the
76
Figure 3.9: Slow response controller design
associated ramp response are not addressed by this thesis. Similarly, very long term
price signals, such seasonal or annual changes in the energy price or the fixed price
are not considered by this design.
The controller design process will require design of the following components. The
slow response components will be designed in future work, while the fast response
components will be the subject of this thesis. The remained of this section describes
the main elements of the control system design in more detail.
Slow Response Controller: The slow response controller applies a low pass filter
to the incoming price signal to remove any subhourly components. Based on
the filtered input signal and the consumer’s comfort preference, a temperature
offset will be output, which is compared to the estimated mass temperature to
determine what change to the mass temperature is required. This conceptual
design of this controller is shown in Figure 3.9 but it is not in the scope of this
thesis.
Mass Temperature Estimator: The air temperature is sampled quasi-continuously
(relative to the price signal) and its derivative is estimated using a band-limited
differentiator. The air temperature, its derivative and the system mode are then
used to estimate the mass temperature and the house model parameters, i.e.,
second-order system’s poles. The mass state estimator is not in the scope of this
thesis.
77
Figure 3.10: Fast response controller design (ts = 5 minutes for all discrete-time
control elements)
Fast Response Controller: The fast response controller applies a high pass filter to
the incoming price signal to remove any hourly or greater components. Based on
the input signal and the consumer’s comfort preference, a temperature offset will
be output, which is added to mass temperature error and the desired temperature
before being compared to the feedback indoor air temperature. Because this
thesis only addresses the fast response system, the filter is omitted from the
analysis and focus is only on the comfort gain K, which is provided by the
consumer to manage their comfort preferences. The system design including
this comfort-only controller is shown in Figure 3.10.
System Mode Controller: The HVAC system mode controller uses the temperature
error to determine whether cooling, heating and auxiliary heating is required
and is in the scope of this thesis, as shown in Figure 3.10.
The remainder of this chapter will address the design of the fast-response controller.
This capacity response controller is based on the Olympic controller design except that
it uses a high-pass filter to remove the super-hourly components of price variations.
This maintains only the input signal that can be reasonably responded to by the air
in the building.
78
3.4.1
High-Pass Filter
The filter is designed based on a 5-minute price sampling interval. A finite-impulse
response filter is initially proposed to avoid the pitfalls of feedback filter design in
spite of the additional processing requirements. The order of the filter will depend
on how far back the price history must go and the desired delay in the filter. A 48th
order filter is used to balance these considerations. The filter cut-off frequency will
depend on the time constant of the building mass. We use mass pole q of the building
to determine the frequency cut-off for the high-pass filter.
For the simulation studies used in this thesis, a high-pass cut-off frequency corresponding to a 1-hour thermal mass time constant is used. Because the simulation
generates price data by sampling a normal distribution every five minutes, the spectrum of the price does not contain any significant low frequency components relative
to the high frequencies. We avoid simulating the high-pass filter by generating a
Gaussian short-term price signal with the desired characteristics of filter output and
the filter is not implemented in this thesis.
3.4.2
Comfort Gain Parameter
The customer’s comfort setting K will serve as the gain for the capacity response
controller. The comfort parameter determines the gain of the input price signal.
Because the system is a LTI and the comfort gain is on the input only and is not in the
feedback loop, it is unnecessary to consider the impact of the gain in the performance
studies themselves. All the results will be linear within the saturation limits of the
hardware. For the purposes of this thesis, the comfort gain will be set constant at
K = 1 for all study cases.
79
Figure 3.11: Diagram of subsampling response of the new thermostat
Table 3.1: Fast response of load control for t < ts
Mode
Cool
Off
Heat
Aux
3.4.3
M(z)
−1
0
1
2
Q(z)
−Q
0
Q
2Q
COP
2.0
−
3.0
1.0
Response
(kW2 .h/$)
1
KQ
2
0
1
KQ
3
2KQ
House Price Response
The overall response of the house to a changed price signal can be derived from the
system diagram in Figure 3.11. The overall load control transfer function for this
system for t < ts is
W (z)
KQC
=
P (z)
(COP )
(3.24)
The response for the various system modes are shown in Table 3.1.
The design response of a typical house is illustrated in Figure 3.12. These vector
field plots show the evolution over 5 minutes of both the mass and air temperatures
for a range of conditions in a typical house where both temperatures are in the
neighborhood of the set point temperature. The neutral mass response condition
ṪM = 0 is also shown, as indicated by the dotted line TM =
UA
T
UM A
+
1
Q.
UM
Under
steady conditions, the sub-hourly fluctuations in the price signal P (z) are expected
to be Gaussian and should not significantly change the mass temperature because
80
Figure 3.12: House heating (left) and cooling (right) design response for system on
(red/blue) and off (black) with neutral-mass response condition (dotted)
by design any long-term fluctuation in the price is filtered and used to control the
mass temperature separately from the short-term response of the air temperature. In
general we can continue to assume that TM ≈ TA . Then the response to a set-back
or set-up event is no different than it is for conventional thermostats as discussed
Section 3.3.3. As a result, excessive mode cycling is not expected to occur more
frequently under steady state conditions than it does with a conventional thermostat.
However, outdoor air temperature changes, changes in internal and solar gains, as
well as setback/setup schedules are expected to change the mass in ways that the price
signal can oppose. To address these issues, mode cycling control must be established
to prevent the load control system from engaging modes in the short-term that are
counterproductive or inefficient in the long-term. Unfortunately, these controls also
have the effect of changing the expected response of the load control system in ways
that cannot be studied using the current simulation tools. Therefore these protections
will be disabled and the extent to which they occur can be evaluated in future work.
81
3.5
Control Performance Metrics
In this section we develop the control performance metrics for the individual loads.
These metrics will be used to design individual thermostats that meet performance
objectives that can be reasonably expected by utilities and determine the limits of
demand response performance in general. Because the aggregate load model is simply
the sum of the individual LTI models, we can easily extrapolate the aggregate load
performance from the response of the individual models.
3.5.1
Comfort Control Performance
The control performance of a single house is estimated for prevailing TMY conditions
over a 4 week period using the indoor air temperature errors with respect to the set
point schedule.
Temperature Overshoot
Although the short-term fluctuations in the indoor temperature set point do not
significantly impact the mass temperature, any long-term trend in the price is expected
to cause a change in the mass temperature that would noticeably affect the indoor
air overshoot in the time interval ts . The simulation is constructed to preclude these
mass effects until closed-loop mass temperature control can be introduced in later
work. Using Equation (3.15) we can evaluate the temperature overshoot as a function
of mass temperature
TM O = TN +
where TN =
h
Q
CA
−
UA +UM
CA
UM
ts TM .
CA
(3.25)
i
TD ts is the neutral mass condition, i.e., the condition
where the mass temperature does not change over the time interval ts .
82
Mean Temperature Error (MTE)
The mean indoor air temperature bias error is computed with 1 minute sampling of
temperatures as
NT
1 X
[E(t)]
MTE =
NT t=1
(3.26)
where NT is the number of temperature samples taken, E is the total temperature
deviation, including the load control offset.
Mean Temperature Deviation (MTD)
The root mean squared indoor temperature error is computed with 1 minute sampling
of temperatures as
v
u
NT
u 1 X
t
MTD =
[E(t)]2
NT t=1
3.5.2
(3.27)
Energy and Cost Performance
Daily energy use and costs are computed based on whole-house metering (which
include other end-use loads) and the prevailing prices.
Mean Daily Energy (MDE)
The mean daily energy computed daily at midnight using interval energy metering
and is given by
NT
ts X
M DE =
W (k)
ND k=0
where ND is the number of days over which the simulation is run.
(3.28)
83
Mean Daily Cost (MDC)
The mean daily cost is computed using the prevailing tariff (fixed or real-time price)
depending on the scenario as
NT
ts X
M DC =
W (k)P (k)
ND k=0
(3.29)
Mean Cost Deviation (MCD)
The cost variance can be used to determine whether short term price volatility results
in disproportionate cost variation. The mean daily cost deviation is computed as
v
u NT
X
1 u
t
M CD =
[W (k)P (k)ts − M DC]2
ND k=0
3.5.3
(3.30)
Compressor Wear and Tear
The wide variation in Ṫ when the set point is changed before the building mass has
reached thermal equilibrium with the air presents a number of important challenges
that conventional thermostats do not address. It seems inappropriate to retrofit
conventional or modern digital thermostat with demand response inputs that are
expected to operate faster than the mass time-constant of roughly one hour. The
challenge addressed by the new controller is that demand response system must include
protective control elements to reduce potentially damaging mode cycling due to the
large and fast set point changes relative to the mass response of the building induced
by short-term RTP volatility arising from distribution capacity management and
regulation services required by the grid.
Excessive heating/cooling mode cycling can be a problem if the controller output
switches from cooling to heating or from heating to cooling in less 24 hours, or about
84
the time constant of the building’s mass. This time is somewhat arbitrary, but seems
reasonable for all but the most extreme weather fluctuations. However, the current
controller design does not attempt to restrict fast mode cycling so that its prevalence
can be evaluated to determine whether it is a significant concern (see Section 3.5.1).
Because the long-term price signal is not changed in the simulation studies this is not
expected to affect the results.
85
Chapter 4
Experiment Design
This chapter describes the design of the numerical simulation experiments used to
test the performance of the new thermostat. The reference house design is described,
including the loadshapes, occupancy schedules, heat-pump design, control systems and
the locations in which the experiments are run. Then a model system of 7 homes with
no parameter diversity is described. Finally the price signals used to dispatch demand
response needed to determine control performance and outcomes are described.
The overall structure of the real-time price demand response experiments is
illustrated in Figure 4.1 (left). For each study the location, house design specifications
Figure 4.1: Single house model (left) and utility feeder model (right)
86
and tariffs are used to produce a single house model, the performance of which is
observed using thermostat and metering telemetry in the simulation. At the utility
level, a feeder is simulated with real-time pricing based on the transactive control
system used in the Olympic and Columbus projects as shown in Figure 4.1 (right). In
transactive control systems the homes submit bid prices and quantities Pn and Qn to
the market to build the demand curve. The feeder submits the cost PF and available
capacity QF to build the supply curve. The clearing price PC is then determined by
the market and sent to the homes to reset the thermostat setpoints. However, this
thesis focuses only on the open-loop behavior of the utility’s demand response control
system by examining only the response of the aggregate load W to a change in the
clearing price PC within the time interval of a single price signal, i.e., less than 5
minutes. The longer term closed-loop behavior of the utility demand response control
system will be examined in future work.
It should be noted that the unit conventions in this chapter include both imperial
and metric units. As a general rule, imperial units are used for heat (Btu/h) and
temperature (◦ F) in the context of heating and cooling equipment design and operation,
while metric units are used for power (W) and energy (W.h) in the context of electricity
demand and prices.
4.1
Location and Weather
The numerical experiments are conducted in 3 study cities located in the continental
United States. Seattle is in a northern cool climate and is chosen for mild winters
amenable to demand response using heat-pumps and mild summers for which demand
response will be very limited. Phoenix and Miami are in southern hot climates, the
former with dry summer conditions and the latter with humid conditions, both of
87
Table 4.1: Cities and climate conditions
City
TMY File
Timezone
Seattle
WA-Seattle.tmy2 PST+8PDT
Miami
FL-Miami.tmy2
EST+5EDT
Phoenix AZ-Phoenix.tmy2
MST+7
Low
High
Solar
(◦ F)
(◦ F)
(Btu/sf.h)
23
38
27
98
93
115
326
330
340
which will challenge air-conditioning demand response.
4.1.1
Reference Cities
For the study cities Typical Meteorological Year (TMY) data is used and their
timezones are shown in Table 4.1. The TMY method [64] is usable in the studies
because each contiguous data block in TMY data file covers an entire month, with
temperature discontinuities only present at the month boundaries. None of the studies
will span a month boundary so no higher-order outdoor air or solar gain disturbances
are expected in the boundary conditions for the house thermal model.
Depending on the location, the homes are expected to consume between 60 and
70 MBtu annually based on the local energy code. Widely varying contribution to
space conditioning costs for heating and cooling are expected based on the locale.
Thermostat setback schedules are employed. Service hotwater is provided by electric
resistance coils. Based on the 2012 International Energy Conservation Code (IECC)
[65], the expected end-use energy consumptions for the model home are shown in
Table 4.2.
4.2
Reference House Design
The reference house design is a two-storey structure with a crawlspace and an attic.
The space conditioning unit is an all-electric direct expansion heat-pump with a single
88
Table 4.2: IECC end-use energy for model home in study cities
End-use
Seattle
(MBtu/y)
Heating
Cooling
Fans
Hotwater
Lighting
Plugs
Total
Miami
(%)
(MBtu/y)
11.7 18.6
2.9
4.6
3.3
5.3
13.7 21.8
4.9
7.8
26.3 41.9
62.8 100.0
Phoenix
(%)
0.6
0.9
20.9 31.9
5.2
7.9
7.6 11.6
4.9
7.5
26.3 40.2
65.5 100.0
(MBtu/y)
(%)
3.1
4.4
21.6 30.7
6.5
9.2
7.9 11.2
4.9
7.0
26.3 37.4
70.4 100.0
Table 4.3: House design
Parameter
Floor area (sf)
UA (Btu/◦ F.h)
CA (Btu/◦ F)
UM (Btu/◦ F.h)
CM (Btu/◦ F)
Seattle
2400
536
1017
11154
4122
Miami
2400
431
1017
11154
4122
Phoenix
2400
431
1017
11154
4122
speed compressor and fan, which are sized according to the design conditions for the
study cities, Seattle, Miami and Phoenix.
The basic house thermal parameters are chosen so that building code complies with
the performance-based energy code, as allowed for by the IECC code. The thermal
parameters of the reference house are derived by GridLAB-D using the thermal model
developed in Chapter 3 with overall performance summarized in Table 4.3.
4.2.1
End-Use Load Shapes
Internal gains are based on hourly ELCAP load shapes [66], as shown in Figure 4.2.
Because the ELCAP load shape data does not include subhourly fluctuations, the
model in this thesis assumes that internal gains are constant for any given 5-minute
interval during which the response to a change in price is considered.
Modifications to the 1993 ELCAP load shapes are necessary to bring the magnitudes
89
Figure 4.2: 1993 ELCAP loadshapes adjusted with 2013 RBSA demand levels
up to date based on the 2013 Residential Building Stock Assessment (RBSA) load
surveys by the Northwest Energy Efficiency Alliance (NEEA). These are shown in
Table 4.4 [67]. The magnitudes of the daily energy use of each end-use was adjusted
to match the RBSA 2013 survey results. However, the overall 1993 ELCAP shapes
of the loads are similar to the new RBSA shapes and were not changed. The RBSA
load surveys cover only the Pacific Northwest region but the occupancy-driven diurnal
end-use load shapes generally are consistent over most regions of the United States,
include Miami and Phoenix. Regional differences in lighting schedules and waterheater
standby losses are not considered significant relative to the total load and internal
gains in the context of this study.
90
Table 4.4: ELCAP loadshapes update with RBSA results
End-use
ELCAP
Lights
Plugs
Dishwasher
Freezer
Refrigerator
Microwave
Range
Waterheater
Clotheswasher
Dryer
Winter
RBSA Change
ELCAP
Summer
RBSA Change
(kWh/d)
(kWh/d)
(pu)
(kWh/d)
(kWh/d)
(pu)
See note (1)
3.62
21.71
0.60
1.40
1.68
0.17
0.80
8.03
0.15
2.00
−
1.50
1.66
0.38
0.43
−
0.56
0.56
0.50
0.64
−
11.00
0.31
5.03
4.60
−
1.14
11.21
0.28
2.56
2.75
16.50
0.60
1.91
1.98
0.14
0.64
6.28
0.14
1.64
−
1.50
1.95
0.38
0.43
−
0.56
0.56
0.50
0.64
14.47
0.36
3.68
3.90
See note (2)
1.43
14.34
0.31
3.12
Notes:
(1) In ELCAP lights and plugs are combined
(2) In ELCAP all cooking is combined
Table 4.5: Occupancy and thermostat set point schedule
Occupancy
Night
Home
Away
Vacation
4.2.2
Weekday
Weekend
Heating
Cooling
Comfort
(◦ F)
(◦ F)
($/◦ F)
68
72
66
−
76
78
80
−
1.0
1.5
0.5
−
20:00−6:00
23:00−7:00
6:00−8:00,18:00−22:00 7:00−23:00
8:00−18:00
−
−
−
Occupancy Schedules
The residential occupancy schedule is chosen for a typical dual-income family with 2
children in school, as shown in Table 4.5. The occupancy comfort settings are based
on the settings observed in the Columbus demonstration, as shown in Figure 4.3.
4.2.3
Indoor Air-Temperature Set Point
The indoor air temperature set points are normally on a setback schedule. The
simulation studies employ setback schedule suitable for a two-worker middle-income
91
Figure 4.3: Columbus demonstration project comfort settings (Source: Steve Widergren, Pacific Northwest National Laboratory)
family with children in public school, as shown in Table 4.5.
4.3
Heat-Pump Sizing
The heat-pump system capacity is based on the larger of the cooling and heating
design capacities.
4.3.1
Cooling Capacity
The cooling capacity is determined by the cooling design conditions for each city, i.e.,
maximum internal gains, maximum solar gains, and maximum outdoor temperature.
The cooling capacity must satisfy the steady state heat balance. From Equation (3.1)
92
we find
QC = UA TC − UA TD + QI + QS + QO
(4.1)
where the following are given at the design condition at the peak cooling hour of the
year
QC is the cooling design capacity
UA is the effective envelope conductance
TD is the desired indoor air temperature
TC is the outdoor temperature,
QI is the total internal heat gains,
QS is the total solar heat gains,
QO is the total occupant heat gains,
In addition a 30% latent heat load factor is included for Miami to account for high
humidity under the cooling design condition.
4.3.2
Heating Capacity
The heating capacity is determined by the heating design conditions for each city,
i.e., minimum internal gains, no solar gains, and minimum outdoor temperature. The
heating capacity must satisfy the steady state heat balance for at worst 20◦ F, or
QH = UA TD − UA max(TH , 20◦ F).
(4.2)
where the following are given at the design condition at the peak heating hour of the
year
93
Table 4.6: Heatpump design criteria and capacities for study cities
Parameter Seattle Miami Phoenix
TH (◦ F)
23
38
27
◦
TC ( F)
98
93
115
QH (Btu/h)
60
54
66
QC (Btu/h)
60
54
66
QC is the cooling design capacity
UA is the effective envelope conductance
TD is the desired indoor air temperature
TH is the outdoor temperature,
These values are given in Table 4.6.
4.3.3
Auxiliary Heating Capacity
The auxiliary heating is sized such that it satisfies the heating design capacity required
to provide emergency heating (no heat-pump contribution) at the peak heating design
condition
QX = UA TD − UA TH .
(4.3)
These values are given in Table 4.6.
4.3.4
Design Capacity
The larger of QH and QC determines the heat pump design capacity for both and is
designated as heat pump capacity QH for the remainder of this thesis. Because none
of the heating design conditions are below 20◦ F, the auxiliary unit capacity QX is the
same as the heating design capacity, as shown in Table 4.6.
94
Table 4.7: Salt River Project (SRP) inclining block rates in Phoenix
Block
4.4
May-Jun
Sep-Oct Jul-Aug
Nov-Apr
(kWh)
(¢/kWh)
(¢/kWh)
(¢/kWh)
< 700
700 − 2000
> 2000
10.57
11.25
12.31
11.17
11.78
12.03
8.03
8.03
8.03
Electricity Prices
The customer cost of electricity varies according to the tariff employed to compute
electricity price. Three tariffs are used in this study, one for customers paying
conventional fixed energy price, one for customers paying time-of-use prices, and one
for customer paying real-time prices.
The tariffs used in the study differ from those extant in the study cities to
facilitate direct comparison of regional response signals with otherwise comparable
characteristics. For example, in Seattle the average price of electricity was 9.6 ¢/kWh
in August 2014. Customers in Miami paid around 12.1 ¢/kWh in the summer of 2014.
Customer in Phoenix paid an inclining block rate as shown in Table 4.7. However, for
the purpose of making results comparable the same tariff structures are used for all
three study cities with price adjustments to maintain revenue neutrality as follows.
The revenue-neutral time-of-use (TOU) tariff was designed first using a methodology
that requires an optimization of the form
minimize [(RF (PF ) − CF ) − (RT (PL , PH ) − CT )]2
PL ,PH
subject to 0 < PL < PF < PH
2PL < PH
where
(a)
(b)
(4.4)
95
RF (PF ) − CF is the net revenue from customers on the fixed price tariff,
RT (PL , PH ) − CT is the net revenue from customers on the TOU tariff,
PL is the off-peak price of the TOU tariff, and
PH is the on-peak price of the TOU tariff.
The constraint (b) is applied to ensure that the on-peak price is sufficiently high with
respect to the off-peak price that it induces demand response.
It is important to observe that although net revenue neutrality is intended, consideration of the cost change of serving the aggregate load is not necessary because
the tariff design assumes no change in the consumer’s response, thus no change in the
load, and thus no change in the cost. Therefore for the purposes of the tariff design
the objective function can be simplified to [RF (PF ) − RT (PL , PH )]2 .
The constraints in Equation (4.4) do not lead to a unique solution. However, a
unique solution can be found by further constraining PL as a function of the fixed
price PF , which we choose to be 2/3 to resemble the TOU tariff used in the Olympic
study. In this case the tariff design problem is simplified to
minimize [RF (PF ) − RT (PL , PH )]2
PH
subject to PL =
(4.5)
2
P
3 F
The study model is greatly simplified by using the same TOU tariff for all studies.
The TOU tariff design is performed for Seattle winter and summer cases only using
the fixed price of 8.1 ¢/kWh, which was used for the Olympic study. The fixed prices
of the Miami and Phoenix summer-only studies are then adjusted to achieve revenue
neutrality by solving the problem
minimize [RF (PF ) − RT (PL , PH )]2
PF
(4.6)
96
Table 4.8: Fixed price tariffs for study cities
City
Seattle
Miami
Phoenix
Tariff
Difference
(¢/kWh)
(%)
8.10
7.88
8.24
−
−2.7
+1.7
The revenue neutral real-time price (RTP) tariffs are obtained for each city by
computing the value of the mean real-time price PA which satisfies the objective
minimize [RF (PF ) − RR (PA , PD )]2
PA
(4.7)
where
PA is the average real-time price,
PD is the standard deviation of the real-time price and
RR (PA , PD ) is the revenue from customers on the real-time price tariff.
The solutions to these three optimization problems are given in the following
sections.
4.4.1
Fixed Price Tariff
The fixed price tariffs obtained are shown in Table 4.8. The tariff in Seattle is set at 8.1
¢/kWh so that results can be compared with the results of the Olympic project. The
Miami and Phoenix fixed price tariffs are changed compared to the Seattle tariff so
that the TOU tariff is revenue neutral for all three cities according to Equation (4.5).
The prevailing fixed rate in Ohio during the Columbus study was 8.64 ¢/kWh.
97
Table 4.9: Seasonal time-of-use rates
Season
Period
Times
Price
(¢/kWh)
4.4.2
Winter
off-peak
on-peak
9:00 – 18:00 & 21:00 – 6:00
6:00 – 9:00 & 18:00 – 21:00
5.40
11.53
Summer
off-peak
on-peak
21:00 – 15:00
15:00 – 21:00
5.40
13.81
Time-of-Use Tariff
The time-of-use (TOU) tariff for all three cities is based on the high-differential tariff
design used for both heating and cooling seasons used in the Olympic demonstration
project, as shown in Table 4.9. The winter tariff is used only for the Seattle winter
study. The summer tariff is used for all three cities’ summer study. Both these tariffs
satisfy Equation (4.5).
4.4.3
Real-Time Price Tariff
The real-time prices received by the thermostat are filtered so that the long-term
trend from the LMP is not present and only the short-term price volatility is present.
For the purposes of this study, the short-term relative volatility of the real-time price
is set at PD /PA = 12.4%. The distribution of the short-term price compared to the
ideal Gaussian distribution is shown in Figure 4.4 (top). The long term prices can
be obtained using a low-pass filter, e.g., an order 144 finite-impulse response (FIR)
with normalized cut-off frequency at π/12 rad/sample. The residual quantity is the
short-term price signal, i.e., P = PLM P − PA , as shown in Figure 4.4 (bottom), where
PA is the long term price and satisfies Equation (4.7). In the model for this thesis
the short-term price is generated by a Gaussian random variable with mean PA and
98
Seattle
Winter Summer
Miami
Summer
Phoenix
Summer
(¢/kWh)
(¢/kWh)
(¢/kWh)
(¢/kWh)
8.084
8.084
7.873
8.222
Figure 4.4: Short-term RTP volatility (top) and RTP means (bottom)
standard deviation PD = 0.1235PA .
4.4.4
Revenue Neutrality
The tariffs are designed to be revenue neutral without demand response, as shown
in Table 4.10. Note that the energy use is affected slightly by the change in control
strategy even when no demand response is extant, as shown in Table 4.11. In the
case of TOU, no demand response is achieved by using the fixed occupancy set point
schedule. In the case of RTP and new thermostats no demand response is achieved by
setting the comfort K = ∞.
99
Table 4.10: Residential energy cost with demand response inactive
Seattle winter
Seattle summer
Miami summer
Phoenix summer
Fixed
TOU
($/month)
($/month)
180.1
98.1
144.8
175.0
180.2
98.1
144.8
175.0
RTP
(%)
+0.0
−0.0
+0.0
−0.0
($/month)
180.1
98.2
144.7
175.0
New
(%)
−0.0
+0.0
−0.0
−0.0
($/month)
(%)
180.1 −0.0
98.2 +0.0
144.7 −0.0
175.0 −0.0
Table 4.11: Residential energy use with demand response inactive
Seattle winter
Seattle summer
Miami summer
Phoenix summer
4.5
Fixed
TOU
(kWh/day)
(kWh/day)
79.4
43.3
65.6
75.9
RTP
(%)
79.4 0.0
43.3 0.0
65.6 0.0
75.9 0.0
(kWh/day)
New
(%)
79.5 +0.1
43.3 +0.1
65.7 +0.1
75.9 +0.1
(kWh/day)
(%)
79.5 +0.1
43.3 +0.1
65.7 +0.1
75.9 +0.1
Performance Metrics
The individual houses are simulated in the four study cases for fixed prices, time-of-use
prices with and without demand response, and real-time prices with and without
demand response for both the transactive thermostat design and the new thermostat
design. In the case of TOU homes, the response results from the change of set point
from an occupancy schedule to a tariff schedule. In the case of the RTP homes the
response results from the change from a 1◦ F deadband around each set point to the
±∆P/K comfort set point offset from the heating or cooling set points, where ∆P is
the normalized price differential (PC − PA )/PD .
The following performance data are collected for analysis. Data is not collected
from the experiment homes when the results are the same as the baseline fixed price
home.
Design: (1 season sampling interval) effective building heat transfer coefficient, floor
area, envelope heat transfer coefficient, air heat capacity, mass heat transfer
100
coefficient, mass heat capacity, design heating capacity, design cooling capacity,
heating design temperature, cooling design temperature.
End-uses: (1 hour sampling interval) system, lights, plugs, dishwasher, microwave,
freezer, refrigerator, range, clotheswasher, dryer.
Meter: (5 minute sampling interval) energy interval meter, prevailing price, monthly
bill accumulator.
House: (1 minute sampling interval) Outdoor air temperature, indoor air temperature,
heating set point, cooling set point, occupancy, system mode
Using the data collected, the following performance data are computed for analysis.
Energy use: Average daily energy use is obtained from the meter at 1 hour intervals
for houses on fixed and TOU tariffs and at 5 minute intervals for houses on RTP
tariffs.
Cost: Monthly cost is calculated by summing energy use interval metering and
multiplying by the prevailing price for the interval. The billing period ends on
the last day of the month at 23:59:59.
Temperature error: The mean temperature set point error is obtained by computing
the mean of the difference between the indoor air temperature and the prevailing
set point, e.g., heating in winter and cooling in summer.
Temperature deviation: The mean temperature deviation is obtained by computing the mean of the square difference between the indoor air temperature and
the prevailing set point, e.g., heating in the winter and cooling in summer.
Discomfort degree-hours: The discomfort degree hours for cooling and heating are
obtained by computing the time-integral of indoor air temperature above (cooling)
101
Figure 4.5: Cooling and heating discomfort degree-hours
and below (heating) the maximum and minimum allowable temperatures. In
the case of the fixed and TOU homes this range is TC + 12 D and TH − 21 D,
respectively. For the RTP and new thermostat homes it is TC + 3/K and
TH − 3/K, respectively. This metric is illustrated in Figure 4.5.
4.6
Summary of Experiment Design
Table 4.12 summarizes the experiment model features used in evaluating the performance of the new thermostat design.
102
Table 4.12: Summary of Experiment Model Features
Fixed
TOU
RTP
New
Thermal Response
1st-order response
Y
Y
Y
Y
2nd-order response
Y
Y
Y
Y
3rd-order response
N
N
N
N
Delay response
N
N
N
N
Heat Pump
Heating
Y
Y
Y
Y
Cooling
Y
Y
Y
Y
Auxiliary
Y
Y
Y
Y
Emergency
N
N
N
N
Defrost
N
N
N
N
Reversing lockout
N
N
N
N
Controller
One speed fan
Y
Y
Y
Y
Two speed fan
N
N
N
N
Variable speed fan
N
N
N
N
Proportional fan control
N
N
N
N
Thermostat deadband (◦ F)
1.0
1.0
1.0
0.0 (1)
Refractory time (min)
2
2
2
5
Set points
2
2
2
2
Setback (◦ F)
–
2(2)
–
–
(2)
(3)
Occupancy modes
3
3
3
3(3)
Comfort setting (p.u.)
–
–
1.0
1.0
(2)
Price signal
N
2-tier
5 min 5 min
Notes:
(1)
Deadband is disabled.
(2)
Setback schedule follows TOU price schedule with DR.
(3)
Occupancy mode controls both set point and comfort.
103
Chapter 5
Evaluation of New Thermostat
This chapter describes the analysis method and results obtained for the performance
of the new thermostat control design when compared to that of conventional fixed and
time-of-use (TOU) pricing setback schedules, as well as the transactive thermostat
used for the Olympic and Columbus real-time pricing (RTP). The energy, comfort,
and economic impacts are summarized and discussed to determine whether the new
thermostat performs well enough to be accepted by consumers and utilities and with
a view to identifying the open issues and opportunities for future research.
5.1
Energy Use Impacts
The performance evaluation simulations are run with nominal demand response, i.e.,
for TOU a 2◦ F setback schedule that coincides with the tariff schedule, and for RTP
comfort gain settings for night, home, and away of K = 1.0, 1.5, and 0.5, respectively.
The energy use impacts are shown in Figure 5.1. The TOU demand response
shows a reduction in energy consistent with customers who change their thermostats
from the unresponsive occupancy-driven set point schedules to responsive tariff-driven
set point schedules. These results are consistent with those of other studies of TOU
104
Fixed
TOU
(kWh/day)
(kWh/day)
79.4
43.3
65.6
75.9
75.9
43.3
63.8
74.2
Seattle winter
Seattle summer
Miami summer
Phoenix summer
RTP
(%)
(kWh/day)
−4.4
+0.1
−2.7
−2.2
71.2
42.9
66.2
76.3
New
(%)
−10.3
−0.9
+0.9
+0.6
(kWh/day)
(%)
72.2 −9.1
44.1 +2.0
66.5 +1.3
76.8 +1.2
Figure 5.1: Total home energy use with demand response active
demand response [31].
The RTP results for heating shows a very significant energy use reduction but
cooling results are mixed and modest in comparison. It should be noted that increases
in energy consumption were also observed in the Olympic and Columbus results,
although the magnitude of the increase was much greater in the Olympic results
because of an error in the auxiliary heating control.
In the Olympic study an increase in energy of about 16% was observed for heating
105
conditions [5]. This increase is believed to be caused by the unnecessary use of
auxiliary heating during thermostat set-up events in excess of 2◦ F. To date there has
been no attempt to rigorously study what the Olympic results would have been had
the auxiliary heating control not be misapplied. It should be noted that this thesis
has completely corrected the auxiliary control problem only in the RTP and new
thermostat, and the auxiliary heating can be engaged during thermostat set-up in the
winter. In any case this result may provide initial evidence of what the Olympic study
would have yielded had the auxiliary heating control been implemented correctly then.
The Columbus study also found increases in energy use in cooling conditions of
about 1% when congestion pricing was extant. Decreases of about 5% were observed
when congestion pricing was not in effect [6]. Because congestion pricing typically
expressed itself in short-term price volatility, the summer results are consistent with
the observations from the Columbus study.
In general the heating energy use impacts of the new thermostat are similar to
those of the RTP thermostat. However, the cooling energy use impacts of the new
thermostat are approximately double those of the RTP thermostat. It is not clear
exactly what is the root cause of this increase, but it is possibly related to so-called
“round-trip” efficiency considerations that result from the use of thermal storage as
a proxy for electric energy storage [68]. These round-trip efficiency impacts may
be increased by the minimum 5-minute runtime under the new thermostat, which
preferentially increases the amount of thermal storage being used for short-term events
with respect to long-term events.
106
Table 5.1: Heating and cooling relative set point errors
Seattle winter
Seattle summer
Miami summer
Phoenix summer
5.2
Heating
Fixed TOU RTP New
(%)
(%) (%) (%)
6.0
1.2
0.4
0.2
0.5 < 0.1
0.2
0.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Cooling
Fixed TOU RTP New
(%)
(%) (%) (%)
0.0
0.0
0.0
0.0
0.3
0.3
0.0
0.0
0.7
0.9 < 0.1
0.2
0.8
1.1
0.2
0.3
Comfort Impacts
The consumer comfort impacts are examined using two performance metrics. The
heating/cooling relative set point errors are computed as the standard deviation of
the air temperature with respect to the prevailing set point. The fixed and TOU
set point errors are computed with respect to the deadband of D = 1◦ F, so only air
◦
temperatures observed outside ± 12 F are considered. Such errors occur each time the
set point is changed either from a change in occupancy schedule or price change, which
explains why the set point errors are relatively high for both fixed and TOU controls.
The RTP and new thermostat control set point errors shown in Figure ?? account
for the comfort setting K and thus allow for larger fluctuations of the indoor air
temperature, provided it does not go outside the consumer’s ±3K ◦ F comfort band.
Because the price signal is generated from a Gaussian distribution, we expect the RTP
signal to deviate by more than 3σ less than 1% of the time. The new thermostat can
deviate more because of the increased probability of indoor air temperature overshoot.
Nonetheless Table 5.1 indicates such a deviation occurs less than 1% of the time with
the new thermostat.
We also apply a more sensitive evaluation of the comfort performance using the
discomfort degree hour method for deviations in excess of 1◦ F, as shown in Figure 5.2.
The results suggest that while the new thermostat is slightly less able to maintain the
107
Fixed
TOU
RTP
New
(◦ F.h)
(◦ F.h)
(◦ F.h)
(◦ F.h)
83
1
4
3
7
2
4
4
21
3
39
48
19
1
63
67
Seattle winter
Seattle summer
Miami summer
Phoenix summer
Figure 5.2: Heating and cooling discomfort degree hours for 1◦ F deviations
consumer’s preferred comfort, it is very nearly as good as the RTP thermostat and
better than the conventional fixed thermostat with a setback schedule.
5.3
Economic Impacts
The utility revenue impacts, which is also the consumer cost impacts of the TOU, RTP
and new thermostat demand responses are shown in Figure 5.3. In this analysis we will
108
Seattle winter
Seattle summer
Miami summer
Phoenix summer
Fixed
TOU
($/month)
($/month)
180.1
98.1
144.8
175.0
RTP
(%)
146.2 −18.8
99.5
+1.3
143.3 −1.0
173.8 −0.7
($/month)
New
(%)
($/month)
154.4 −14.3
94.3 −4.0
139.0 −4.0
167.1 −4.5
(%)
155.8 −13.5
96.6 −1.6
139.7 −3.5
168.4 −3.8
Figure 5.3: Energy cost with demand response active
consider the consumer’s budget as equivalent to what is denoted “income” in the sense
that is used in the consumer theory of microeconomics, i.e., it is the invariant amount
of the consumer’s money allocated to comfort obtained from electricity consumption.
Other consumer goods are not considered, and in this context only the consumer’s
allocation of money to off-peak versus on-peak electricity is examined. This way
“revenue neutral” for the utility is equivalent to a constant consumer budget and
the revenue neutrality tariff design corresponds to designing a tariff the satisfies a
109
Figure 5.4: Time-of-use demand, revenue neutral and revenue expansion paths
consumer’s constant budget constraint in the absence of demand response.
The responsive thermostat designs all give rise to demand elasticity that can be
estimated from the simulation, with the understanding that this elasticity is primarily
driven by the choices of setback temperatures in the case of TOU customers and the
choices of occupancy-based comfort settings in the case of the RTP customers. In
either case we estimate the demand elasticity by computing the ratio of the fractional
change in energy use ∆E/E to a fractional change in energy price ∆P/P , or
η=
P ∆E
E ∆P
(5.1)
TOU energy demand elasticity is estimated separately for off-peak and on-peak
conditions with respect to each of the customer’s occupancy modes under fixed pricing
(i.e., home, away, night), as shown in Figure 5.4. Energy demands are plotted with the
revenue neutral lines for that occupancy mode. The consumer’s indifference curves (not
shown) are neither perfect substitutes nor perfect complements, but they are expected
to be convex toward the origin, i.e., Cobb-Douglas [42]. The demands shown are the
110
consumer’s optimal choices where the budget lines are tangent to those curves. The
resulting revenue expansion path is analogous to an income consumption curve for a
representative customer because the budget curves are equivalent to revenue neutrality
in this context. For TOU demand response the revenue expansion path is positive
indicating that TOU energy is indeed a normal good and the indifference curves are
therefore convex. Because no corner solutions for on-peak/off-peak consumption were
found and in fact there is no reason to believe that TOU off-peak energy is a perfect
complement to on-peak energy, the indifference curves are thus strictly convex toward
the origin.
In general negative values of elasticity are expected because the change of energy
use should have an inverse relation to the price. However, the fixed price occupancy
schedule causes large set-back and set-up changes in energy demand that give rise
to large fluctuations in the elasticity of demand for TOU response. The transition
periods are when the greatest amount of inter-temporal substitution is taking place
and this effect confounds the calculation of demand elasticity, as observed in Table 5.2.
In some cases the change in schedule confers a relatively large benefit when the
TOU set point schedule is adopted, this results in very large elasticities, while in other
cases a very large penalty is incurred. As a result the total energy consumption changes
for various combinations of occupancy and tariff schedules can vary significantly based
on the sign and magnitude of the substitution across the occupancy or tariff boundary.
This gives rise to high sensitivity of demand elasticity with respect to the consumer
choices of temperature set points and occupancy schedule. The high sensitivity of
observed demand elasticity for TOU demand response is what makes the problem of
optimal TOU tariff design so challenging for utilities and regulators and is in large
part the rationale for seeking tariffs that allow consumers to observe real-time prices
111
Table 5.2: TOU demand elasticity
City
Seattle winter elasticity
of Off-peak demand
with respect to Away occupancy
with respect to Night occupancy
with respect to Home occupancy
of On-peak demand
with respect to Away occupancy
with respect to Night occupancy
with respect to Home occupancy
Seattle summer elasticity
of Off-peak demand
with respect to Away occupancy
with respect to Night occupancy
with respect to Home occupancy
of On-peak demand
with respect to Away occupancy
with respect to Night occupancy
with respect to Home occupancy
Miami summer elasticity
of Off-peak demand
with respect to Away occupancy
with respect to Night occupancy
with respect to Home occupancy
of On-peak demand
with respect to Away occupancy
with respect to Night occupancy
with respect to Home occupancy
Phoenix summer elasticity
of Off-peak demand
with respect to Away occupancy
with respect to Night occupancy
with respect to Home occupancy
of On-peak demand
with respect to Away occupancy
with respect to Night occupancy
with respect to Home occupancy
Energy use
Fixed TOU
Demand
Elasticity
(kWh)
(kWh)
(pu)
67
186
93
84
244
121
-0.57
-0.66
-0.66
51
148
73
27
77
38
-1.70
-1.82
-1.80
46
123
60
44
121
60
0.07
0.05
-0.01
27
55
27
28
57
28
0.07
0.07
0.08
71
190
99
67
180
94
0.15
0.14
0.12
30
82
39
31
84
40
0.06
0.05
0.05
78
206
107
74
197
103
0.12
0.11
0.10
42
107
50
43
109
51
0.05
0.04
0.04
112
[69]. In any case the absence of reports describing failed TOU tariffs is certainly not
evidence that TOU tariffs necessarily work—selective publication of tariff design can
lead to a bias toward reporting of TOU tariffs that work, with exceptions noted [70].
The defects of the TOU tariff design could be remedied by producing separate
tariffs for each city and/or season. However, it is the objective of this thesis to make
the TOU controller technical performance comparable to those of the RTP and new
thermostat, so resolving economic deficiencies of the TOU tariff design itself is not in
the scope of this thesis and the tariff design is not corrected to eliminate this problem.
Unfortunately there is no accepted method for performing the demand elasticity
analysis for RTP results and thus neither for the new thermostat. We can only
compute the aggregate demand elasticity and the elasticities of the night, home, and
away occupancies, as shown in Table 5.3. The demand elasticity of the new thermostat
is very close to that of the RTP thermostat while both consistently perform better
than the TOU thermostat, to the extent that one can compare these to the TOU
elasticity results (see Table 5.2). Certainly the RTP and new elasticities are more
consistent with previous studies and more stable than those for TOU across the various
occupancy modes.
The results also suggest that certain occupancy modes are indeed sufficiently elastic
to justify deployment of RTP systems to mitigate on-peak demand. It is interesting
to note that “away” (weekdays only) in the summer is highly elastic as expected,
but home is more elastic than night. This is very likely due to the fact the night
setback in the conventional fixed tariff thermostat is already quite energy efficient and
there is little opportunity for additional savings, while there are still some savings
to be realized in the daytime on weekends. This observation suggests that the new
thermostat may present better elasticity characteristics than expected during summer
peak cooling conditions.
113
Table 5.3: RTP demand elasticity
City
Seattle winter elasticity of
Aggregate demand
Night demand
Home demand
Away demand
Seattle summer elasticity of
Aggregate demand
Night demand
Home demand
Away demand
Miami summer elasticity of
Aggregate demand
Night demand
Home demand
Away demand
Phoenix summer elasticity of
Aggregate demand
Night demand
Home demand
Away demand
5.4
TOU
RTP
New
(pu)
(pu)
(pu)
-0.28
0.76
4.42
0.91
-2.41
0.19
-0.23
0.32
-1.90
0.20
-0.23
0.32
-0.12
0.41
0.86
-0.77
-0.27
0.25
-0.16
-0.19
0.54
0.24
-0.17
-0.18
1.55
-0.77
0.01
-1.72
0.17
0.23
-0.07
-0.26
0.27
0.22
-0.06
-0.25
1.48
-0.54
0.04
-1.73
0.11
0.24
-0.09
-0.27
0.23
0.23
-0.08
-0.24
Feeder Load Control Impacts
A feeder-scale simulation of 100 homes in Phoenix is used to illustrate the open-loop
response of the load to changes in the price signal. A sample of the output is shown
in Figure 5.5. The result clearly illustrates how the new thermostat (blue) remedies
the RTP thermostat drift problem (red) as they respond to the price signals (black).
Note that the feeder load control is simulated as an open-loop control and the demand
response gain K is not influenced by the states of loads prior to clearing the market.
114
Figure 5.5: Feeder open-loop load control response to price
5.5
Open Issues and Opportunities
In the conduct of the experiment and analysis of the new thermostat design a number
of issues were identified that, if addressed, would allow a systematic estimation of
impacts and benefits of this and future designs for thermostatic controls.
Occupancy Schedules: The fixed tariff customer’s baseline occupancy has a significant impact of the benefits analysis of various alternative rates. TOU customers
typically alter their thermostat’s occupancy schedule to match the tariff schedule.
In response to these changes in schedule TOU customers also change the set
points such that they maintain the desired comfort. This mixed temporal and
thermal response is very challenging to analyze unless the responses can be
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separated. At the present time there is no analytic method for separating these
two responses. Consequently it can be difficult to obtain clear and distinct loadshifting and load-curtailment performance results when occupancy schedules are
altered by the consumer.
Lack of suitable economic analysis methods: At present the economic analysis
of TOU rate is very challenging [71]. In this thesis only a simplistic analysis
of demand elasticity was performed. Other methods for studying TOU rates
exist but they can be very difficult to implement [72] and were not attempted in
this analysis. The challenge for RTP rates is even more serious because there
are no generally accepted methods yet for determining the short-term demand
elasticity of consumers under real-time tariffs. However, the development of
linear time-invariant load control designs by utilities may allow the identification
of relatively simple transfer functions from price to energy that permit the
derivation of analytic forms for the demand elasticity of load in the aggregate in
both time domain and frequency domain.
The design and analysis of this new thermostat has revealed a number of potential
valuable avenues for future research in transactive control system design.
Mass temperature control: The design of fast-acting demand response using a
building’s air heat capacity revealed the opportunity to separately construct
a system for observing and controlling the building thermal mass. This slowacting demand response can be used to “position” buildings to respond better
to long-term fluctuations in energy prices arising from existing forward energy
markets independently from the short-term fluctuations that would arise from
ancillary service markets when they become more prevalent.
Split tariffs: There is no economic justification for requiring consumers to place both
116
technology-response loads and behavior-response loads on the same tariff. Split
tariffs should be considered wherein the technology-driven responsive loads (e.g.,
thermostatic loads) are sent fast-changing prices (e.g., RTP) while behaviordriven responsive loads are subject to regular time-of-use prices that are known
to influence consumer habits. Meanwhile, unresponsive loads can continue to be
charged fixed prices that encourage energy conservation.
5.6
Summary of Results
The principal result of this thesis is the design of a transactive thermostat for residential
space heating/cooling that resolves the short-term load control drift problem found
in existing real-time price responsive thermostats. The new control system can be
modeled as a linear time-invariant system for short-term changes to real-time prices.
This is an important innovation because it enables the design of utility load control
systems that are themselves modeled as linear time-invariant systems for short-term
real-time price response and thus subject to design and integration with existing
control systems using classical control theory. This can dramatically increase the
short-term rate of adoption of transactive control systems by making them much
more approachable to power system engineers who haven’t been trained or aren’t
comfortable working with highly non-linear or distributed control systems.
The technical analysis shows that the comfort impacts on the consumer are consistent with those of the RTP thermostat and within the comfort control specifications
of the consumer, even as occupancy changes. In addition, the energy consumption
and cost savings opportunities for the consumer are consistent with those of the RTP
thermostat. Thus the new thermostat can be expected to offer roughly the same
overall energy and cost performance as the RTP thermostat did in the Olympic and
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Columbus studies, while correcting the known deficiencies observed in those studies.
The economic analysis of the new thermostat shows that like the RTP thermostat,
the demand elasticity is much more consistent and predictable than TOU demand
response provides. TOU performance is very sensitive to consumer choices in set point
schedules, a process that consumers find very challenging to follow and thus is not
likely to result in a setup that reflects the consumers true preferences. In contrast,
RTP and the new thermostat’s performance is sensitive primarily to the consumer’s
comfort setting, which is much simpler to understand and set, and thus is much more
likely reflect consumers’ true preferences. However, under certain conditions, the new
thermostat, like the RTP thermostat still has positive demand elasticity, meaning the
energy use is sometime proportional to price rather than inversely proportional to
price. This condition appears to be limited off-peak hours and may suggest that RTP
energy consumption may not be a strictly normal good under certain conditions when
compared to fixed price energy consumption.
Finally, a modeling and analysis framework for the study and design of residential
thermostatic device controls has been constructed and validated for this thesis. This
framework can be expanded upon to study the behavior of thermostatic appliances
and to study the effect of feedback control of demand response when deployed by a
utility.
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Chapter 6
Conclusions
This thesis examined the behavior of a new control strategy for residential heating
and cooling thermostats based on the transactive control system design. The new
thermostat outperforms conventional thermostats in providing demand response and
does so in a manner that is highly conducive to aggregate load control by utilities using
real-time price signals. The new thermostat has all the features and advantages of realtime price (RTP) and time-of-use (TOU) thermostats and overcomes disadvantages
typically associated with them. In particular, the main features of this new thermostat
are:
No Aggregate Load Drift: The new thermostat eliminates the hysteresis arising
from the deadband in the Schmitt trigger control element used in conventional,
TOU and RTP thermostats. As a result the new thermostat does not exhibit an
aggregate load drift behavior between price clearing events in the market.
Consumer Comfort Control: The new thermostat maintains satisfactory control
of indoor air temperature. The new thermostat enhances the favorable economic
and thermal control characteristics of conventional, TOU and RTP thermostats.
Most significant is the new thermostat allows consumers to specify a comfort
119
preference for each occupancy mode, e.g., home/awake, night/sleep, away/work,
and by extension any others the consumer might add.
Load shifting and Cost Savings: The new thermostat provides the desired energy
shifting and cost savings properties also found in the RTP thermostats and
enhances those found in the conventional and TOU thermostat, especially in the
short-term response time intervals. In particular, the new thermostat’s summer
air-conditioning demand elasticity for the entire residential load is in the range of
6% to 25% for occupancy modes in cities summer demand response is a resource.
Larger total house demand elasticities could be achieved if a similar control
strategy were adopted for other thermostatic end-use load such refrigerators,
freezers, water heaters, dish washers, clothes washers, and dryers.
Transactive Control Compatibility: The new thermostat demand response implementation is consistent and compatible with the RTP thermostat demand
response design used in the Olympic and Columbus transactive control studies. Thus it can operate in real-time distribution capacity auction system and
provides all the benefits associated with transactive systems.
From the consumer’s perspective the new thermostatic control design’s performance
compares favorably to conventional, TOU thermostats, and RTP thermostat. In
particular the new thermostat exhibits the following characteristics that appeal to
consumers.
Control: The new thermostat offers far better control of fast-acting demand response
than conventional and TOU thermostats and the same degree of control over
short-term comfort as a function of electricity price that the RTP thermostat
offers. Consumers can indicate a comfort preference for each occupancy mode
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and the new thermostat uses that comfort setting to regulate energy use and
significantly reduce heating/cooling costs.
Savings: The new thermostat significantly reduces cost relative to conventional and
TOU thermostats, consistent with those for RTP thermostats, and significantly
improves on the heating energy use and overall cost performance of demand
response using conventional and TOU thermostats.
From the utility’s perspective the new thermostat offers one very significant
advantage over the other demand response thermostat designs. The new thermostat
provides significantly better control tracking of the load for the price given. In
particular, the heating/cooling load under control of the new thermostat will remain
at the level associated with the price given for the entire duration of the pricing
time-interval. Unlike conventional, TOU and RTP thermostats, which allows the
heating/cooling system load to change when the deadband is exceeded, the new
thermostat maintains the system load as dispatched until the new price is received.
Thus the impact of the error is shifted from the utility where it cannot be mitigated
without resorting to more complex bid/response compensation/anticipation strategies
to the consumer where its impact can be mitigated by the consumer’s comfort setting.
The economic impacts of the new thermostat design remain clouded by the lack of
methods for rigorously analyzing real-time price-based demand response. This gap in
our engineering and economic analysis methods remains an important problem that
has yet to be fully overcome. In particular, we are unable precisely determine the
microeconomic performance of the new thermostat control systems. The inability to
precisely quantify instantaneous demand elasticity of real-time price demand response
systems will continue to hamper the deployment of control strategies that employ them
by making it difficult to anticipate the economic impact of their adoption. Utilities
will continue to seek methods that will enhance their ability to minimize their costs in
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the face of demand response, especially as they are forced to respond to growth in the
availability and flexibility of distributed renewable and storage resources.
6.1
Main Contributions
This thesis has demonstrated the following important contributions to the development
of transactive systems.
1. A new thermostat design now exists that enables reliable and predictable aggregate demand response resources and makes them available to utilities for
short-duration fast-acting reliability services. This thermostat design overcomes
the concerns that utilities have with real-time price-based demand response,
particularly in resource planning when they have the greatest financial impact
and in system operation when they have the greatest technical impact.
2. A comprehensive set of performance metrics for demand response control using
thermostatic devices has been developed and implemented using GridLAB-D,
a scalable open-source smart-grid analysis tool. These metrics give utilities
the ability to rigorously design, monitor, and optimize the performance of
aggregate demand response control systems. Consequently utilities will be able
to consistently provide more reliability services based on demand response to
bulk system operators and derive economic benefits that can be passed on to
the consumers who provide the underlying load resources.
3. An economically and technically robust design for residential HVAC equipment
controls is now available that supports aggregate demand response. This gives
consumers the ability to better control when and to what degree their space
conditioning systems are participating in demand response services provided by
the utility to the bulk system operators.
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6.2
Recommendations
The following avenues of research are recommended based on the results obtained in
this thesis.
1. Develop a method for separately characterizing inter-period (long-term loadshifting) demand response from the intra-period (short-term load-shedding) demand response. The fact that both kinds of demand response exist is well-known.
However there is no rigorous method extant for identifying which part of the
total demand response observed arises from load-shifting behavior and which is
from load-shedding. In addition these are expected to be independent and thus
separate to a first-order. However almost all loads add heat to the indoor air and
thus can both affect and be affected by each other to a potentially significant
degree. This seriously complicates the matter and has limited the development
of methods to resolve the question.
2. Develop a method to quantify the economic performance of real-time price-based
demand response. The existence of linear time-invariant aggregate load control
models at the utility operations level permits the derivation of transfer functions
for energy response to price. These transfer functions can be used to obtain
analytic forms of demand elasticity for the short-term response in both time
domain and frequency domain.
3. Develop an aggregate load control system for utilities to deploy on existing transactive demand response systems. The use of linear time-invariant thermostats
will give rise to linear aggregate demand response control systems that can be
subject to standard feedback control design methods. These linear closed-loop
load control designs may allow utilities to mitigate the stability, observability
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and controllability concerns that have been raised with open-loop load control
strategies [73].
4. Design a slow demand response thermostat control strategy for second-order
thermal systems. The design of fast-acting demand response using a building’s
air heat capacity revealed an opportunity to separately construct a system
for observing and controlling building thermal mass. This slow-acting demand
response can be used to position buildings to respond better to long-term fluctuations in energy prices arising from existing forward energy markets independently
from the short-term fluctuations that would arise from ancillary service markets
when they become more prevalent. A critical element of this approach will be
to establish observability of the second state variable, e.g., the thermal mass
temperature. This can be done either by direct measurement of the mass temperature or estimating it from the derivative of the air temperature, provided
an appropriate adaptive band-limited differentiator can be designed.
5. Design and test a tariff structure that allows technical response loads to be billed
separately from human response loads. There is no economic justification for
forcing consumers to place both technology-response and behavior-response loads
on the price structure. Split tariffs should be considered when the technologydriven responsive loads are available while behavior-driven responsive loads
are subject to regular time-of-use prices that can influence long-term consumer
habits.
6. Automate the tariff design mechanism in GridLAB-D. Analysis of designs such
as the one studied in this thesis require a very time-consuming revenue neutral
tariff design process. The manual method used in this thesis limited the ability
to use tariffs that were well-designed for the study cities and seasons and limited
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the number of studies that could reasonably be tested. Development of an
automated tariff design module in GridLAB-D will significantly augment the
analysis capabilities of GridLAB-D by allowed very wide-ranging scenarios to be
explored very quickly.
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Appendix A
Tables in SI Units
Table 4.1: Cities and climate conditions
City
TMY File
Timezone
Low
High
Solar
(◦ C)
(◦ C)
(W/m2 )
-5
-3
3
37
46
34
1028
1072
1042
Seattle
WA-Seattle.tmy2 PST+8PDT
Phoenix AZ-Phoenix.tmy2
MST+7
Miami
FL-Miami.tmy2
EST+5EDT
Table 4.2: IECC end-use energy for model home in study cities
End-use
Heating
Cooling
Fans
Hotwater
Lighting
Plugs
Total
Seattle
Miami
Phoenix
(MWh/y)
(%)
(MWh/y)
(%)
(MWh/y)
(%)
3.4
0.8
1.0
4.0
1.4
7.7
19
5
5
22
8
42
0.2
6.1
1.5
2.2
1.4
7.7
1
32
8
12
7
40
0.9
6.3
1.9
2.3
1.4
7.7
4
31
9
11
7
37
18.4
100
19.2
100
20.6
100
126
Table 4.3: House design
Parameter
2
Floor area (m )
UA (W/◦ C)
CA (kJ/◦ C)
UM (W/◦ C)
CM (kJ/◦ C)
Seattle
Miami
Phoenix
223
283
1931
5884
7828
223
227
1735
5884
7033
223
227
1735
5884
7033
Table 4.5: Occupancy and thermostat setpoint schedule
Occupancy
Night
Home
Away
Vacation
Weekday
Weekend
20:00−6:00
23:00−7:00
6:00−8:00,18:00−22:00 7:00−23:00
8:00−18:00
−
−
−
Heating
Cooling
Comfort
(◦ C)
(◦ C)
($/◦ C)
20
22
19
−
24
26
27
−
0.56
0.83
0.28
−
Table 4.6: Heatpump design criteria and capacities for study cities
Parameter
TH (◦ C)
TC (◦ C)
QH (kW)
QC (kW)
Seattle
Miami
Phoenix
-5
37
18
18
3
34
16
16
-3
46
19
19
127
Appendix B
Simulation Models
B.1
Common Models
B.1.1
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Demand Response Controllers
// DR e n a b l e d h o u s e
module r e s i d e n t i a l ;
c l a s s house {
d o u b l e k ; // demand r e s p o n s i v e n e s s f a c t o r
d o u b l e o c c u p a n c y ; // s c h e d u l e d o c c u p a n c y
}
module p o w e r f l o w ;
class triplex meter {
d o u b l e o c c u p a n c y ; // s c h e d u l e o c c u p a n c y
}
// Olympic / Columbus RTP t h e r m o s t a t
class rtp thermostat {
o b j e c t market ;
double P;
d o u b l e Pavg ;
d o u b l e Pstd ;
double k ;
double d l c o f f s e t ;
d o u b l e Tout ;
double Tair ;
d o u b l e Theat ;
double Tcool ;
d o u b l e Taux ;
double dTair ;
d o u b l e dTairMin ;
i n t 6 4 mode ;
timestamp t l a s t ; // t i m e o f l a s t mode
i n t 6 4 m l a s t ; // l a s t mode
i n t r i n s i c p r e s y n c (TIMESTAMP t0 , TIMESTAMP t 1 )
{
// r e t a i l p r i c e
g l g e t v a l u e ( (OBJECT∗ ) market , ” p r i c e ” ,P ) ;
g l g e t v a l u e ( (OBJECT∗ ) market , ” p r i c e m e a n ” , Pavg ) ;
g l g e t v a l u e ( (OBJECT∗ ) market , ” p r i c e s t d e v ” , Pstd ) ;
// c o m f o r t s e t t i n g and t e m p e r a t u r e
g l g e t v a l u e (my−>p a r e n t , ” k ” , k ) ;
d l c o f f s e t = k ∗ (P−Pavg ) / Pstd ;
offset
// t h e r m o s t a t i n f o
g l g e t v a l u e (my−>p a r e n t , ” h e a t i n g s e t p o i n t ” , Theat ) ;
g l g e t v a l u e (my−>p a r e n t , ” c o o l i n g s e t p o i n t ” , T c o o l ) ;
g l g e t v a l u e (my−>p a r e n t , ” a i r t e m p e r a t u r e ” , T a i r ) ;
g l g e t v a l u e (my−>p a r e n t , ” o u t d o o r t e m p e r a t u r e ” , Tout ) ;
g l g e t v a l u e (my−>p a r e n t , ” d T a i r ” , d T a i r ) ;
Taux = Theat − 3∗ k ;
t y p e d e f enum {SM OFF=1 , SM HEAT=2 , SM AUX=3 , SM COOL=4} SYSTEMMODE;
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// d i s a b l e house ’ s i n t e r n a l t h e r m o s t a t
t y p e d e f enum {SM NONE=2} THERMOSTATCONTROL;
THERMOSTATCONTROL ∗ p T h e r m o s t a t C o n t r o l = (THERMOSTATCONTROL∗ ) g l g e t a d d r (my−>p a r e n t , ” t h e r m o s t a t c o n t r o l ” ) ;
∗ p T h e r m o s t a t C o n t r o l = SM NONE ;
// compute new s e t p o i n t s
Theat −= d l c o f f s e t ;
T c o o l += d l c o f f s e t ;
// d e t e r m i n e mode
s w i t c h ( (SYSTEMMODE) mode ) {
c a s e SM OFF :
i f ( T a i r<Theat && dTair<−dTairMin )
mode = SM HEAT ;
e l s e i f ( T a i r>T c o o l && dTair>dTairMin )
mode = SM COOL ;
break ;
c a s e SM HEAT :
i f ( T a i r>Theat )
mode = SM OFF ;
e l s e i f ( dTair <0 )
mode = SM AUX ;
break ;
c a s e SM AUX :
i f ( T a i r>Theat )
mode = SM OFF ;
break ;
c a s e SM COOL :
i f ( T a i r<T c o o l )
mode = SM OFF ;
break ;
default :
mode = SM OFF ;
break ;
}
// s e n d mode s i g n a l
SYSTEMMODE ∗pSystemMode = (SYSTEMMODE∗ ) g l g e t a d d r (my−>p a r e n t , ” system mode ” ) ;
i f ( pSystemMode==NULL )
g l e r r o r (” unable to get house o v e r r i d e ” ) ;
else
∗pSystemMode = (SYSTEMMODE) mode ;
// p r i v a t e
logging
r e t u r n TS NEVER ;
};
}
// New t h e r m o s t a t
c l a s s new thermostat {
o b j e c t market ;
double P;
d o u b l e Pavg ;
d o u b l e Pstd ;
double k ;
double d l c o f f s e t ;
d o u b l e Tout ;
double Tair ;
d o u b l e Theat ;
double Tcool ;
d o u b l e Taux ;
double dTair ;
d o u b l e dTairMin ;
i n t 6 4 mode ;
timestamp t l a s t ; // t i m e o f l a s t mode
i n t 6 4 m l a s t ; // l a s t mode
i n t r i n s i c p r e s y n c (TIMESTAMP t0 , TIMESTAMP t 1 )
{
// n e x t e v e n t t i m e
TIMESTAMP t 2 = ( ( t 1 / 3 0 0 ) + 1 ) ∗ 3 0 0 ;
// r e t a i l p r i c e
g l g e t v a l u e ( (OBJECT∗ ) market , ” p r i c e ” ,P ) ;
g l g e t v a l u e ( (OBJECT∗ ) market , ” p r i c e m e a n ” , Pavg ) ;
g l g e t v a l u e ( (OBJECT∗ ) market , ” p r i c e s t d e v ” , Pstd ) ;
// c o m f o r t s e t t i n g and t e m p e r a t u r e
g l g e t v a l u e (my−>p a r e n t , ” k ” , k ) ;
d l c o f f s e t = k ∗ (P−Pavg ) / Pstd ;
offset
// t h e r m o s t a t i n f o
g l g e t v a l u e (my−>p a r e n t , ” h e a t i n g s e t p o i n t ” , Theat ) ;
g l g e t v a l u e (my−>p a r e n t , ” c o o l i n g s e t p o i n t ” , T c o o l ) ;
g l g e t v a l u e (my−>p a r e n t , ” a i r t e m p e r a t u r e ” , T a i r ) ;
g l g e t v a l u e (my−>p a r e n t , ” o u t d o o r t e m p e r a t u r e ” , Tout ) ;
g l g e t v a l u e (my−>p a r e n t , ” d T a i r ” , d T a i r ) ;
Taux = Theat − 3∗ k ;
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// u p d a t e HVAC s t a t e
i f ( t 1 %300 == 0 ) // t i m e t o u p d a t e
{
t y p e d e f enum {SM OFF=1 , SM HEAT=2 , SM AUX=3 , SM COOL=4} SYSTEMMODE;
// d i s a b l e house ’ s i n t e r n a l t h e r m o s t a t
t y p e d e f enum {SM NONE=2} THERMOSTATCONTROL;
THERMOSTATCONTROL ∗ p T h e r m o s t a t C o n t r o l = (THERMOSTATCONTROL∗ ) g l g e t a d d r (my−>p a r e n t , ” t h e r m o s
∗ p T h e r m o s t a t C o n t r o l = SM NONE ;
// compute new s e t p o i n t s
Theat −= d l c o f f s e t ;
T c o o l += d l c o f f s e t ;
// d e t e r m i n e mode
s w i t c h ( (SYSTEMMODE) mode ) {
c a s e SM OFF :
i f ( T a i r<Theat && dTair<−dTairMin )
mode = SM HEAT ;
e l s e i f ( T a i r>T c o o l && dTair>dTairMin )
mode = SM COOL ;
break ;
c a s e SM HEAT :
i f ( T a i r>Theat )
mode = SM OFF ;
e l s e i f ( dTair <0 )
mode = SM AUX ;
break ;
c a s e SM AUX :
i f ( T a i r>Theat )
mode = SM OFF ;
break ;
c a s e SM COOL :
i f ( T a i r<T c o o l )
mode = SM OFF ;
break ;
default :
mode = SM OFF ;
break ;
}
// s e n d mode s i g n a l
SYSTEMMODE ∗pSystemMode = (SYSTEMMODE∗ ) g l g e t a d d r (my−>p a r e n t , ” system mode ” ) ;
i f ( pSystemMode==NULL )
g l e r r o r (” unable to get house o v e r r i d e ” ) ;
else
∗pSystemMode = (SYSTEMMODE) mode ;
// p r i v a t e
}
return
}
Market Model
// market c l a s s
c l a s s market {
randomvar p r i c e [ $ /kWh ] ;
d o u b l e p r i c e m e a n [ $ /kWh ] ;
d o u b l e p r i c e s t d e v [ $ /kWh ] ;
}
B.1.3
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
t2 ;
};
B.1.2
1
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3
4
5
6
logging
Occupancy Schedules
// b a s e l i n e h e a t i n g and c o o l i n g
#d e f i n e TH NIGHT=68
#d e f i n e TH HOME=72
#d e f i n e TH AWAY=66
#d e f i n e TC NIGHT=76
#d e f i n e TC HOME=78
#d e f i n e TC AWAY=80
setpoints
// TOU
#d e f i n e
#d e f i n e
#d e f i n e
#d e f i n e
prices
WINTER OFFPEAK PRICE=0.0540
WINTER ONPEAK PRICE=0.1153
SUMMER OFFPEAK PRICE=0.0540
SUMMER ONPEAK PRICE=0.1381
// TOU
#d e f i n e
#d e f i n e
#d e f i n e
h e a t i n g and c o o l i n g
TH OFFPEAK=72
TH ONPEAK=70
TC OFFPEAK=80
setpoints
( no r e s p o n s e )
(TOU r e s p o n s e )
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#d e f i n e TC ONPEAK=78
// RTP
#d e f i n e
#d e f i n e
#d e f i n e
comfort s e t t t i n g s
K NIGHT=1.00
K HOME=0.67
K AWAY=2.00
// f i x e d s c h e d u l e s
s c h e d u l e occupancy {
∗ 22−5 ∗ ∗ 1−5
1 # n i g h t weekday
∗ 6 −8 ,18 −21 ∗ ∗ 1−5
2 # home weekday
∗ 9−17 ∗ ∗ 1−5
3 # away weekday
∗ 23−6 ∗ ∗ 6−0
1 # n i g h t weekend
∗ 7−22 ∗ ∗ 6−0
2 # home weekend
}
schedule heating setpoint {
∗ 22−5 ∗ ∗ 1−5
$ {TH NIGHT} # n i g h t weekday
∗ 6 −8 ,18 −21 ∗ ∗ 1−5
$ {TH HOME} # home weekday
∗ 9−17 ∗ ∗ 1−5
$ {TH AWAY} # away weekday
∗ 23−6 ∗ ∗ 6−0
$ {TH NIGHT} # n i g h t weekend
∗ 7−22 ∗ ∗ 6−0
$ {TH HOME} # home weekend
}
schedule cooling setpoint {
∗ 22−5 ∗ ∗ 1−5
$ {TC NIGHT} # n i g h t weekday
∗ 6 −8 ,18 −21 ∗ ∗ 1−5
$ {TC HOME} # home weekday
∗ 9−17 ∗ ∗ 1−5
$ {TC AWAY} # away weekday
∗ 23−6 ∗ ∗ 6−0
$ {TC NIGHT} # n i g h t weekend
∗ 7−22 ∗ ∗ 6−0
$ {TC HOME} # home weekend
}
// TOU s c h e d u l e s
schedule t o u t a r i f f {
∗ 9 −17 ,21 −5 ∗ 10−6 ∗ $ {WINTER OFFPEAK PRICE} # w i n t e r o f f p e a k
∗ 6 −8 ,18 −20 ∗ 10−6 ∗ $ {WINTER ONPEAK PRICE} # w i n t e r onpeak
∗ 21−14 ∗ 7−9 ∗
$ {SUMMER OFFPEAK PRICE} # summer o f f p e a k
∗ 15−20 ∗ 7−9 ∗
$ {SUMMER ONPEAK PRICE} # summer onpeak
}
schedule heating setback
∗ 9 −17 ,21 −5 ∗ 10−6 ∗
∗ 6 −8 ,18 −20 ∗ 10−6 ∗
∗ 21−14 ∗ 7−9 ∗
∗ 15−20 ∗ 7−9 ∗
}
schedule cooling setback
∗ 9 −17 ,21 −5 ∗ 10−6 ∗
∗ 6 −8 ,18 −20 ∗ 10−6 ∗
∗ 21−14 ∗ 7−9 ∗
∗ 15−20 ∗ 7−9 ∗
}
// RTP s c h e d u l e s
schedule comfort {
∗ 22−5 ∗ ∗ 1−5
∗ 6 −8 ,18 −21 ∗ ∗ 1−5
∗ 9−17 ∗ ∗ 1−5
∗ 23−6 ∗ ∗ 6−0
∗ 7−22 ∗ ∗ 6−0
}
B.1.4
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(RTP e s p o n s e )
{
$ {TH OFFPEAK} # w i n t e r o f f p e a k
$ {TH ONPEAK} # w i n t e r onpeak
$ {TH OFFPEAK} # summer o f f p e a k
$ {TH ONPEAK} # summer onpeak
{
$ {TC OFFPEAK} # w i n t e r o f f p e a k
$ {TC ONPEAK} # w i n t e r onpeak
$ {TC OFFPEAK} # summer onpeak
$ {TC ONPEAK} # summer o f f p e a k
$ {K NIGHT} # n i g h t weekday
$ {K HOME} # home weekday
$ {K AWAY} # away weekday
$ {K NIGHT} # n i g h t weekend
$ {K HOME} # home weekend
End-use Load Monitoring
// e n d u s e m o n i t o r i n g . h
//
// Header f o r e n d u s e m o n i t o r i n g
#i f n d e f
#d e f i n e
class
ENDUSEMONITORING H
ENDUSEMONITORING H
t y p e d e f enum {
BRK OPEN=0 ,
BRK CLOSED=1 ,
///<
BRK FAULT=−1,
///<
} BREAKERSTATUS; ///< b r e a k e r s t a t e
t y p e d e f enum {
X12=0 ,
///< c i r c u i t
X23=1 ,
///< c i r c u i t
X13=2 ,
///< c i r c u i t
} CIRCUITTYPE ; ///< c i r c u i t t y p e
///< b r e a k e r open
breaker closed
breaker faulted
from
from
from
l i n e 1 to
l i n e 2 to
l i n e 1 to
line 2
( 2 4 0V)
l i n e 3 (N) ( 1 2 0V)
l i n e 3 (N) ( 1 2 0V)
typedef struct s c i r c u i t {
CIRCUITTYPE t y p e ;
///< c i r c u i t t y p e
s t r u c t s e n d u s e ∗ pLoad ;
///< p o i n t e r t o t h e l o a d s t r u c t
complex ∗pV ; ///< p o i n t e r t o c i r c u i t v o l t a g e
d o u b l e max amps ; ///< maximum b r e a k e r amps
i n t i d ; ///< c i r c u i t i d
BREAKERSTATUS s t a t u s ; ///< b r e a k e r s t a t u s
TIMESTAMP r e c l o s e ; ///< t i m e a t which b r e a k e r i s r e c l o s e d
(ENDUSELOAD∗ i n
house a ,
enduse ∗ in
house e )
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u n s i g n e d s h o r t t r i p s l e f t ; ///< t h e number o f
s t r u c t s c i r c u i t ∗ n e x t ; ///< n e x t c i r c u i t i n
// DPC: commented t h i s o u t u n t i l t h e r e s t o f
} CIRCUIT ; ///< c i r c u i t d e f i n i t i o n
trips left
list
house e i s
typedef struct s panel {
d o u b l e max amps ; ///< maximum p a n e l amps
BREAKERSTATUS s t a t u s ; ///< p a n e l b r e a k e r s t a t u s
TIMESTAMP r e c l o s e ; ///< t i m e a t which b r e a k e r i s
CIRCUIT ∗ c i r c u i t s ; ///< p o i n t e r t o f i r s t c i r c u i t
} PANEL ;
before
breaker
faults
updated
reclosed
in c i r c u i t
list
#e n d i f
// e n d u s e m o n i t o r i n g . glm
//
// S u p p o r t s e n d u s e m o n i t o r i n g by i n s p e c t i n g
the house panel
circuits
c l a s s enduse monitor {
char1024 filename ;
i n t r i n s i c create ( object parent )
{
s p r i n t f ( f i l e n a m e , ” e n d u s e %d . c s v ” ,my−>i d ) ;
r e t u r n SUCCESS ;
};
i n t r i n s i c i n i t ( object parent )
{
OBJECT ∗ w e a t h e r = (OBJECT∗ ) g l g e t a d d r (my−>p a r e n t , ” w e a t h e r ” ) ;
PANEL ∗ p a n e l = (PANEL∗ ) ( ( c h a r ∗ ) w e a t h e r+ s i z e o f (OBJECT ∗ ) ) ;
CIRCUIT ∗ c i r c u i t ;
i f ( p a r e n t==NULL | | s t r c m p ( p a r e n t −>o c l a s s −>name , ” h o u s e ” ) ! = 0 )
{
g l e r r o r (” parent i s not a house ” ) ;
r e t u r n FAILED ;
}
f p = f o p e n ( f i l e n a m e , ”w ” ) ;
i f ( f p==NULL )
{
g l e r r o r ( ” u n a b l e t o open e n d u s e m o n i t o r i n g o u t p u t f i l e ’% s ’ ” , f i l e n a m e ) ;
r e t u r n FAILED ;
}
f p r i n t f ( f p ,”# f i l e . . . . . . %s \n# d a t e . . . . . . \n# u s e r . . . . . . \n# h o s t . . . . . . \n# t a r g e t . . . .
”# t r i g g e r . . . \n# i n t e r v a l . . 3600\ n# l i m i t . . . . . \n# timestamp ” , f i l e n a m e ) ;
f o r ( c i r c u i t =p a n e l −> c i r c u i t s ; c i r c u i t !=NULL ; c i r c u i t = c i r c u i t −>n e x t )
{
e n d u s e ∗ l o a d = c i r c u i t −>pLoad ;
f p r i n t f ( f p , ” , % s ” , l o a d −>name ) ;
l o a d −>e n e r g y = 0 ;
}
f p r i n t f ( f p ,”% s ” , ” \ n ” ) ;
r e t u r n SUCCESS ;
};
i n t r i n s i c commit (TIMESTAMP t0 , TIMESTAMP t 1 )
{
i f ( t 1 %3600==0 )
{
// p a n e l i s n o t p u b l i s h e d but i t f o l l o w s w e a t h e r , which i s p u b l i s h e d
OBJECT ∗ w e a t h e r = (OBJECT∗ ) g l g e t a d d r (my−>p a r e n t , ” w e a t h e r ” ) ;
c h a r b u f f e r [ 2 5 6 ] = ” INVALID ” ;
PANEL ∗ p a n e l = (PANEL∗ ) ( ( c h a r ∗ ) w e a t h e r+ s i z e o f (OBJECT ∗ ) ) ;
CIRCUIT ∗ c i r c u i t ;
s t r u c t tm ∗ t s=l o c a l t i m e (& t 1 ) ;
f p r i n t f ( f p ,”%04d−%02d−%02d %02d :%02 d :%02 d PST” ,
t s −>t m y e a r +1900 , t s −>tm mon+1 , t s −>tm mday ,
t s −>tm hour , t s −>tm min , t s −>t m s e c
);
f o r ( c i r c u i t =p a n e l −> c i r c u i t s ; c i r c u i t !=NULL ; c i r c u i t = c i r c u i t −>n e x t )
{
e n d u s e ∗ l o a d = c i r c u i t −>pLoad ;
d o u b l e d i f f = l o a d −>e n e r g y . Re ( ) − e n e r g y [ c i r c u i t −>i d ] ;
f p r i n t f ( fp ,” ,% g ” , d i f f ) ;
e n e r g y [ c i r c u i t −>i d ] = l o a d −>e n e r g y . Re ( ) ;
}
f p r i n t f ( f p ,”% s ” , ” \ n ” ) ;
}
return 3600∗(( t1 /3600)+1);
};
i n t r i n s i c f i n a l i z e ()
{
f c l o s e ( fp ) ;
r e t u r n SUCCESS ;
};
p r i v a t e FILE ∗ f p ;
p r i v a t e double energy [ 3 2 ] ;
}
\n”
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B.2
Study Models
B.2.1
Seattle Winter
// g e n e r a t e d by Matlab c l a s s G r i d l a b d on 30−Oct −2014 0 9 : 0 6 : 3 6
#s e t tmp=.
#i n c l u d e <C: \ U s e r s \ d c h a s s i n \ Desktop \ Dropbox \UVic\ C o u r s e s \MECH599\ T h e s i s \ e v a l u a t i o n \ matlab \ e n d u s e m o n i t o r . h>
#i n c l u d e ”C: \ U s e r s \ d c h a s s i n \ Desktop \ Dropbox \UVic\ C o u r s e s \MECH599\ T h e s i s \ e v a l u a t i o n \ matlab \ e n d u s e m o n i t o r . glm ”
#i n c l u d e ”C: \ U s e r s \ d c h a s s i n \ Desktop \ Dropbox \UVic\ C o u r s e s \MECH599\ T h e s i s \ e v a l u a t i o n \ matlab \ s c h e d u l e s . glm ”
#i n c l u d e ”C: \ U s e r s \ d c h a s s i n \ Desktop \ Dropbox \UVic\ C o u r s e s \MECH599\ T h e s i s \ e v a l u a t i o n \ matlab \ h o u s e . glm ”
#i n c l u d e ”C: \ U s e r s \ d c h a s s i n \ Desktop \ Dropbox \UVic\ C o u r s e s \MECH599\ T h e s i s \ e v a l u a t i o n \ matlab \ market . glm ”
clock {
t i m e z o n e PST+8PDT;
s t a r t t i m e ’2014 −01 −01 0 0 : 0 0 : 0 0 ’ ;
s t o p t i m e ’2014 −01 −29 0 0 : 0 0 : 0 0 ’ ;
}
module c l i m a t e ;
module p o w e r f l o w {
market price name ” p r i c e ”;
}
module t a p e ;
object climate {
name ” w e a t h e r ” ;
t m y f i l e ”WA−S e a t t l e . tmy2 ” ;
i n t e r p o l a t e ”QUADRATIC” ;
}
object triplex meter {
name ” f i x e d m e t e r ” ;
p h a s e s ”SA ” ;
b i l l m o d e ”UNIFORM” ;
bill day 1;
p r i c e ” 8 1 . 0 0 0 0 $ /MWh” ;
occupancy occupancy ;
nominal voltage 120;
}
o b j e c t house {
name ” f i x e d h o u s e ” ;
parent ” fixed meter ”;
f l o o r a r e a 2400;
t h e r m a l i n t e g r i t y l e v e l ”GOOD” ;
number of stories 2;
heating setpoint heating setpoint ;
cooling setpoint cooling setpoint ;
design heating capacity 0;
design cooling capacity 0;
air temperature 75;
occupancy occupancy ;
thermostat deadband 1 ;
}
object recorder {
parent ” fixed house ”;
property ” outdoor temperature , air temperature , heating setpoint , c o o l i n g s e t p o i n t , adj heating cap , adj cooling
interval 60;
f i l e ” f i x e d h o u s e . csv ”;
}
object recorder {
parent ” fixed meter ”;
p r o p e r t y ” m e a s u r e d r e a l e n e r g y , p r i c e , m o n t h l y b i l l , occupancy ” ;
i n t e r v a l 3600;
f i l e ” fixed meter . csv ”;
}
object enduse monitor {
parent ” fixed house ”;
filename ” fixed enduse . csv ”;
}
object recorder {
parent ” fixed house ”;
p r o p e r t y ” envelope UA , f l o o r a r e a , UA, a i r h e a t c a p a c i t y , m a s s h e a t c o e f f , m a s s h e a t c a p a c i t y , d e s i g n h e a t i n g c a p
i n t e r v a l ” −1”;
limit 1;
f i l e ” design . csv ”;
}
object triplex meter {
name ” t o u m e t e r ” ;
p h a s e s ”SA ” ;
b i l l m o d e ”UNIFORM” ;
bill day 1;
price tou tariff ;
occupancy occupancy ;
nominal voltage 120;
}
o b j e c t house {
name ” t o u h o u s e ” ;
parent ” tou meter ”;
f l o o r a r e a 2400;
t h e r m a l i n t e g r i t y l e v e l ”GOOD” ;
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number of stories 2;
heating setpoint heating setpoint ;
cooling setpoint cooling setpoint ;
design heating capacity 0;
design cooling capacity 0;
air temperature 75;
occupancy occupancy ;
thermostat deadband 1 ;
}
object
}
object
}
object
}
object
}
object
recorder {
parent ” tou meter ”;
property ” measured real energy , price , monthly bill ”;
i n t e r v a l 3600;
f i l e ” tou meter . csv ”;
triplex meter {
name ” t o u m e t e r d r ” ;
p h a s e s ”SA ” ;
b i l l m o d e ”UNIFORM” ;
bill day 1;
price tou tariff ;
occupancy occupancy ;
nominal voltage 120;
house {
name ” t o u h o u s e d r ” ;
parent ” tou meter dr ”;
f l o o r a r e a 2400;
t h e r m a l i n t e g r i t y l e v e l ”GOOD” ;
number of stories 2;
heating setpoint heating setback ;
cooling setpoint cooling setback ;
design heating capacity 0;
design cooling capacity 0;
air temperature 75;
occupancy occupancy ;
thermostat deadband 1 ;
recorder {
parent ” tou meter dr ”;
property ” measured real energy , price , monthly bill ”;
i n t e r v a l 3600;
f i l e ” tou meter dr . csv ”;
recorder {
parent ” tou house dr ”;
property ” outdoor temperature , air temperature , heating setpoint , c o o l i n g s e t p o i n t , adj heating cap , adj cooling
interval 60;
f i l e ” tou house . csv ”;
}
o b j e c t market {
name ” r e t a i l ” ;
p r i c e ” t y p e : normal ( 0 . 0 8 0 8 4 0 , 0 . 0 0 9 9 8 4 ) ; r e f r e s h : 5 min ; min : 0 ; max : 9 9 9 ” ;
price mean 0.08084;
price stdev 0.00998374;
}
object triplex meter {
name ” r t p m e t e r ” ;
p h a s e s ”SA ” ;
b i l l m o d e ”HOURLY” ;
bill day 1;
power market ” r e t a i l ” ;
occupancy occupancy ;
nominal voltage 120;
}
o b j e c t house {
name ” r t p h o u s e ” ;
parent ” rtp meter ”;
f l o o r a r e a 2400;
t h e r m a l i n t e g r i t y l e v e l ”GOOD” ;
number of stories 2;
heating setpoint heating setpoint ;
cooling setpoint cooling setpoint ;
design heating capacity 0;
design cooling capacity 0;
air temperature 75;
occupancy occupancy ;
thermostat deadband 1 ;
}
object recorder {
parent ” rtp meter ”;
p r o p e r t y ” m e a s u r e d r e a l e n e r g y , p r i c e , m o n t h l y b i l l , occupancy ” ;
interval 300;
f i l e ” rtp meter . csv ”;
}
object triplex meter {
name ” r t p m e t e r d r ” ;
p h a s e s ”SA ” ;
b i l l m o d e ”HOURLY” ;
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bill day 1;
power market ” r e t a i l ” ;
occupancy occupancy ;
nominal voltage 120;
}
object
}
object
}
object
}
object
}
object
}
object
}
object
}
object
}
object
}
object
house {
name ” r t p h o u s e d r ” ;
parent ” rtp meter dr ”;
f l o o r a r e a 2400;
t h e r m a l i n t e g r i t y l e v e l ”GOOD” ;
number of stories 2;
heating setpoint heating setpoint ;
cooling setpoint cooling setpoint ;
design heating capacity 0;
design cooling capacity 0;
air temperature 75;
thermostat deadband 0 . 0 1 ;
d l c o f f s e t 0;
k comfort ;
occupancy occupancy ;
o v e r r i d e ”OFF” ;
rtp thermostat {
name ” r t p t s t a t ” ;
parent ” rtp house dr ”;
market ” r e t a i l ” ;
recorder {
parent ” r t p t s t a t ”;
p r o p e r t y ”P , Pavg , Pstd , k , d l c o f f s e t , Tout , T a i r , Theat , Tcool , Taux , dTair , dTairMin , mode ” ;
interval 300;
f i l e ” r t p t s t a t . csv ”;
recorder {
parent ” rtp meter dr ”;
p r o p e r t y ” m e a s u r e d r e a l e n e r g y , p r i c e , m o n t h l y b i l l , occupancy ” ;
interval 300;
f i l e ” rtp meter dr . csv ”;
recorder {
parent ” rtp house dr ”;
property ” outdoor temperature , air temperature , heating setpoint , c o o l i n g s e t p o i n t , adj heating cap , adj cooling
interval 60;
f i l e ” rtp house . csv ”;
triplex meter {
name ” n e w m e t e r ” ;
p h a s e s ”SA ” ;
b i l l m o d e ”HOURLY” ;
bill day 1;
power market ” r e t a i l ” ;
occupancy occupancy ;
nominal voltage 120;
house {
name ” n e w h o u s e ” ;
p a r e n t ” new meter ” ;
f l o o r a r e a 2400;
t h e r m a l i n t e g r i t y l e v e l ”GOOD” ;
number of stories 2;
heating setpoint heating setpoint ;
cooling setpoint cooling setpoint ;
design heating capacity 0;
design cooling capacity 0;
air temperature 75;
occupancy occupancy ;
thermostat deadband 1 ;
recorder {
p a r e n t ” new meter ” ;
p r o p e r t y ” m e a s u r e d r e a l e n e r g y , p r i c e , m o n t h l y b i l l , occupancy ” ;
interval 300;
f i l e ” new meter . csv ” ;
triplex meter {
name ” n e w m e t e r d r ” ;
p h a s e s ”SA ” ;
b i l l m o d e ”HOURLY” ;
bill day 1;
power market ” r e t a i l ” ;
occupancy occupancy ;
nominal voltage 120;
house {
name ” n e w h o u s e d r ” ;
parent ” new meter dr ” ;
f l o o r a r e a 2400;
t h e r m a l i n t e g r i t y l e v e l ”GOOD” ;
number of stories 2;
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heating setpoint heating setpoint ;
cooling setpoint cooling setpoint ;
design heating capacity 0;
design cooling capacity 0;
air temperature 75;
thermostat deadband 0 . 0 1 ;
d l c o f f s e t 0;
k comfort ;
occupancy occupancy ;
o v e r r i d e ”OFF” ;
}
object
}
object
}
object
}
object
new thermostat {
name ” n e w t s t a t ” ;
parent ” new house dr ” ;
market ” r e t a i l ” ;
recorder {
parent ” new tstat ”;
p r o p e r t y ”P , Pavg , Pstd , k , d l c o f f s e t , Tout , T a i r , Theat , Tcool , Taux , dTair , dTairMin , mode ” ;
interval 300;
f i l e ” new tstat . csv ”;
recorder {
parent ” new meter dr ” ;
p r o p e r t y ” m e a s u r e d r e a l e n e r g y , p r i c e , m o n t h l y b i l l , occupancy ” ;
interval 300;
f i l e ” new meter dr . csv ” ;
recorder {
parent ” new house dr ” ;
property ” outdoor temperature , air temperature , heating setpoint , c o o l i n g s e t p o i n t , adj heating cap , adj cooling
interval 60;
f i l e ” new house . csv ” ;
}
B.2.2
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Seattle Summer
// g e n e r a t e d by Matlab c l a s s G r i d l a b d on 30−Oct −2014 0 9 : 0 6 : 5 2
#s e t tmp=.
#i n c l u d e <C: \ U s e r s \ d c h a s s i n \ Desktop \ Dropbox \UVic\ C o u r s e s \MECH599\ T h e s i s \ e v a l u a t i o n \ matlab \ e n d u s e m o n i t o r . h>
#i n c l u d e ”C: \ U s e r s \ d c h a s s i n \ Desktop \ Dropbox \UVic\ C o u r s e s \MECH599\ T h e s i s \ e v a l u a t i o n \ matlab \ e n d u s e m o n i t o r . glm ”
#i n c l u d e ”C: \ U s e r s \ d c h a s s i n \ Desktop \ Dropbox \UVic\ C o u r s e s \MECH599\ T h e s i s \ e v a l u a t i o n \ matlab \ s c h e d u l e s . glm ”
#i n c l u d e ”C: \ U s e r s \ d c h a s s i n \ Desktop \ Dropbox \UVic\ C o u r s e s \MECH599\ T h e s i s \ e v a l u a t i o n \ matlab \ h o u s e . glm ”
#i n c l u d e ”C: \ U s e r s \ d c h a s s i n \ Desktop \ Dropbox \UVic\ C o u r s e s \MECH599\ T h e s i s \ e v a l u a t i o n \ matlab \ market . glm ”
clock {
t i m e z o n e PST+8PDT;
s t a r t t i m e ’2014 −07 −01 0 0 : 0 0 : 0 0 ’ ;
s t o p t i m e ’2014 −07 −29 0 0 : 0 0 : 0 0 ’ ;
}
module c l i m a t e ;
module p o w e r f l o w {
market price name ” p r i c e ”;
}
module t a p e ;
object climate {
name ” w e a t h e r ” ;
t m y f i l e ”WA−S e a t t l e . tmy2 ” ;
i n t e r p o l a t e ”QUADRATIC” ;
}
object triplex meter {
name ” f i x e d m e t e r ” ;
p h a s e s ”SA ” ;
b i l l m o d e ”UNIFORM” ;
bill day 1;
p r i c e ” 8 1 . 0 0 0 0 $ /MWh” ;
occupancy occupancy ;
nominal voltage 120;
}
o b j e c t house {
name ” f i x e d h o u s e ” ;
parent ” fixed meter ”;
f l o o r a r e a 2400;
t h e r m a l i n t e g r i t y l e v e l ”GOOD” ;
number of stories 2;
heating setpoint heating setpoint ;
cooling setpoint cooling setpoint ;
design heating capacity 0;
design cooling capacity 0;
air temperature 75;
occupancy occupancy ;
thermostat deadband 1 ;
}
object recorder {
parent ” fixed house ”;
property ” outdoor temperature , air temperature , heating setpoint , c o o l i n g s e t p o i n t , adj heating cap , adj cooling
interval 60;
f i l e ” f i x e d h o u s e . csv ”;
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}
object
}
object
}
object
}
object
}
object
}
object
}
object
}
object
}
object
}
object
recorder {
parent ” fixed meter ”;
p r o p e r t y ” m e a s u r e d r e a l e n e r g y , p r i c e , m o n t h l y b i l l , occupancy ” ;
i n t e r v a l 3600;
f i l e ” fixed meter . csv ”;
enduse monitor {
parent ” fixed house ”;
filename ” fixed enduse . csv ”;
recorder {
parent ” fixed house ”;
p r o p e r t y ” envelope UA , f l o o r a r e a , UA, a i r h e a t c a p a c i t y , m a s s h e a t c o e f f , m a s s h e a t c a p a c i t y , d e s i g n h e a t i n g c a p
i n t e r v a l ” −1”;
limit 1;
f i l e ” design . csv ”;
triplex meter {
name ” t o u m e t e r ” ;
p h a s e s ”SA ” ;
b i l l m o d e ”UNIFORM” ;
bill day 1;
price tou tariff ;
occupancy occupancy ;
nominal voltage 120;
house {
name ” t o u h o u s e ” ;
parent ” tou meter ”;
f l o o r a r e a 2400;
t h e r m a l i n t e g r i t y l e v e l ”GOOD” ;
number of stories 2;
heating setpoint heating setpoint ;
cooling setpoint cooling setpoint ;
design heating capacity 0;
design cooling capacity 0;
air temperature 75;
occupancy occupancy ;
thermostat deadband 1 ;
recorder {
parent ” tou meter ”;
property ” measured real energy , price , monthly bill ”;
i n t e r v a l 3600;
f i l e ” tou meter . csv ”;
triplex meter {
name ” t o u m e t e r d r ” ;
p h a s e s ”SA ” ;
b i l l m o d e ”UNIFORM” ;
bill day 1;
price tou tariff ;
occupancy occupancy ;
nominal voltage 120;
house {
name ” t o u h o u s e d r ” ;
parent ” tou meter dr ”;
f l o o r a r e a 2400;
t h e r m a l i n t e g r i t y l e v e l ”GOOD” ;
number of stories 2;
heating setpoint heating setback ;
cooling setpoint cooling setback ;
design heating capacity 0;
design cooling capacity 0;
air temperature 75;
occupancy occupancy ;
thermostat deadband 1 ;
recorder {
parent ” tou meter dr ”;
property ” measured real energy , price , monthly bill ”;
i n t e r v a l 3600;
f i l e ” tou meter dr . csv ”;
recorder {
parent ” tou house dr ”;
property ” outdoor temperature , air temperature , heating setpoint , c o o l i n g s e t p o i n t , adj heating cap , adj cooling
interval 60;
f i l e ” tou house . csv ”;
}
o b j e c t market {
name ” r e t a i l ” ;
p r i c e ” t y p e : normal ( 0 . 0 8 0 8 4 0 , 0 . 0 0 9 9 8 4 ) ;
price mean 0.08084;
price stdev 0.00998374;
}
object triplex meter {
r e f r e s h : 5 min ; min : 0 ; max : 9 9 9 ” ;
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name ” r t p m e t e r ” ;
p h a s e s ”SA ” ;
b i l l m o d e ”HOURLY” ;
bill day 1;
power market ” r e t a i l ” ;
occupancy occupancy ;
nominal voltage 120;
}
object
}
object
}
object
}
object
}
object
}
object
}
object
}
object
}
object
}
object
house {
name ” r t p h o u s e ” ;
parent ” rtp meter ”;
f l o o r a r e a 2400;
t h e r m a l i n t e g r i t y l e v e l ”GOOD” ;
number of stories 2;
heating setpoint heating setpoint ;
cooling setpoint cooling setpoint ;
design heating capacity 0;
design cooling capacity 0;
air temperature 75;
occupancy occupancy ;
thermostat deadband 1 ;
recorder {
parent ” rtp meter ”;
p r o p e r t y ” m e a s u r e d r e a l e n e r g y , p r i c e , m o n t h l y b i l l , occupancy ” ;
interval 300;
f i l e ” rtp meter . csv ”;
triplex meter {
name ” r t p m e t e r d r ” ;
p h a s e s ”SA ” ;
b i l l m o d e ”HOURLY” ;
bill day 1;
power market ” r e t a i l ” ;
occupancy occupancy ;
nominal voltage 120;
house {
name ” r t p h o u s e d r ” ;
parent ” rtp meter dr ”;
f l o o r a r e a 2400;
t h e r m a l i n t e g r i t y l e v e l ”GOOD” ;
number of stories 2;
heating setpoint heating setpoint ;
cooling setpoint cooling setpoint ;
design heating capacity 0;
design cooling capacity 0;
air temperature 75;
thermostat deadband 0 . 0 1 ;
d l c o f f s e t 0;
k comfort ;
occupancy occupancy ;
o v e r r i d e ”OFF” ;
rtp thermostat {
name ” r t p t s t a t ” ;
parent ” rtp house dr ”;
market ” r e t a i l ” ;
recorder {
parent ” r t p t s t a t ”;
p r o p e r t y ”P , Pavg , Pstd , k , d l c o f f s e t , Tout , T a i r , Theat , Tcool , Taux , dTair , dTairMin , mode ” ;
interval 300;
f i l e ” r t p t s t a t . csv ”;
recorder {
parent ” rtp meter dr ”;
p r o p e r t y ” m e a s u r e d r e a l e n e r g y , p r i c e , m o n t h l y b i l l , occupancy ” ;
interval 300;
f i l e ” rtp meter dr . csv ”;
recorder {
parent ” rtp house dr ”;
property ” outdoor temperature , air temperature , heating setpoint , c o o l i n g s e t p o i n t , adj heating cap , adj cooling
interval 60;
f i l e ” rtp house . csv ”;
triplex meter {
name ” n e w m e t e r ” ;
p h a s e s ”SA ” ;
b i l l m o d e ”HOURLY” ;
bill day 1;
power market ” r e t a i l ” ;
occupancy occupancy ;
nominal voltage 120;
house {
name ” n e w h o u s e ” ;
p a r e n t ” new meter ” ;
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f l o o r a r e a 2400;
t h e r m a l i n t e g r i t y l e v e l ”GOOD” ;
number of stories 2;
heating setpoint heating setpoint ;
cooling setpoint cooling setpoint ;
design heating capacity 0;
design cooling capacity 0;
air temperature 75;
occupancy occupancy ;
thermostat deadband 1 ;
}
object
}
object
}
object
}
object
}
object
}
object
}
object
recorder {
p a r e n t ” new meter ” ;
p r o p e r t y ” m e a s u r e d r e a l e n e r g y , p r i c e , m o n t h l y b i l l , occupancy ” ;
interval 300;
f i l e ” new meter . csv ” ;
triplex meter {
name ” n e w m e t e r d r ” ;
p h a s e s ”SA ” ;
b i l l m o d e ”HOURLY” ;
bill day 1;
power market ” r e t a i l ” ;
occupancy occupancy ;
nominal voltage 120;
house {
name ” n e w h o u s e d r ” ;
parent ” new meter dr ” ;
f l o o r a r e a 2400;
t h e r m a l i n t e g r i t y l e v e l ”GOOD” ;
number of stories 2;
heating setpoint heating setpoint ;
cooling setpoint cooling setpoint ;
design heating capacity 0;
design cooling capacity 0;
air temperature 75;
thermostat deadband 0 . 0 1 ;
d l c o f f s e t 0;
k comfort ;
occupancy occupancy ;
o v e r r i d e ”OFF” ;
new thermostat {
name ” n e w t s t a t ” ;
parent ” new house dr ” ;
market ” r e t a i l ” ;
recorder {
parent ” new tstat ”;
p r o p e r t y ”P , Pavg , Pstd , k , d l c o f f s e t , Tout , T a i r , Theat , Tcool , Taux , dTair , dTairMin , mode ” ;
interval 300;
f i l e ” new tstat . csv ”;
recorder {
parent ” new meter dr ” ;
p r o p e r t y ” m e a s u r e d r e a l e n e r g y , p r i c e , m o n t h l y b i l l , occupancy ” ;
interval 300;
f i l e ” new meter dr . csv ” ;
recorder {
parent ” new house dr ” ;
property ” outdoor temperature , air temperature , heating setpoint , c o o l i n g s e t p o i n t , adj heating cap , adj cooling
interval 60;
f i l e ” new house . csv ” ;
}
B.2.3
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Phoenix Summer
// g e n e r a t e d by Matlab c l a s s G r i d l a b d on 30−Oct −2014 0 9 : 0 7 : 2 2
#s e t tmp=.
#i n c l u d e <C: \ U s e r s \ d c h a s s i n \ Desktop \ Dropbox \UVic\ C o u r s e s \MECH599\ T h e s i s \ e v a l u a t i o n \ matlab \ e n d u s e m o n i t o r . h>
#i n c l u d e ”C: \ U s e r s \ d c h a s s i n \ Desktop \ Dropbox \UVic\ C o u r s e s \MECH599\ T h e s i s \ e v a l u a t i o n \ matlab \ e n d u s e m o n i t o r . glm ”
#i n c l u d e ”C: \ U s e r s \ d c h a s s i n \ Desktop \ Dropbox \UVic\ C o u r s e s \MECH599\ T h e s i s \ e v a l u a t i o n \ matlab \ s c h e d u l e s . glm ”
#i n c l u d e ”C: \ U s e r s \ d c h a s s i n \ Desktop \ Dropbox \UVic\ C o u r s e s \MECH599\ T h e s i s \ e v a l u a t i o n \ matlab \ h o u s e . glm ”
#i n c l u d e ”C: \ U s e r s \ d c h a s s i n \ Desktop \ Dropbox \UVic\ C o u r s e s \MECH599\ T h e s i s \ e v a l u a t i o n \ matlab \ market . glm ”
clock {
t i m e z o n e MST+7;
s t a r t t i m e ’2014 −07 −01 0 0 : 0 0 : 0 0 ’ ;
s t o p t i m e ’2014 −07 −29 0 0 : 0 0 : 0 0 ’ ;
}
module c l i m a t e ;
module p o w e r f l o w {
market price name ” p r i c e ”;
}
module t a p e ;
object climate {
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name ” w e a t h e r ” ;
t m y f i l e ”AZ−P h o e n i x . tmy2 ” ;
i n t e r p o l a t e ”QUADRATIC” ;
}
object
}
object
}
object
}
object
}
object
}
object
}
object
}
object
}
object
}
object
}
object
triplex meter {
name ” f i x e d m e t e r ” ;
p h a s e s ”SA ” ;
b i l l m o d e ”UNIFORM” ;
bill day 1;
p r i c e ” 8 2 . 3 8 0 0 $ /MWh” ;
occupancy occupancy ;
nominal voltage 120;
house {
name ” f i x e d h o u s e ” ;
parent ” fixed meter ”;
f l o o r a r e a 2400;
t h e r m a l i n t e g r i t y l e v e l ”VERY GOOD” ;
number of stories 2;
heating setpoint heating setpoint ;
cooling setpoint cooling setpoint ;
design heating capacity 0;
design cooling capacity 0;
air temperature 75;
occupancy occupancy ;
thermostat deadband 1 ;
recorder {
parent ” fixed house ”;
property ” outdoor temperature , air temperature , heating setpoint , c o o l i n g s e t p o i n t , adj heating cap , adj cooling
interval 60;
f i l e ” f i x e d h o u s e . csv ”;
recorder {
parent ” fixed meter ”;
p r o p e r t y ” m e a s u r e d r e a l e n e r g y , p r i c e , m o n t h l y b i l l , occupancy ” ;
i n t e r v a l 3600;
f i l e ” fixed meter . csv ”;
enduse monitor {
parent ” fixed house ”;
filename ” fixed enduse . csv ”;
recorder {
parent ” fixed house ”;
p r o p e r t y ” envelope UA , f l o o r a r e a , UA, a i r h e a t c a p a c i t y , m a s s h e a t c o e f f , m a s s h e a t c a p a c i t y , d e s i g n h e a t i n g c a p
i n t e r v a l ” −1”;
limit 1;
f i l e ” design . csv ”;
triplex meter {
name ” t o u m e t e r ” ;
p h a s e s ”SA ” ;
b i l l m o d e ”UNIFORM” ;
bill day 1;
price tou tariff ;
occupancy occupancy ;
nominal voltage 120;
house {
name ” t o u h o u s e ” ;
parent ” tou meter ”;
f l o o r a r e a 2400;
t h e r m a l i n t e g r i t y l e v e l ”VERY GOOD” ;
number of stories 2;
heating setpoint heating setpoint ;
cooling setpoint cooling setpoint ;
design heating capacity 0;
design cooling capacity 0;
air temperature 75;
occupancy occupancy ;
thermostat deadband 1 ;
recorder {
parent ” tou meter ”;
property ” measured real energy , price , monthly bill ”;
i n t e r v a l 3600;
f i l e ” tou meter . csv ”;
triplex meter {
name ” t o u m e t e r d r ” ;
p h a s e s ”SA ” ;
b i l l m o d e ”UNIFORM” ;
bill day 1;
price tou tariff ;
occupancy occupancy ;
nominal voltage 120;
house {
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name ” t o u h o u s e d r ” ;
parent ” tou meter dr ”;
f l o o r a r e a 2400;
t h e r m a l i n t e g r i t y l e v e l ”VERY GOOD” ;
number of stories 2;
heating setpoint heating setback ;
cooling setpoint cooling setback ;
design heating capacity 0;
design cooling capacity 0;
air temperature 75;
occupancy occupancy ;
thermostat deadband 1 ;
}
object
}
object
recorder {
parent ” tou meter dr ”;
property ” measured real energy , price , monthly bill ”;
i n t e r v a l 3600;
f i l e ” tou meter dr . csv ”;
recorder {
parent ” tou house dr ”;
property ” outdoor temperature , air temperature , heating setpoint , c o o l i n g s e t p o i n t , adj heating cap , adj cooling
interval 60;
f i l e ” tou house . csv ”;
}
o b j e c t market {
name ” r e t a i l ” ;
p r i c e ” t y p e : normal ( 0 . 0 8 2 2 2 0 , 0 . 0 1 0 1 5 4 ) ; r e f r e s h : 5 min ; min : 0 ; max : 9 9 9 ” ;
price mean 0.08222;
price stdev 0.0101542;
}
object triplex meter {
name ” r t p m e t e r ” ;
p h a s e s ”SA ” ;
b i l l m o d e ”HOURLY” ;
bill day 1;
power market ” r e t a i l ” ;
occupancy occupancy ;
nominal voltage 120;
}
o b j e c t house {
name ” r t p h o u s e ” ;
parent ” rtp meter ”;
f l o o r a r e a 2400;
t h e r m a l i n t e g r i t y l e v e l ”VERY GOOD” ;
number of stories 2;
heating setpoint heating setpoint ;
cooling setpoint cooling setpoint ;
design heating capacity 0;
design cooling capacity 0;
air temperature 75;
occupancy occupancy ;
thermostat deadband 1 ;
}
object recorder {
parent ” rtp meter ”;
p r o p e r t y ” m e a s u r e d r e a l e n e r g y , p r i c e , m o n t h l y b i l l , occupancy ” ;
interval 300;
f i l e ” rtp meter . csv ”;
}
object triplex meter {
name ” r t p m e t e r d r ” ;
p h a s e s ”SA ” ;
b i l l m o d e ”HOURLY” ;
bill day 1;
power market ” r e t a i l ” ;
occupancy occupancy ;
nominal voltage 120;
}
o b j e c t house {
name ” r t p h o u s e d r ” ;
parent ” rtp meter dr ”;
f l o o r a r e a 2400;
t h e r m a l i n t e g r i t y l e v e l ”VERY GOOD” ;
number of stories 2;
heating setpoint heating setpoint ;
cooling setpoint cooling setpoint ;
design heating capacity 0;
design cooling capacity 0;
air temperature 75;
thermostat deadband 0 . 0 1 ;
d l c o f f s e t 0;
k comfort ;
occupancy occupancy ;
o v e r r i d e ”OFF” ;
}
object rtp thermostat {
name ” r t p t s t a t ” ;
parent ” rtp house dr ”;
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market ” r e t a i l ” ;
}
object
}
object
}
object
}
object
}
object
}
object
}
object
}
object
}
object
}
object
}
object
recorder {
parent ” r t p t s t a t ”;
p r o p e r t y ”P , Pavg , Pstd , k , d l c o f f s e t , Tout , T a i r , Theat , Tcool , Taux , dTair , dTairMin , mode ” ;
interval 300;
f i l e ” r t p t s t a t . csv ”;
recorder {
parent ” rtp meter dr ”;
p r o p e r t y ” m e a s u r e d r e a l e n e r g y , p r i c e , m o n t h l y b i l l , occupancy ” ;
interval 300;
f i l e ” rtp meter dr . csv ”;
recorder {
parent ” rtp house dr ”;
property ” outdoor temperature , air temperature , heating setpoint , c o o l i n g s e t p o i n t , adj heating cap , adj cooling
interval 60;
f i l e ” rtp house . csv ”;
triplex meter {
name ” n e w m e t e r ” ;
p h a s e s ”SA ” ;
b i l l m o d e ”HOURLY” ;
bill day 1;
power market ” r e t a i l ” ;
occupancy occupancy ;
nominal voltage 120;
house {
name ” n e w h o u s e ” ;
p a r e n t ” new meter ” ;
f l o o r a r e a 2400;
t h e r m a l i n t e g r i t y l e v e l ”VERY GOOD” ;
number of stories 2;
heating setpoint heating setpoint ;
cooling setpoint cooling setpoint ;
design heating capacity 0;
design cooling capacity 0;
air temperature 75;
occupancy occupancy ;
thermostat deadband 1 ;
recorder {
p a r e n t ” new meter ” ;
p r o p e r t y ” m e a s u r e d r e a l e n e r g y , p r i c e , m o n t h l y b i l l , occupancy ” ;
interval 300;
f i l e ” new meter . csv ” ;
triplex meter {
name ” n e w m e t e r d r ” ;
p h a s e s ”SA ” ;
b i l l m o d e ”HOURLY” ;
bill day 1;
power market ” r e t a i l ” ;
occupancy occupancy ;
nominal voltage 120;
house {
name ” n e w h o u s e d r ” ;
parent ” new meter dr ” ;
f l o o r a r e a 2400;
t h e r m a l i n t e g r i t y l e v e l ”VERY GOOD” ;
number of stories 2;
heating setpoint heating setpoint ;
cooling setpoint cooling setpoint ;
design heating capacity 0;
design cooling capacity 0;
air temperature 75;
thermostat deadband 0 . 0 1 ;
d l c o f f s e t 0;
k comfort ;
occupancy occupancy ;
o v e r r i d e ”OFF” ;
new thermostat {
name ” n e w t s t a t ” ;
parent ” new house dr ” ;
market ” r e t a i l ” ;
recorder {
parent ” new tstat ”;
p r o p e r t y ”P , Pavg , Pstd , k , d l c o f f s e t , Tout , T a i r , Theat , Tcool , Taux , dTair , dTairMin , mode ” ;
interval 300;
f i l e ” new tstat . csv ”;
recorder {
parent ” new meter dr ” ;
p r o p e r t y ” m e a s u r e d r e a l e n e r g y , p r i c e , m o n t h l y b i l l , occupancy ” ;
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interval 300;
f i l e ” new meter dr . csv ” ;
}
object
recorder {
parent ” new house dr ” ;
property ” outdoor temperature , air temperature , heating setpoint , c o o l i n g s e t p o i n t , adj heating cap , adj cooling
interval 60;
f i l e ” new house . csv ” ;
}
B.2.4
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Miami Summer
// g e n e r a t e d by Matlab c l a s s G r i d l a b d on 30−Oct −2014 0 9 : 0 7 : 0 6
#s e t tmp=.
#i n c l u d e <C: \ U s e r s \ d c h a s s i n \ Desktop \ Dropbox \UVic\ C o u r s e s \MECH599\ T h e s i s \ e v a l u a t i o n \ matlab \ e n d u s e m o n i t o r . h>
#i n c l u d e ”C: \ U s e r s \ d c h a s s i n \ Desktop \ Dropbox \UVic\ C o u r s e s \MECH599\ T h e s i s \ e v a l u a t i o n \ matlab \ e n d u s e m o n i t o r . glm ”
#i n c l u d e ”C: \ U s e r s \ d c h a s s i n \ Desktop \ Dropbox \UVic\ C o u r s e s \MECH599\ T h e s i s \ e v a l u a t i o n \ matlab \ s c h e d u l e s . glm ”
#i n c l u d e ”C: \ U s e r s \ d c h a s s i n \ Desktop \ Dropbox \UVic\ C o u r s e s \MECH599\ T h e s i s \ e v a l u a t i o n \ matlab \ h o u s e . glm ”
#i n c l u d e ”C: \ U s e r s \ d c h a s s i n \ Desktop \ Dropbox \UVic\ C o u r s e s \MECH599\ T h e s i s \ e v a l u a t i o n \ matlab \ market . glm ”
clock {
t i m e z o n e EST+5EDT;
s t a r t t i m e ’2014 −07 −01 0 0 : 0 0 : 0 0 ’ ;
s t o p t i m e ’2014 −07 −29 0 0 : 0 0 : 0 0 ’ ;
}
module c l i m a t e ;
module p o w e r f l o w {
market price name ” p r i c e ”;
}
module t a p e ;
object climate {
name ” w e a t h e r ” ;
t m y f i l e ”FL−Miami . tmy2 ” ;
i n t e r p o l a t e ”QUADRATIC” ;
}
object triplex meter {
name ” f i x e d m e t e r ” ;
p h a s e s ”SA ” ;
b i l l m o d e ”UNIFORM” ;
bill day 1;
p r i c e ” 7 8 . 8 1 0 0 $ /MWh” ;
occupancy occupancy ;
nominal voltage 120;
}
o b j e c t house {
name ” f i x e d h o u s e ” ;
parent ” fixed meter ”;
f l o o r a r e a 2400;
t h e r m a l i n t e g r i t y l e v e l ”VERY GOOD” ;
number of stories 2;
heating setpoint heating setpoint ;
cooling setpoint cooling setpoint ;
design heating capacity 0;
design cooling capacity 0;
air temperature 75;
occupancy occupancy ;
thermostat deadband 1 ;
}
object recorder {
parent ” fixed house ”;
property ” outdoor temperature , air temperature , heating setpoint , c o o l i n g s e t p o i n t , adj heating cap , adj cooling
interval 60;
f i l e ” f i x e d h o u s e . csv ”;
}
object recorder {
parent ” fixed meter ”;
p r o p e r t y ” m e a s u r e d r e a l e n e r g y , p r i c e , m o n t h l y b i l l , occupancy ” ;
i n t e r v a l 3600;
f i l e ” fixed meter . csv ”;
}
object enduse monitor {
parent ” fixed house ”;
filename ” fixed enduse . csv ”;
}
object recorder {
parent ” fixed house ”;
p r o p e r t y ” envelope UA , f l o o r a r e a , UA, a i r h e a t c a p a c i t y , m a s s h e a t c o e f f , m a s s h e a t c a p a c i t y , d e s i g n h e a t i n g c a p
i n t e r v a l ” −1”;
limit 1;
f i l e ” design . csv ”;
}
object triplex meter {
name ” t o u m e t e r ” ;
p h a s e s ”SA ” ;
b i l l m o d e ”UNIFORM” ;
bill day 1;
price tou tariff ;
occupancy occupancy ;
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nominal voltage
}
object
}
object
}
object
}
object
}
object
}
object
120;
house {
name ” t o u h o u s e ” ;
parent ” tou meter ”;
f l o o r a r e a 2400;
t h e r m a l i n t e g r i t y l e v e l ”VERY GOOD” ;
number of stories 2;
heating setpoint heating setpoint ;
cooling setpoint cooling setpoint ;
design heating capacity 0;
design cooling capacity 0;
air temperature 75;
occupancy occupancy ;
thermostat deadband 1 ;
recorder {
parent ” tou meter ”;
property ” measured real energy , price , monthly bill ”;
i n t e r v a l 3600;
f i l e ” tou meter . csv ”;
triplex meter {
name ” t o u m e t e r d r ” ;
p h a s e s ”SA ” ;
b i l l m o d e ”UNIFORM” ;
bill day 1;
price tou tariff ;
occupancy occupancy ;
nominal voltage 120;
house {
name ” t o u h o u s e d r ” ;
parent ” tou meter dr ”;
f l o o r a r e a 2400;
t h e r m a l i n t e g r i t y l e v e l ”VERY GOOD” ;
number of stories 2;
heating setpoint heating setback ;
cooling setpoint cooling setback ;
design heating capacity 0;
design cooling capacity 0;
air temperature 75;
occupancy occupancy ;
thermostat deadband 1 ;
recorder {
parent ” tou meter dr ”;
property ” measured real energy , price , monthly bill ”;
i n t e r v a l 3600;
f i l e ” tou meter dr . csv ”;
recorder {
parent ” tou house dr ”;
property ” outdoor temperature , air temperature , heating setpoint , c o o l i n g s e t p o i n t , adj heating cap , adj cooling
interval 60;
f i l e ” tou house . csv ”;
}
o b j e c t market {
name ” r e t a i l ” ;
p r i c e ” t y p e : normal ( 0 . 0 7 8 7 3 0 , 0 . 0 0 9 7 2 3 ) ; r e f r e s h : 5 min ; min : 0 ; max : 9 9 9 ” ;
price mean 0.07873;
price stdev 0.00972316;
}
object triplex meter {
name ” r t p m e t e r ” ;
p h a s e s ”SA ” ;
b i l l m o d e ”HOURLY” ;
bill day 1;
power market ” r e t a i l ” ;
occupancy occupancy ;
nominal voltage 120;
}
o b j e c t house {
name ” r t p h o u s e ” ;
parent ” rtp meter ”;
f l o o r a r e a 2400;
t h e r m a l i n t e g r i t y l e v e l ”VERY GOOD” ;
number of stories 2;
heating setpoint heating setpoint ;
cooling setpoint cooling setpoint ;
design heating capacity 0;
design cooling capacity 0;
air temperature 75;
occupancy occupancy ;
thermostat deadband 1 ;
}
object recorder {
parent ” rtp meter ”;
p r o p e r t y ” m e a s u r e d r e a l e n e r g y , p r i c e , m o n t h l y b i l l , occupancy ” ;
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interval 300;
f i l e ” rtp meter . csv ”;
}
object
}
object
}
object
}
object
}
object
}
object
}
object
}
object
}
object
}
object
triplex meter {
name ” r t p m e t e r d r ” ;
p h a s e s ”SA ” ;
b i l l m o d e ”HOURLY” ;
bill day 1;
power market ” r e t a i l ” ;
occupancy occupancy ;
nominal voltage 120;
house {
name ” r t p h o u s e d r ” ;
parent ” rtp meter dr ”;
f l o o r a r e a 2400;
t h e r m a l i n t e g r i t y l e v e l ”VERY GOOD” ;
number of stories 2;
heating setpoint heating setpoint ;
cooling setpoint cooling setpoint ;
design heating capacity 0;
design cooling capacity 0;
air temperature 75;
thermostat deadband 0 . 0 1 ;
d l c o f f s e t 0;
k comfort ;
occupancy occupancy ;
o v e r r i d e ”OFF” ;
rtp thermostat {
name ” r t p t s t a t ” ;
parent ” rtp house dr ”;
market ” r e t a i l ” ;
recorder {
parent ” r t p t s t a t ”;
p r o p e r t y ”P , Pavg , Pstd , k , d l c o f f s e t , Tout , T a i r , Theat , Tcool , Taux , dTair , dTairMin , mode ” ;
interval 300;
f i l e ” r t p t s t a t . csv ”;
recorder {
parent ” rtp meter dr ”;
p r o p e r t y ” m e a s u r e d r e a l e n e r g y , p r i c e , m o n t h l y b i l l , occupancy ” ;
interval 300;
f i l e ” rtp meter dr . csv ”;
recorder {
parent ” rtp house dr ”;
property ” outdoor temperature , air temperature , heating setpoint , c o o l i n g s e t p o i n t , adj heating cap , adj cooling
interval 60;
f i l e ” rtp house . csv ”;
triplex meter {
name ” n e w m e t e r ” ;
p h a s e s ”SA ” ;
b i l l m o d e ”HOURLY” ;
bill day 1;
power market ” r e t a i l ” ;
occupancy occupancy ;
nominal voltage 120;
house {
name ” n e w h o u s e ” ;
p a r e n t ” new meter ” ;
f l o o r a r e a 2400;
t h e r m a l i n t e g r i t y l e v e l ”VERY GOOD” ;
number of stories 2;
heating setpoint heating setpoint ;
cooling setpoint cooling setpoint ;
design heating capacity 0;
design cooling capacity 0;
air temperature 75;
occupancy occupancy ;
thermostat deadband 1 ;
recorder {
p a r e n t ” new meter ” ;
p r o p e r t y ” m e a s u r e d r e a l e n e r g y , p r i c e , m o n t h l y b i l l , occupancy ” ;
interval 300;
f i l e ” new meter . csv ” ;
triplex meter {
name ” n e w m e t e r d r ” ;
p h a s e s ”SA ” ;
b i l l m o d e ”HOURLY” ;
bill day 1;
power market ” r e t a i l ” ;
occupancy occupancy ;
nominal voltage 120;
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}
object
}
object
}
object
}
object
}
object
house {
name ” n e w h o u s e d r ” ;
parent ” new meter dr ” ;
f l o o r a r e a 2400;
t h e r m a l i n t e g r i t y l e v e l ”VERY GOOD” ;
number of stories 2;
heating setpoint heating setpoint ;
cooling setpoint cooling setpoint ;
design heating capacity 0;
design cooling capacity 0;
air temperature 75;
thermostat deadband 0 . 0 1 ;
d l c o f f s e t 0;
k comfort ;
occupancy occupancy ;
o v e r r i d e ”OFF” ;
new thermostat {
name ” n e w t s t a t ” ;
parent ” new house dr ” ;
market ” r e t a i l ” ;
recorder {
parent ” new tstat ”;
p r o p e r t y ”P , Pavg , Pstd , k , d l c o f f s e t , Tout , T a i r , Theat , Tcool , Taux , dTair , dTairMin , mode ” ;
interval 300;
f i l e ” new tstat . csv ”;
recorder {
parent ” new meter dr ” ;
p r o p e r t y ” m e a s u r e d r e a l e n e r g y , p r i c e , m o n t h l y b i l l , occupancy ” ;
interval 300;
f i l e ” new meter dr . csv ” ;
recorder {
parent ” new house dr ” ;
property ” outdoor temperature , air temperature , heating setpoint , c o o l i n g s e t p o i n t , adj heating cap , adj cooling
interval 60;
f i l e ” new house . csv ” ;
}
B.2.5
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Feeder Response
// FEEDER TEST
#d e f i n e NHOMES=100
#s e t tmp=.
#i n c l u d e <C: \ U s e r s \ d c h a s s i n \ Desktop \ Dropbox \UVic\ C o u r s e s \MECH599\ T h e s i s \ e v a l u a t i o n \ matlab \ e n d u s e m o n i t o r . h>
#i n c l u d e ”C: \ U s e r s \ d c h a s s i n \ Desktop \ Dropbox \UVic\ C o u r s e s \MECH599\ T h e s i s \ e v a l u a t i o n \ matlab \ e n d u s e m o n i t o r . glm ”
#i n c l u d e ”C: \ U s e r s \ d c h a s s i n \ Desktop \ Dropbox \UVic\ C o u r s e s \MECH599\ T h e s i s \ e v a l u a t i o n \ matlab \ s c h e d u l e s . glm ”
#i n c l u d e ”C: \ U s e r s \ d c h a s s i n \ Desktop \ Dropbox \UVic\ C o u r s e s \MECH599\ T h e s i s \ e v a l u a t i o n \ matlab \ h o u s e . glm ”
#i n c l u d e ”C: \ U s e r s \ d c h a s s i n \ Desktop \ Dropbox \UVic\ C o u r s e s \MECH599\ T h e s i s \ e v a l u a t i o n \ matlab \ market . glm ”
clock {
t i m e z o n e MST+7;
s t a r t t i m e ’2014 −07 −01 0 0 : 0 0 : 0 0 ’ ;
s t o p t i m e ’2014 −07 −02 0 0 : 0 0 : 0 0 ’ ;
}
module c l i m a t e ;
module p o w e r f l o w {
market price name ” p r i c e ”;
}
module t a p e ;
object climate {
name ” w e a t h e r ” ;
t m y f i l e ”AZ−P h o e n i x . tmy2 ” ;
i n t e r p o l a t e ”QUADRATIC” ;
}
// MARKET
o b j e c t market {
name ” r e t a i l ” ;
p r i c e ” t y p e : normal ( 0 . 0 7 8 7 3 0 , 0 . 0 0 9 7 2 3 ) ;
price mean 0.07873;
price stdev 0.00972316;
object recorder {
property price ;
interval 4;
f i l e ” p r i c e . csv ”;
};
}
// RTP HOUSES
o b j e c t t r i p l e x m e t e r : . . $ {NHOMES} {
groupid 1;
p h a s e s ”SA ” ;
b i l l m o d e ”HOURLY” ;
r e f r e s h : 5 min ; min : 0 ; max : 9 9 9 ” ;
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bill day 1;
power market ” r e t a i l ” ;
occupancy occupancy ;
nominal voltage 120;
o b j e c t house {
f l o o r a r e a random . u n i f o r m ( 1 2 0 0 , 3 6 0 0 ) ;
t h e r m a l i n t e g r i t y l e v e l ”VERY GOOD” ;
number of stories 2;
heating setpoint heating setpoint ;
cooling setpoint cooling setpoint ;
design heating capacity 0;
design cooling capacity 0;
a i r t e m p e r a t u r e random . u n i f o r m ( 7 2 , 7 8 ) ;
thermostat deadband 1 ;
d l c o f f s e t 0;
k comfort ;
occupancy occupancy ;
o v e r r i d e ”OFF” ;
object rtp thermostat {
market ” r e t a i l ” ;
};
};
}
// NEW HOUSES
o b j e c t t r i p l e x m e t e r : . . $ {NHOMES} {
groupid 2;
p h a s e s ”SA ” ;
b i l l m o d e ”HOURLY” ;
bill day 1;
power market ” r e t a i l ” ;
occupancy occupancy ;
nominal voltage 120;
o b j e c t house {
f l o o r a r e a random . u n i f o r m ( 1 2 0 0 , 3 6 0 0 ) ;
t h e r m a l i n t e g r i t y l e v e l ”VERY GOOD” ;
number of stories 2;
heating setpoint heating setpoint ;
cooling setpoint cooling setpoint ;
design heating capacity 0;
design cooling capacity 0;
a i r t e m p e r a t u r e random . u n i f o r m ( 7 2 , 7 8 ) ;
thermostat deadband 0 . 0 1 ;
d l c o f f s e t 0;
k comfort ;
occupancy occupancy ;
o v e r r i d e ”OFF” ;
o b j e c t new thermostat {
market ” r e t a i l ” ;
};
};
}
// DATA COLLECTORS
object collector {
g r o u p ” c l a s s=t r i p l e x m e t e r and g r o u p i d =1”;
p r o p e r t y ”sum ( m e a s u r e d r e a l p o w e r ) ” ;
interval 4;
f i l e ” f e e d e r r t p . csv ”;
}
object collector {
g r o u p ” c l a s s=t r i p l e x m e t e r and g r o u p i d =2”;
p r o p e r t y ”sum ( m e a s u r e d r e a l p o w e r ) ” ;
interval 4;
f i l e ” feeder new . csv ”;
}
B.3
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Modifications to GridLAB-D
Index : c o r e / load . c
===================================================================
−−− c o r e / l o a d . c ( r e v i s i o n 4 8 5 4 )
+++ c o r e / l o a d . c ( w o r k i n g copy )
@@ −898 ,6 +898 ,11 @@
}
else
o c l a s s −>h a s r u n t i m e = f a l s e ;
+
+
/∗ c l e a r b u f f e r s ∗/
+
code block [ 0 ] = global block [ 0 ] = i n i t b l o c k [ 0 ] = ’\0 ’;
+
code used = 0;
+
r e t u r n SUCCESS ;
}
147
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
Index : c o r e / r t / g r i d l a b d . h
===================================================================
−−− c o r e / r t / g r i d l a b d . h
( r e v i s i o n 4854)
+++ c o r e / r t / g r i d l a b d . h
( w o r k i n g copy )
@@ −824 ,22 +824 ,81 @@
loadshape ∗ next ;
/∗ n e x t l o a d s h a p e i n l i s t ∗/
};
−s t r u c t s e n d u s e {
−
loadshape ∗ shape ;
−
complex power ;
/∗ power i n kW ∗/
−
complex e n e r g y ;
/∗ t o t a l e n e r g y i n kWh ∗/
−
complex demand ;
/∗ maximum power i n kW ( can be r e s e t ) ∗/
/∗ c o n s t a n t impedance f r a c t i o n ( pu l o a d ) ∗/
−
double impedance fraction ;
−
double c u r r e n t f r a c t i o n ;
/∗ c o n s t a n t c u r r e n t f r a c t i o n ( pu l o a d ) ∗/
−
double p o w e r f r a c t i o n ;
/∗ c o n s t a n t power f r a c t i o n ( pu l o a d ) ∗ /
/∗ power f a c t o r ∗/
−
double power factor ;
−
double v o l t a g e f a c t o r ;
/∗ v o l t a g e f a c t o r ( pu n o m i n a l ) ∗/
−
double heatgain ;
/∗ i n t e r n a l h e a t from l o a d ( Btu /h ) ∗/
−
double h e a t g a i n f r a c t i o n ;
/∗ f r a c t i o n o f power t h a t g o e s t o i n t e r n a l h e a t ( pu Btu /h ) ∗/
+t y p e d e f enum {
+
EUMT MOTOR A, /∗∗< 3ph i n d u c t i o n m o t o r s d r i v i n g c o n s t a n t t o r q u e l o a d s ∗/
+
EUMT MOTOR B, /∗∗< i n d u c t i o n m o t o r s d r i v i n g h i g h i n e r t i a s p e e d−s q u a r e s t o r q u e l o a d s ∗/
+
EUMT MOTOR C, /∗∗< i n d u c t i o n m o t o r s d r i v i n g low i n e r t i a l o a d s s p e e d−s q u a r e d t o r q u e l o a d s ∗/
+
EUMT MOTOR D, /∗∗< 1ph i n d u c t i o n m o t o r s d r i v i n g c o n s t a n t t o r q u e l o a d s ∗/
+
EUMT COUNT, /∗ must be l a s t ∗/
+} EUMOTORTYPE;
+t y p e d e f enum {
+
EUET ELECTRONIC A, /∗∗< s i m p l e power e l e c t r o n i c s ( no b a c k f e e d ) ∗/
+
EUET ELECTRONIC B , /∗∗< advanced power e l e c t r o n i c s (w/ b a c k f e e d ) ∗/
+
EUET COUNT, /∗ must be l a s t ∗/
+} EUELECTRONICTYPE;
+t y p e d e f s t r u c t s m o t o r {
+
complex power ;
/∗∗< motor power when r u n n i n g ∗/
+
complex impedance ; /∗∗< motor impedance when s t a l l e d ∗/
+
double i n e r t i a ;
/∗∗< motor i n e r t i a i n s e c o n d s ∗/
+
double v s t a l l ;
/∗∗< motor s t a l l v o l t a g e ( pu ) ∗/
+
double v s t a r t ;
/∗∗< motor s t a r t v o l t a g e ( pu ) ∗/
+
double v t r i p ;
/∗∗< motor t r i p v o l t a g e ( pu ) ∗/
+
double t t r i p ;
/∗∗< motor t h e r m a l t r i p t i m e i n s e c o n d s ∗/
+
/∗ TODO add s l i p d a t a ( 0 f o r s y n c h r o n o u s m o t o r s ) ∗/
+} EUMOTOR;
+t y p e d e f s t r u c t s e l e c t r o n i c {
+
complex power ;
/∗∗< l o a d power when r u n n i n g ∗/
+
double i n e r t i a ;
/∗∗< l o a d ” i n e r t i a ” ∗/
+
double v t r i p ;
/∗∗< l o a d ” t r i p ” v o l t a g e ( pu ) ∗/
/∗∗< l o a d ” s t a r t ” v o l t a g e ( pu ) ∗/
+
double v s t a r t ;
+} EUELECTRONIC ;
−
enduse ∗ next ;
−};
−
+t y p e d e f s t r u c t s e n d u s e {
+
/∗ t h e o u t p u t v a l u e must be f i r s t f o r t r a n s f o r m t o s t r e a m ∗/
+
/∗ m e t e r v a l u e s ∗/
+
complex t o t a l ;
/∗ t o t a l power i n kW ∗/
+
complex e n e r g y ;
/∗ t o t a l e n e r g y i n kWh ∗/
+
complex demand ;
/∗ maximum power i n kW ( can be r e s e t ) ∗/
+
+
/∗ c i r c u i t c o n f i g u r a t i o n ∗/
+
set config ;
/∗ end−u s e c o n f i g u r a t i o n ∗/
+
double breaker amps ;
/∗ b r e a k e r l i m i t ( i f any ) ∗/
+
+
/∗ z i p v a l u e s ∗/
+
complex a d m i t t a n c e ;
/∗ c o n s t a n t impedance o p r t i o n o f l o a d i n kW ∗/
+
complex c u r r e n t ;
/∗ c o n s t a n t c u r r e n t p o r t i o n o f l o a d i n kW ∗/
+
complex power ;
/∗ c o n s t a n t power p o r t i o n o f l o a d i n kW ∗/
+
+
/∗ c o m p o s i t e l o a d d a t a ∗/
+
EUMOTOR motor [ EUMT COUNT ] ;
/∗ motor l o a d s (A−D) ∗/
+
EUELECTRONIC e l e c t r o n i c [ EUET COUNT ] ;
/∗ e l e c t r o n i c l o a d s ( S/D) ∗/
+
+
/∗ l o a d i n g ∗/
+
double impedance fraction ;
/∗ c o n s t a n t impedance f r a c t i o n ( pu l o a d ) ∗/
+
double c u r r e n t f r a c t i o n ;
/∗ c o n s t a n t c u r r e n t f r a c t i o n ( pu l o a d ) ∗/
+
double p o w e r f r a c t i o n ;
/∗ c o n s t a n t power f r a c t i o n ( pu l o a d ) ∗ /
+
double power factor ;
/∗ power f a c t o r ∗/
+
double v o l t a g e f a c t o r ;
/∗ v o l t a g e f a c t o r ( pu n o m i n a l ) ∗/
+
+
/∗ h e a t ∗/
+
double heatgain ;
/∗ i n t e r n a l h e a t from l o a d ( Btu /h ) ∗/
+
double cumulative heatgain ;
/∗ i n t e r n a l c u m u l a t i v e h e a t g a i n from l o a d ( Btu ) ∗/
+
double h e a t g a i n f r a c t i o n ;
/∗ f r a c t i o n o f power t h a t g o e s t o i n t e r n a l h e a t ( pu Btu /h ) ∗/
+
+
/∗ m i s c i n f o ∗/
+
c h a r ∗name ;
+
loadshape ∗ shape ;
+
TIMESTAMP t l a s t ;
/∗ l a s t t i m e o f u p d a t e ∗/
+
148
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
+
// added f o r backward c o m p a t i b i l i t y w i t h r e s ENDUSELOAD
+
// @todo t h e s e a r e o b s o l e t e and must be r e t r o f i t t e d w i t h t h e above v a l u e s
+
struct s o b j e c t l i s t ∗ end obj ;
+
+
s t r u c t s enduse ∗ next ;
+#i f d e f DEBUG
+
u n s i g n e d i n t magic ;
+#e n d i f
+} e n d u s e ;
/∗ o b j e c t f l a g s ∗/
0 x0000 /∗∗< O b j e c t f l a g ; none s e t ∗/
#d e f i n e OF NONE
#d e f i n e OF HASPLC
0 x0001 /∗∗< O b j e c t f l a g ; e x t e r n a l PLC i s a t t a c h e d , d i s a b l e s
I n d e x : p o w e r f l o w / t r i p l e x m e t e r . cpp
===================================================================
−−− p o w e r f l o w / t r i p l e x m e t e r . cpp ( r e v i s i o n 4 8 5 4 )
+++ p o w e r f l o w / t r i p l e x m e t e r . cpp ( w o r k i n g copy )
@@ −36 ,6 +36 ,8 @@
return 0;
}
l o c a l PLC ∗/
+s t a t i c char1024 market price name = ” c u r r e n t m a r k e t . c l e a r i n g p r i c e ” ;
+
//////////////////////////////////////////////////////////////////////////
// t r i p l e x m e t e r CLASS FUNCTIONS
//////////////////////////////////////////////////////////////////////////
@@ −122 ,6 +124 ,9 @@
GL THROW( ” Unable t o p u b l i s h t r i p l e x m e t e r d e l t a m o d e f u n c t i o n ” ) ;
i f ( g l p u b l i s h f u n c t i o n ( o c l a s s , ” d e l t a f r e q p w r o b j e c t ” , (FUNCTIONADDR) d e l t a f r e q u e n c y n o d e )
GL THROW( ” Unable t o p u b l i s h t r i p l e x m e t e r d e l t a m o d e f u n c t i o n ” ) ;
+
+
// market p r i c e name
+
g l g l o b a l c r e a t e ( ” p o w e r f l o w : : m a r k e t p r i c e n a m e ” , PT char1024 ,& m a r k e t p r i c e n a m e , NULL ) ;
}
}
@@ −179 ,9 +184 ,9 @@
#e n d i f
i f ( p o w e r m a r k e t != 0 ) {
p r i c e p r o p = g l g e t p r o p e r t y ( p o we r m a rk e t , ” c u r r e n t m a r k e t . c l e a r i n g p r i c e ” ) ;
p r i c e p r o p = g l g e t p r o p e r t y ( p o we r m a rk e t , m a r k e t p r i c e n a m e ) ;
i f ( p r i c e p r o p == 0 ) {
−
GL THROW( ” t r i p l e x m e t e r : : p o w e r m a r k e t o b j e c t \’% s \ ’ d o e s n o t p u b l i s h \ ’ c u r r e n t m a r k e t . c l e a r
+
GL THROW( ” t r i p l e x m e t e r : : p o w e r m a r k e t o b j e c t \’% s \ ’ d o e s n o t p u b l i s h \’% s \ ’ ” , ( p o we r m a rk e t−
}
}
check prices ();
Index : r e s i d e n t i a l / bsra2014 . h
===================================================================
−−− r e s i d e n t i a l / b s r a 2 0 1 4 . h
( r e v i s i o n 0)
+++ r e s i d e n t i a l / b s r a 2 0 1 4 . h
( w o r k i n g copy )
@@ −0 ,0 +1 ,401 @@
+// $ I d $
+//
+// T h i s f i l e c o n t a i n s t h e BSRA 2014 e n d u s e d a i l y e n e r g y u s e s o v e r ELCAP l o a d s h a p e s ( s e e NEEA 2 0 1 4 )
+//
+//
NEEA 2014
+//
Daily energy
Fraction
+// Enduse
(kWh/d )
o f ELCAP
Remarks
+// −−−−−−−−−−−−−−
−−−−−−−−−−−−
−−−−−−−−
−−−−−−−−−−−−−−−−−−−−−−−
+// R e f r i g e r a t o r
1.7
0.43
+// F r e e z e r
1.4
0.38
+// D i s h w a s h e r
0.65
1.80
+// C l o t h e s w a s h e r
0.15
0.50
+// Dryer
2.0
0.64
+// Oven
0.8
0.56
Does n o t i n c l u d e m i c r o w a v e s
+// W a t e r h e a t e r
8.0
0.56
+// P l u g s
2.5
Does n o t i n c l u d e l i g h t s
+//
TV
0.6
+//
C a b l e /DVR
0.7
+//
Game
0.2
+//
Computer
0.9
+//
DVD
0.1
+//
+// l i g h t i n g ( s o u r c e : ELCAP l i t −s p . d a t )
+{
”LIGHTS” ,
+
{30 , f a l s e , { 0 . 5 , 0 . 1 , 0 . 4 } , 0 . 9 7 , 0 . 9 } ,
+
” t y p e : a n a l o g ; s c h e d u l e : r e s i d e n t i a l −l i g h t s −d e f a u l t ; power : 0 . 2 5 kW” , // 1/8 power , 2 x l i g h t s
+
” r e s i d e n t i a l −l i g h t s −d e f a u l t ” ,
+
” p o s i t i v e ; n o n z e r o ; weekday−summer {”
+
”∗
0 ∗ 4−9 1−5 0 . 3 8 0 ; ∗
1 ∗ 4−9 1−5 0 . 3 4 0 ; ∗
2 ∗ 4−9 1−5 0 . 3 2 0 ; ∗
3 ∗ 4−9 1−5 0 . 3 2 0 ; ”
+
”∗
4 ∗ 4−9 1−5 0 . 3 2 0 ; ∗
5 ∗ 4−9 1−5 0 . 3 5 0 ; ∗
6 ∗ 4−9 1−5 0 . 4 1 0 ; ∗
7 ∗ 4−9 1−5 0 . 4 5 0 ; ”
+
”∗
8 ∗ 4−9 1−5 0 . 4 5 0 ; ∗
9 ∗ 4−9 1−5 0 . 4 5 0 ; ∗ 10 ∗ 4−9 1−5 0 . 4 5 0 ; ∗ 11 ∗ 4−9 1−5 0 . 4 5 0 ; ”
+
”∗ 12 ∗ 4−9 1−5 0 . 4 5 0 ; ∗ 13 ∗ 4−9 1−5 0 . 4 4 0 ; ∗ 14 ∗ 4−9 1−5 0 . 4 4 0 ; ∗ 15 ∗ 4−9 1−5 0 . 4 5 0 ; ”
+
”∗ 16 ∗ 4−9 1−5 0 . 4 7 0 ; ∗ 17 ∗ 4−9 1−5 0 . 5 1 0 ; ∗ 18 ∗ 4−9 1−5 0 . 5 4 0 ; ∗ 19 ∗ 4−9 1−5 0 . 5 6 0 ; ”
+
”∗ 20 ∗ 4−9 1−5 0 . 6 3 0 ; ∗ 21 ∗ 4−9 1−5 0 . 7 1 0 ; ∗ 22 ∗ 4−9 1−5 0 . 6 5 0 ; ∗ 23 ∗ 4−9 1−5 0 . 4 9 0 ”
+
”}”
+
” weekend−summer {”
+
”∗
0 ∗ 4−9 6−0 0 . 4 1 0 ; ∗
1 ∗ 4−9 6−0 0 . 3 6 0 ; ∗
2 ∗ 4−9 6−0 0 . 3 3 0 ; ∗
3 ∗ 4−9 6−0 0 . 3 2 0 ; ”
−
+
149
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+{
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+{
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
”∗
4 ∗ 4−9 6−0 0 . 3 2 0 ; ∗
5 ∗ 4−9 6−0 0 . 3 2 0 ; ∗
6 ∗ 4−9 6−0 0 . 3 4 0 ; ∗
7 ∗ 4−9 6−0 0 . 3 9 0 ; ”
”∗
8 ∗ 4−9 6−0 0 . 4 4 0 ; ∗
9 ∗ 4−9 6−0 0 . 4 7 0 ; ∗ 10 ∗ 4−9 6−0 0 . 4 7 0 ; ∗ 11 ∗ 4−9 6−0 0 . 4 7 0 ; ”
”∗ 12 ∗ 4−9 6−0 0 . 4 7 0 ; ∗ 13 ∗ 4−9 6−0 0 . 4 6 0 ; ∗ 14 ∗ 4−9 6−0 0 . 4 6 0 ; ∗ 15 ∗ 4−9 6−0 0 . 4 6 0 ; ”
”∗ 16 ∗ 4−9 6−0 0 . 4 7 0 ; ∗ 17 ∗ 4−9 6−0 0 . 4 9 0 ; ∗ 18 ∗ 4−9 6−0 0 . 5 2 0 ; ∗ 19 ∗ 4−9 6−0 0 . 5 4 0 ; ”
”∗ 20 ∗ 4−9 6−0 0 . 6 1 0 ; ∗ 21 ∗ 4−9 6−0 0 . 6 8 0 ; ∗ 22 ∗ 4−9 6−0 0 . 6 3 0 ; ∗ 23 ∗ 4−9 6−0 0 . 5 0 0 ”
”}”
” weekday−w i n t e r {”
”∗
0 ∗ 10−3 1−5 0 . 4 2 0 0 ; ∗
1 ∗ 10−3 1−5 0 . 3 8 0 0 ; ∗
2 ∗ 10−3 1−5 0 . 3 7 0 0 ; ∗
3 ∗ 10−3 1−5
”∗
4 ∗ 10−3 1−5 0 . 3 7 0 0 ; ∗
5 ∗ 10−3 1−5 0 . 4 2 0 0 ; ∗
6 ∗ 10−3 1−5 0 . 5 8 0 0 ; ∗
7 ∗ 10−3 1−5
”∗
8 ∗ 10−3 1−5 0 . 6 1 0 0 ; ∗
9 ∗ 10−3 1−5 0 . 5 6 0 0 ; ∗ 10 ∗ 10−3 1−5 0 . 5 3 0 0 ; ∗ 11 ∗ 10−3 1−5
”∗ 12 ∗ 10−3 1−5 0 . 4 9 0 0 ; ∗ 13 ∗ 10−3 1−5 0 . 4 7 0 0 ; ∗ 14 ∗ 10−3 1−5 0 . 4 7 0 0 ; ∗ 15 ∗ 10−3 1−5
”∗ 16 ∗ 10−3 1−5 0 . 6 3 0 0 ; ∗ 17 ∗ 10−3 1−5 0 . 8 4 0 0 ; ∗ 18 ∗ 10−3 1−5 0 . 9 7 0 0 ; ∗ 19 ∗ 10−3 1−5
”∗ 20 ∗ 10−3 1−5 0 . 9 6 0 0 ; ∗ 21 ∗ 10−3 1−5 0 . 8 9 0 0 ; ∗ 22 ∗ 10−3 1−5 0 . 7 4 0 0 ; ∗ 23 ∗ 10−3 1−5
”}”
” weekend−w i n t e r {”
”∗
0 ∗ 10−3 6−0 0 . 4 9 0 0 ; ∗
1 ∗ 10−3 6−0 0 . 4 2 0 0 ; ∗
2 ∗ 10−3 6−0 0 . 3 8 0 0 ; ∗
3 ∗ 10−3 6−0
”∗
4 ∗ 10−3 6−0 0 . 3 7 0 0 ; ∗
5 ∗ 10−3 6−0 0 . 3 8 0 0 ; ∗
6 ∗ 10−3 6−0 0 . 4 3 0 0 ; ∗
7 ∗ 10−3 6−0
”∗
8 ∗ 10−3 6−0 0 . 6 0 0 0 ; ∗
9 ∗ 10−3 6−0 0 . 6 3 0 0 ; ∗ 10 ∗ 10−3 6−0 0 . 6 3 0 0 ; ∗ 11 ∗ 10−3 6−0
”∗ 12 ∗ 10−3 6−0 0 . 6 0 0 0 ; ∗ 13 ∗ 10−3 6−0 0 . 5 9 0 0 ; ∗ 14 ∗ 10−3 6−0 0 . 5 9 0 0 ; ∗ 15 ∗ 10−3 6−0
”∗ 16 ∗ 10−3 6−0 0 . 7 1 0 0 ; ∗ 17 ∗ 10−3 6−0 0 . 8 8 0 0 ; ∗ 18 ∗ 10−3 6−0 0 . 9 6 0 0 ; ∗ 19 ∗ 10−3 6−0
”∗ 20 ∗ 10−3 6−0 0 . 9 4 0 0 ; ∗ 21 ∗ 10−3 6−0 0 . 8 8 0 0 ; ∗ 22 ∗ 10−3 6−0 0 . 7 6 0 0 ; ∗ 23 ∗ 10−3 6−0
”}”
},
// P l u g s ( s o u r c e : ELCAP l i t −s p . d a t )
”PLUGS” ,
{30 , f a l s e , { 0 . 0 , 0 . 0 , 1 . 0 } , 0 . 9 0 , 0 . 9 } ,
” t y p e : a n a l o g ; s c h e d u l e : r e s i d e n t i a l −p l u g s −d e f a u l t ; power : 1 . 5 kW” , // 50% more l o a d
” r e s i d e n t i a l −p l u g s −d e f a u l t ” ,
” p o s i t i v e ; n o n z e r o ; weekday−summer {”
”∗
0 ∗ 4−9 1−5 0 . 3 8 0 ; ∗
1 ∗ 4−9 1−5 0 . 3 4 0 ; ∗
2 ∗ 4−9 1−5 0 . 3 2 0 ; ∗
3 ∗ 4−9 1−5 0 . 3 2 0 ; ”
”∗
4 ∗ 4−9 1−5 0 . 3 2 0 ; ∗
5 ∗ 4−9 1−5 0 . 3 5 0 ; ∗
6 ∗ 4−9 1−5 0 . 4 1 0 ; ∗
7 ∗ 4−9 1−5 0 . 4 5 0 ; ”
”∗
8 ∗ 4−9 1−5 0 . 4 5 0 ; ∗
9 ∗ 4−9 1−5 0 . 4 5 0 ; ∗ 10 ∗ 4−9 1−5 0 . 4 5 0 ; ∗ 11 ∗ 4−9 1−5 0 . 4 5 0 ; ”
”∗ 12 ∗ 4−9 1−5 0 . 4 5 0 ; ∗ 13 ∗ 4−9 1−5 0 . 4 4 0 ; ∗ 14 ∗ 4−9 1−5 0 . 4 4 0 ; ∗ 15 ∗ 4−9 1−5 0 . 4 5 0 ; ”
”∗ 16 ∗ 4−9 1−5 0 . 4 7 0 ; ∗ 17 ∗ 4−9 1−5 0 . 5 1 0 ; ∗ 18 ∗ 4−9 1−5 0 . 5 4 0 ; ∗ 19 ∗ 4−9 1−5 0 . 5 6 0 ; ”
”∗ 20 ∗ 4−9 1−5 0 . 6 3 0 ; ∗ 21 ∗ 4−9 1−5 0 . 7 1 0 ; ∗ 22 ∗ 4−9 1−5 0 . 6 5 0 ; ∗ 23 ∗ 4−9 1−5 0 . 4 9 0 ”
”}”
” weekend−summer {”
”∗
0 ∗ 4−9 6−0 0 . 4 1 0 ; ∗
1 ∗ 4−9 6−0 0 . 3 6 0 ; ∗
2 ∗ 4−9 6−0 0 . 3 3 0 ; ∗
3 ∗ 4−9 6−0 0 . 3 2 0 ; ”
”∗
4 ∗ 4−9 6−0 0 . 3 2 0 ; ∗
5 ∗ 4−9 6−0 0 . 3 2 0 ; ∗
6 ∗ 4−9 6−0 0 . 3 4 0 ; ∗
7 ∗ 4−9 6−0 0 . 3 9 0 ; ”
”∗
8 ∗ 4−9 6−0 0 . 4 4 0 ; ∗
9 ∗ 4−9 6−0 0 . 4 7 0 ; ∗ 10 ∗ 4−9 6−0 0 . 4 7 0 ; ∗ 11 ∗ 4−9 6−0 0 . 4 7 0 ; ”
”∗ 12 ∗ 4−9 6−0 0 . 4 7 0 ; ∗ 13 ∗ 4−9 6−0 0 . 4 6 0 ; ∗ 14 ∗ 4−9 6−0 0 . 4 6 0 ; ∗ 15 ∗ 4−9 6−0 0 . 4 6 0 ; ”
”∗ 16 ∗ 4−9 6−0 0 . 4 7 0 ; ∗ 17 ∗ 4−9 6−0 0 . 4 9 0 ; ∗ 18 ∗ 4−9 6−0 0 . 5 2 0 ; ∗ 19 ∗ 4−9 6−0 0 . 5 4 0 ; ”
”∗ 20 ∗ 4−9 6−0 0 . 6 1 0 ; ∗ 21 ∗ 4−9 6−0 0 . 6 8 0 ; ∗ 22 ∗ 4−9 6−0 0 . 6 3 0 ; ∗ 23 ∗ 4−9 6−0 0 . 5 0 0 ”
”}”
” weekday−w i n t e r {”
”∗
0 ∗ 10−3 1−5 0 . 4 2 0 0 ; ∗
1 ∗ 10−3 1−5 0 . 3 8 0 0 ; ∗
2 ∗ 10−3 1−5 0 . 3 7 0 0 ; ∗
3 ∗ 10−3 1−5
”∗
4 ∗ 10−3 1−5 0 . 3 7 0 0 ; ∗
5 ∗ 10−3 1−5 0 . 4 2 0 0 ; ∗
6 ∗ 10−3 1−5 0 . 5 8 0 0 ; ∗
7 ∗ 10−3 1−5
”∗
8 ∗ 10−3 1−5 0 . 6 1 0 0 ; ∗
9 ∗ 10−3 1−5 0 . 5 6 0 0 ; ∗ 10 ∗ 10−3 1−5 0 . 5 3 0 0 ; ∗ 11 ∗ 10−3 1−5
”∗ 12 ∗ 10−3 1−5 0 . 4 9 0 0 ; ∗ 13 ∗ 10−3 1−5 0 . 4 7 0 0 ; ∗ 14 ∗ 10−3 1−5 0 . 4 7 0 0 ; ∗ 15 ∗ 10−3 1−5
”∗ 16 ∗ 10−3 1−5 0 . 6 3 0 0 ; ∗ 17 ∗ 10−3 1−5 0 . 8 4 0 0 ; ∗ 18 ∗ 10−3 1−5 0 . 9 7 0 0 ; ∗ 19 ∗ 10−3 1−5
”∗ 20 ∗ 10−3 1−5 0 . 9 6 0 0 ; ∗ 21 ∗ 10−3 1−5 0 . 8 9 0 0 ; ∗ 22 ∗ 10−3 1−5 0 . 7 4 0 0 ; ∗ 23 ∗ 10−3 1−5
”}”
” weekend−w i n t e r {”
”∗
0 ∗ 10−3 6−0 0 . 4 9 0 0 ; ∗
1 ∗ 10−3 6−0 0 . 4 2 0 0 ; ∗
2 ∗ 10−3 6−0 0 . 3 8 0 0 ; ∗
3 ∗ 10−3 6−0
”∗
4 ∗ 10−3 6−0 0 . 3 7 0 0 ; ∗
5 ∗ 10−3 6−0 0 . 3 8 0 0 ; ∗
6 ∗ 10−3 6−0 0 . 4 3 0 0 ; ∗
7 ∗ 10−3 6−0
”∗
8 ∗ 10−3 6−0 0 . 6 0 0 0 ; ∗
9 ∗ 10−3 6−0 0 . 6 3 0 0 ; ∗ 10 ∗ 10−3 6−0 0 . 6 3 0 0 ; ∗ 11 ∗ 10−3 6−0
”∗ 12 ∗ 10−3 6−0 0 . 6 0 0 0 ; ∗ 13 ∗ 10−3 6−0 0 . 5 9 0 0 ; ∗ 14 ∗ 10−3 6−0 0 . 5 9 0 0 ; ∗ 15 ∗ 10−3 6−0
”∗ 16 ∗ 10−3 6−0 0 . 7 1 0 0 ; ∗ 17 ∗ 10−3 6−0 0 . 8 8 0 0 ; ∗ 18 ∗ 10−3 6−0 0 . 9 6 0 0 ; ∗ 19 ∗ 10−3 6−0
”∗ 20 ∗ 10−3 6−0 0 . 9 4 0 0 ; ∗ 21 ∗ 10−3 6−0 0 . 8 8 0 0 ; ∗ 22 ∗ 10−3 6−0 0 . 7 6 0 0 ; ∗ 23 ∗ 10−3 6−0
”}”
},
”CLOTHESWASHER” ,
{20 , f a l s e , { 0 . 0 , 0 . 0 , 1 . 0 } , 0 . 9 , 1 . 0 } ,
” t y p e : a n a l o g ; s c h e d u l e : r e s i d e n t i a l −c l o t h e s w a s h e r −d e f a u l t ; power : 0 . 5 kW” ,
” r e s i d e n t i a l −c l o t h e s w a s h e r −d e f a u l t ” ,
” p o s i t i v e ; n o n z e r o ; weekday−summer {”
”∗
0 ∗ 4−9 1−5 0 . 0 0 2 9 ; ∗
1 ∗ 4−9 1−5 0 . 0 0 1 9 ; ∗
2 ∗ 4−9 1−5 0 . 0 0 1 4 ; ∗
3
”∗
4 ∗ 4−9 1−5 0 . 0 0 1 8 ; ∗
5 ∗ 4−9 1−5 0 . 0 0 2 6 ; ∗
6 ∗ 4−9 1−5 0 . 0 0 5 5 ; ∗
7
”∗
8 ∗ 4−9 1−5 0 . 0 1 8 1 ; ∗
9 ∗ 4−9 1−5 0 . 0 2 0 8 ; ∗ 10 ∗ 4−9 1−5 0 . 0 2 2 9 ; ∗ 11
”∗ 12 ∗ 4−9 1−5 0 . 0 1 9 3 ; ∗ 13 ∗ 4−9 1−5 0 . 0 1 7 0 ; ∗ 14 ∗ 4−9 1−5 0 . 0 1 4 5 ; ∗ 15
”∗ 16 ∗ 4−9 1−5 0 . 0 1 3 5 ; ∗ 17 ∗ 4−9 1−5 0 . 0 1 4 2 ; ∗ 18 ∗ 4−9 1−5 0 . 0 1 4 5 ; ∗ 19
”∗ 20 ∗ 4−9 1−5 0 . 0 1 4 6 ; ∗ 21 ∗ 4−9 1−5 0 . 0 1 4 1 ; ∗ 22 ∗ 4−9 1−5 0 . 0 1 1 0 ; ∗ 23
”}”
” weekend−summer {”
”∗
0 ∗ 4−9 6−0 0 . 0 0 3 1 ; ∗
1 ∗ 4−9 6−0 0 . 0 0 1 9 ; ∗
2 ∗ 4−9 6−0 0 . 0 0 1 3 ; ∗
3
”∗
4 ∗ 4−9 6−0 0 . 0 0 1 2 ; ∗
5 ∗ 4−9 6−0 0 . 0 0 1 6 ; ∗
6 ∗ 4−9 6−0 0 . 0 0 2 7 ; ∗
7
”∗
8 ∗ 4−9 6−0 0 . 0 1 5 7 ; ∗
9 ∗ 4−9 6−0 0 . 0 2 2 0 ; ∗ 10 ∗ 4−9 6−0 0 . 0 2 5 8 ; ∗ 11
”∗ 12 ∗ 4−9 6−0 0 . 0 2 3 1 ; ∗ 13 ∗ 4−9 6−0 0 . 0 2 1 7 ; ∗ 14 ∗ 4−9 6−0 0 . 0 1 8 6 ; ∗ 15
”∗ 16 ∗ 4−9 6−0 0 . 0 1 5 6 ; ∗ 17 ∗ 4−9 6−0 0 . 0 1 5 1 ; ∗ 18 ∗ 4−9 6−0 0 . 0 1 4 7 ; ∗ 19
”∗ 20 ∗ 4−9 6−0 0 . 0 1 5 6 ; ∗ 21 ∗ 4−9 6−0 0 . 0 1 4 8 ; ∗ 22 ∗ 4−9 6−0 0 . 0 1 0 6 ; ∗ 23
”}”
” weekday−w i n t e r {”
”∗
0 ∗ 10−3 1−5 0 . 0 0 3 6 ; ∗
1 ∗ 10−3 1−5 0 . 0 0 2 4 ; ∗
2 ∗ 10−3 1−5 0 . 0 0 2 0 ; ∗
”∗
4 ∗ 10−3 1−5 0 . 0 0 2 6 ; ∗
5 ∗ 10−3 1−5 0 . 0 0 4 0 ; ∗
6 ∗ 10−3 1−5 0 . 0 0 6 2 ; ∗
”∗
8 ∗ 10−3 1−5 0 . 0 1 7 7 ; ∗
9 ∗ 10−3 1−5 0 . 0 2 1 1 ; ∗ 10 ∗ 10−3 1−5 0 . 0 2 1 5 ; ∗
”∗ 12 ∗ 10−3 1−5 0 . 0 1 7 6 ; ∗ 13 ∗ 10−3 1−5 0 . 0 1 5 5 ; ∗ 14 ∗ 10−3 1−5 0 . 0 1 3 3 ; ∗
”∗ 16 ∗ 10−3 1−5 0 . 0 1 4 5 ; ∗ 17 ∗ 10−3 1−5 0 . 0 1 5 9 ; ∗ 18 ∗ 10−3 1−5 0 . 0 1 6 6 ; ∗
”∗ 20 ∗ 10−3 1−5 0 . 0 1 5 4 ; ∗ 21 ∗ 10−3 1−5 0 . 0 1 4 9 ; ∗ 22 ∗ 10−3 1−5 0 . 0 1 1 0 ; ∗
0.3600;”
0.6900;”
0.5100;”
0.5100;”
0.9800;”
0.5500”
0.3800;”
0.5100;”
0.6100;”
0.6100;”
0.9700;”
0.5800”
0.3600;”
0.6900;”
0.5100;”
0.5100;”
0.9800;”
0.5500”
0.3800;”
0.5100;”
0.6100;”
0.6100;”
0.9700;”
0.5800”
∗
∗
∗
∗
∗
∗
4−9
4−9
4−9
4−9
4−9
4−9
1−5
1−5
1−5
1−5
1−5
1−5
0.0013;”
0.0126;”
0.0216;”
0.0135;”
0.0148;”
0.0062”
∗
∗
∗
∗
∗
∗
4−9
4−9
4−9
4−9
4−9
4−9
6−0
6−0
6−0
6−0
6−0
6−0
0.0012;”
0.0066;”
0.0251;”
0.0157;”
0.0150;”
0.0065”
3
7
11
15
19
23
∗
∗
∗
∗
∗
∗
10−3
10−3
10−3
10−3
10−3
10−3
1−5
1−5
1−5
1−5
1−5
1−5
0.0019;”
0.0118;”
0.0203;”
0.0130;”
0.0164;”
0.0065”
150
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
+
+
+
+
+
+
+
+
+
+
+
+{
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+{
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+{
+
”}”
” weekend−w i n t e r {”
”∗
0 ∗ 10−3 6−0 0 . 0 0 4 4 ;
”∗
4 ∗ 10−3 6−0 0 . 0 0 2 1 ;
”∗
8 ∗ 10−3 6−0 0 . 0 1 4 5 ;
”∗ 12 ∗ 10−3 6−0 0 . 0 3 0 8 ;
”∗ 16 ∗ 10−3 6−0 0 . 0 2 1 5 ;
”∗ 20 ∗ 10−3 6−0 0 . 0 1 8 0 ;
”}”
},
∗
1 ∗ 10−3
∗
5 ∗ 10−3
∗
9 ∗ 10−3
∗ 13 ∗ 10−3
∗ 17 ∗ 10−3
∗ 21 ∗ 10−3
6−0
6−0
6−0
6−0
6−0
6−0
0.0030;
0.0021;
0.0244;
0.0285;
0.0203;
0.0151;
∗
∗
∗
∗
∗
∗
2
6
10
14
18
22
∗
∗
∗
∗
∗
∗
10−3
10−3
10−3
10−3
10−3
10−3
6−0
6−0
6−0
6−0
6−0
6−0
∗
∗
∗
∗
∗
∗
0.0022;
0.0030;
0.0310;
0.0251;
0.0194;
0.0122;
”WATERHEATER” ,
{30 , true , { 0 . 0 , 0 . 0 , 1 . 0 } , 1 . 0 , 0 . 5 } ,
” t y p e : a n a l o g ; s c h e d u l e : r e s i d e n t i a l −w a t e r h e a t e r −d e f a u l t ; power : 0 . 5 6
” r e s i d e n t i a l −w a t e r h e a t e r −d e f a u l t ” ,
” p o s i t i v e ; n o n z e r o ; weekday−summer {”
”∗
0 ∗ 4−9 1−5 0 . 2 1 ; ∗
1 ∗ 4−9 1−5 0 . 1 6 ; ∗
2 ∗ 4−9 1−5 0 . 1 3 ; ∗
3
”∗
4 ∗ 4−9 1−5 0 . 1 5 ; ∗
5 ∗ 4−9 1−5 0 . 2 6 ; ∗
6 ∗ 4−9 1−5 0 . 5 1 ; ∗
7
”∗
8 ∗ 4−9 1−5 0 . 7 7 ; ∗
9 ∗ 4−9 1−5 0 . 7 6 ; ∗ 10 ∗ 4−9 1−5 0 . 7 1 ; ∗ 11
”∗ 12 ∗ 4−9 1−5 0 . 5 4 ; ∗ 13 ∗ 4−9 1−5 0 . 4 9 ; ∗ 14 ∗ 4−9 1−5 0 . 4 3 ; ∗ 15
”∗ 16 ∗ 4−9 1−5 0 . 4 3 ; ∗ 17 ∗ 4−9 1−5 0 . 5 2 ; ∗ 18 ∗ 4−9 1−5 0 . 6 0 ; ∗ 19
”∗ 20 ∗ 4−9 1−5 0 . 5 9 ; ∗ 21 ∗ 4−9 1−5 0 . 6 0 ; ∗ 22 ∗ 4−9 1−5 0 . 5 5 ; ∗ 23
”}”
” weekend−summer {”
”∗
0 ∗ 4−9 6−0 0 . 2 3 ; ∗
1 ∗ 4−9 6−0 0 . 1 7 ; ∗
2 ∗ 4−9 6−0 0 . 1 4 ; ∗
3
”∗
4 ∗ 4−9 6−0 0 . 1 3 ; ∗
5 ∗ 4−9 6−0 0 . 1 7 ; ∗
6 ∗ 4−9 6−0 0 . 2 6 ; ∗
7
”∗
8 ∗ 4−9 6−0 0 . 6 9 ; ∗
9 ∗ 4−9 6−0 0 . 8 5 ; ∗ 10 ∗ 4−9 6−0 0 . 8 4 ; ∗ 11
”∗ 12 ∗ 4−9 6−0 0 . 6 5 ; ∗ 13 ∗ 4−9 6−0 0 . 5 8 ; ∗ 14 ∗ 4−9 6−0 0 . 4 9 ; ∗ 15
”∗ 16 ∗ 4−9 6−0 0 . 4 6 ; ∗ 17 ∗ 4−9 6−0 0 . 5 0 ; ∗ 18 ∗ 4−9 6−0 0 . 5 4 ; ∗ 19
”∗ 20 ∗ 4−9 6−0 0 . 5 6 ; ∗ 21 ∗ 4−9 6−0 0 . 5 6 ; ∗ 22 ∗ 4−9 6−0 0 . 4 9 ; ∗ 23
”}”
” weekday−w i n t e r {”
”∗
0 ∗ 10−3 1−5 0 . 2 5 ; ∗
1 ∗ 10−3 1−5 0 . 1 9 ; ∗
2 ∗ 10−3 1−5 0 . 1 6 ; ∗
”∗
4 ∗ 10−3 1−5 0 . 1 8 ; ∗
5 ∗ 10−3 1−5 0 . 3 4 ; ∗
6 ∗ 10−3 1−5 0 . 7 4 ; ∗
”∗
8 ∗ 10−3 1−5 1 . 1 0 ; ∗
9 ∗ 10−3 1−5 0 . 9 4 ; ∗ 10 ∗ 10−3 1−5 0 . 8 2 ; ∗
”∗ 12 ∗ 10−3 1−5 0 . 6 2 ; ∗ 13 ∗ 10−3 1−5 0 . 5 5 ; ∗ 14 ∗ 10−3 1−5 0 . 4 8 ; ∗
”∗ 16 ∗ 10−3 1−5 0 . 5 4 ; ∗ 17 ∗ 10−3 1−5 0 . 6 8 ; ∗ 18 ∗ 10−3 1−5 0 . 8 3 ; ∗
”∗ 20 ∗ 10−3 1−5 0 . 7 4 ; ∗ 21 ∗ 10−3 1−5 0 . 6 8 ; ∗ 22 ∗ 10−3 1−5 0 . 5 7 ; ∗
”}”
” weekend−w i n t e r {”
”∗
0 ∗ 10−3 6−0 0 . 2 9 ; ∗
1 ∗ 10−3 6−0 0 . 2 2 ; ∗
2 ∗ 10−3 6−0 0 . 1 7 ; ∗
”∗
4 ∗ 10−3 6−0 0 . 1 6 ; ∗
5 ∗ 10−3 6−0 0 . 1 9 ; ∗
6 ∗ 10−3 6−0 0 . 2 7 ; ∗
”∗
8 ∗ 10−3 6−0 0 . 8 2 ; ∗
9 ∗ 10−3 6−0 1 . 0 8 ; ∗ 10 ∗ 10−3 6−0 1 . 1 5 ; ∗
”∗ 12 ∗ 10−3 6−0 0 . 9 8 ; ∗ 13 ∗ 10−3 6−0 0 . 8 7 ; ∗ 14 ∗ 10−3 6−0 0 . 7 7 ; ∗
”∗ 16 ∗ 10−3 6−0 0 . 7 2 ; ∗ 17 ∗ 10−3 6−0 0 . 7 8 ; ∗ 18 ∗ 10−3 6−0 0 . 8 3 ; ∗
”∗ 20 ∗ 10−3 6−0 0 . 7 2 ; ∗ 21 ∗ 10−3 6−0 0 . 6 4 ; ∗ 22 ∗ 10−3 6−0 0 . 5 3 ; ∗
”}”
},
3
7
11
15
19
23
{0.9 ,0.0 ,0.1} ,
0.99 ,
0.15} ,
10−3
10−3
10−3
10−3
10−3
10−3
6−0
6−0
6−0
6−0
6−0
6−0
0.0020;”
0.0067;”
0.0323;”
0.0224;”
0.0188;”
0.0073”
kW” ,
∗
∗
∗
∗
∗
∗
4−9
4−9
4−9
4−9
4−9
4−9
1−5
1−5
1−5
1−5
1−5
1−5
0.12;”
0.76;”
0.61;”
0.41;”
0.60;”
0.37”
∗
∗
∗
∗
∗
∗
4−9
4−9
4−9
4−9
4−9
4−9
6−0
6−0
6−0
6−0
6−0
6−0
0.13;”
0.45;”
0.76;”
0.46;”
0.55;”
0.38”
3
7
11
15
19
23
∗
∗
∗
∗
∗
∗
10−3
10−3
10−3
10−3
10−3
10−3
1−5
1−5
1−5
1−5
1−5
1−5
0.15;”
1.20;”
0.71;”
0.47;”
0.82;”
0.40”
3
7
11
15
19
23
∗
∗
∗
∗
∗
∗
10−3
10−3
10−3
10−3
10−3
10−3
6−0
6−0
6−0
6−0
6−0
6−0
0.15;”
0.47;”
1.08;”
0.69;”
0.79;”
0.43”
”REFRIGERATOR” ,
{20 , f a l s e , { 0 . 1 , 0 . 0 , 0 . 9 } , 0 . 9 , 1 . 0 } ,
” t y p e : a n a l o g ; s c h e d u l e : r e s i d e n t i a l −r e f r i g e r a t o r −d e f a u l t ; power : 0 . 4 3 kW” ,
” r e s i d e n t i a l −r e f r i g e r a t o r −d e f a u l t ” ,
” p o s i t i v e ; n o n z e r o ; weekday−summer {”
”∗
0 ∗ 4−9 1−5 0 . 1 8 7 ; ∗
1 ∗ 4−9 1−5 0 . 1 8 2 ; ∗
2 ∗ 4−9 1−5 0 . 1 7 6 ; ∗
3 ∗ 4−9
”∗
4 ∗ 4−9 1−5 0 . 1 6 8 ; ∗
5 ∗ 4−9 1−5 0 . 1 6 8 ; ∗
6 ∗ 4−9 1−5 0 . 1 7 7 ; ∗
7 ∗ 4−9
”∗
8 ∗ 4−9 1−5 0 . 1 7 7 ; ∗
9 ∗ 4−9 1−5 0 . 1 8 0 ; ∗ 10 ∗ 4−9 1−5 0 . 1 8 0 ; ∗ 11 ∗ 4−9
”∗ 12 ∗ 4−9 1−5 0 . 1 9 2 ; ∗ 13 ∗ 4−9 1−5 0 . 1 9 2 ; ∗ 14 ∗ 4−9 1−5 0 . 1 9 4 ; ∗ 15 ∗ 4−9
”∗ 16 ∗ 4−9 1−5 0 . 2 0 5 ; ∗ 17 ∗ 4−9 1−5 0 . 2 1 7 ; ∗ 18 ∗ 4−9 1−5 0 . 2 2 5 ; ∗ 19 ∗ 4−9
”∗ 20 ∗ 4−9 1−5 0 . 2 1 6 ; ∗ 21 ∗ 4−9 1−5 0 . 2 1 4 ; ∗ 22 ∗ 4−9 1−5 0 . 2 0 7 ; ∗ 23 ∗ 4−9
”}”
” weekend−summer {”
”∗
0 ∗ 4−9 6−0 0 . 1 8 7 ; ∗
1 ∗ 4−9 6−0 0 . 1 8 1 ; ∗
2 ∗ 4−9 6−0 0 . 1 7 6 ; ∗
3 ∗ 4−9
”∗
4 ∗ 4−9 6−0 0 . 1 6 6 ; ∗
5 ∗ 4−9 6−0 0 . 1 6 4 ; ∗
6 ∗ 4−9 6−0 0 . 1 6 7 ; ∗
7 ∗ 4−9
”∗
8 ∗ 4−9 6−0 0 . 1 8 0 ; ∗
9 ∗ 4−9 6−0 0 . 1 8 4 ; ∗ 10 ∗ 4−9 6−0 0 . 1 8 7 ; ∗ 11 ∗ 4−9
”∗ 12 ∗ 4−9 6−0 0 . 1 9 5 ; ∗ 13 ∗ 4−9 6−0 0 . 2 0 0 ; ∗ 14 ∗ 4−9 6−0 0 . 2 0 1 ; ∗ 15 ∗ 4−9
”∗ 16 ∗ 4−9 6−0 0 . 2 0 9 ; ∗ 17 ∗ 4−9 6−0 0 . 2 1 8 ; ∗ 18 ∗ 4−9 6−0 0 . 2 2 2 ; ∗ 19 ∗ 4−9
”∗ 20 ∗ 4−9 6−0 0 . 2 1 7 ; ∗ 21 ∗ 4−9 6−0 0 . 2 1 6 ; ∗ 22 ∗ 4−9 6−0 0 . 2 0 7 ; ∗ 23 ∗ 4−9
”}”
” weekday−w i n t e r {”
”∗
0 ∗ 10−3 1−5 0 . 1 5 3 0 ; ∗
1 ∗ 10−3 1−5 0 . 1 5 0 0 ; ∗
2 ∗ 10−3 1−5 0 . 1 4 6 0 ; ∗
3
”∗
4 ∗ 10−3 1−5 0 . 1 4 0 0 ; ∗
5 ∗ 10−3 1−5 0 . 1 4 5 0 ; ∗
6 ∗ 10−3 1−5 0 . 1 5 2 0 ; ∗
7
”∗
8 ∗ 10−3 1−5 0 . 1 5 8 0 ; ∗
9 ∗ 10−3 1−5 0 . 1 5 8 0 ; ∗ 10 ∗ 10−3 1−5 0 . 1 5 6 0 ; ∗ 11
”∗ 12 ∗ 10−3 1−5 0 . 1 6 3 0 ; ∗ 13 ∗ 10−3 1−5 0 . 1 6 2 0 ; ∗ 14 ∗ 10−3 1−5 0 . 1 5 9 0 ; ∗ 15
”∗ 16 ∗ 10−3 1−5 0 . 1 6 9 0 ; ∗ 17 ∗ 10−3 1−5 0 . 1 8 5 0 ; ∗ 18 ∗ 10−3 1−5 0 . 1 9 2 0 ; ∗ 19
”∗ 20 ∗ 10−3 1−5 0 . 1 8 0 0 ; ∗ 21 ∗ 10−3 1−5 0 . 1 7 6 0 ; ∗ 22 ∗ 10−3 1−5 0 . 1 6 7 0 ; ∗ 23
”}”
” weekend−w i n t e r {”
”∗
0 ∗ 10−3 6−0 0 . 1 5 6 0 ; ∗
1 ∗ 10−3 6−0 0 . 1 5 2 0 ; ∗
2 ∗ 10−3 6−0 0 . 1 4 7 0 ; ∗
3
”∗
4 ∗ 10−3 6−0 0 . 1 4 2 0 ; ∗
5 ∗ 10−3 6−0 0 . 1 4 3 0 ; ∗
6 ∗ 10−3 6−0 0 . 1 4 3 0 ; ∗
7
”∗
8 ∗ 10−3 6−0 0 . 1 6 1 0 ; ∗
9 ∗ 10−3 6−0 0 . 1 6 9 0 ; ∗ 10 ∗ 10−3 6−0 0 . 1 6 7 0 ; ∗ 11
”∗ 12 ∗ 10−3 6−0 0 . 1 7 4 0 ; ∗ 13 ∗ 10−3 6−0 0 . 1 7 6 0 ; ∗ 14 ∗ 10−3 6−0 0 . 1 7 4 0 ; ∗ 15
”∗ 16 ∗ 10−3 6−0 0 . 1 7 9 0 ; ∗ 17 ∗ 10−3 6−0 0 . 1 9 1 0 ; ∗ 18 ∗ 10−3 6−0 0 . 1 9 3 0 ; ∗ 19
”∗ 20 ∗ 10−3 6−0 0 . 1 8 4 0 ; ∗ 21 ∗ 10−3 6−0 0 . 1 7 8 0 ; ∗ 22 ∗ 10−3 6−0 0 . 1 7 0 0 ; ∗ 23
”}”
},
”DRYER” ,
{30 , true ,
∗
∗
∗
∗
∗
∗
1−5
1−5
1−5
1−5
1−5
1−5
0.170;”
0.174;”
0.183;”
0.196;”
0.221;”
0.195”
6−0
6−0
6−0
6−0
6−0
6−0
0.169;”
0.169;”
0.187;”
0.203;”
0.221;”
0.196”
∗
∗
∗
∗
∗
∗
10−3
10−3
10−3
10−3
10−3
10−3
1−5
1−5
1−5
1−5
1−5
1−5
0.1420;”
0.1600;”
0.1560;”
0.1620;”
0.1820;”
0.1590”
∗
∗
∗
∗
∗
∗
10−3
10−3
10−3
10−3
10−3
10−3
6−0
6−0
6−0
6−0
6−0
6−0
0.1430;”
0.1500;”
0.1660;”
0.1750;”
0.1870;”
0.1600”
151
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
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394
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398
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403
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411
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419
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431
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434
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436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+{
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+{
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
” t y p e : a n a l o g ; s c h e d u l e : r e s i d e n t i a l −d r y e r −d e f a u l t ; power : 0 . 6 4 kW” ,
” r e s i d e n t i a l −d r y e r −d e f a u l t ” ,
” p o s i t i v e ; n o n z e r o ; weekday−summer {”
”∗
0 ∗ 4−9 1−5 0 . 0 3 6 ; ∗
1 ∗ 4−9 1−5 0 . 0 1 3 ; ∗
2 ∗ 4−9 1−5 0 . 0 0 7 ; ∗
3 ∗ 4−9
”∗
4 ∗ 4−9 1−5 0 . 0 0 5 ; ∗
5 ∗ 4−9 1−5 0 . 0 1 7 ; ∗
6 ∗ 4−9 1−5 0 . 0 4 8 ; ∗
7 ∗ 4−9
”∗
8 ∗ 4−9 1−5 0 . 1 1 5 ; ∗
9 ∗ 4−9 1−5 0 . 1 5 6 ; ∗ 10 ∗ 4−9 1−5 0 . 1 7 9 ; ∗ 11 ∗ 4−9
”∗ 12 ∗ 4−9 1−5 0 . 1 7 2 ; ∗ 13 ∗ 4−9 1−5 0 . 1 6 2 ; ∗ 14 ∗ 4−9 1−5 0 . 1 4 5 ; ∗ 15 ∗ 4−9
”∗ 16 ∗ 4−9 1−5 0 . 1 3 3 ; ∗ 17 ∗ 4−9 1−5 0 . 1 3 4 ; ∗ 18 ∗ 4−9 1−5 0 . 1 2 7 ; ∗ 19 ∗ 4−9
”∗ 20 ∗ 4−9 1−5 0 . 1 4 1 ; ∗ 21 ∗ 4−9 1−5 0 . 1 5 4 ; ∗ 22 ∗ 4−9 1−5 0 . 1 3 8 ; ∗ 23 ∗ 4−9
”}”
” weekend−summer {”
”∗
0 ∗ 4−9 6−0 0 . 0 4 1 ; ∗
1 ∗ 4−9 6−0 0 . 0 1 7 ; ∗
2 ∗ 4−9 6−0 0 . 0 0 8 ; ∗
3 ∗ 4−9
”∗
4 ∗ 4−9 6−0 0 . 0 0 5 ; ∗
5 ∗ 4−9 6−0 0 . 0 0 7 ; ∗
6 ∗ 4−9 6−0 0 . 0 1 8 ; ∗
7 ∗ 4−9
”∗
8 ∗ 4−9 6−0 0 . 1 0 0 ; ∗
9 ∗ 4−9 6−0 0 . 1 6 8 ; ∗ 10 ∗ 4−9 6−0 0 . 2 0 5 ; ∗ 11 ∗ 4−9
”∗ 12 ∗ 4−9 6−0 0 . 2 1 1 ; ∗ 13 ∗ 4−9 6−0 0 . 2 1 0 ; ∗ 14 ∗ 4−9 6−0 0 . 1 8 8 ; ∗ 15 ∗ 4−9
”∗ 16 ∗ 4−9 6−0 0 . 1 5 4 ; ∗ 17 ∗ 4−9 6−0 0 . 1 4 6 ; ∗ 18 ∗ 4−9 6−0 0 . 1 3 8 ; ∗ 19 ∗ 4−9
”∗ 20 ∗ 4−9 6−0 0 . 1 4 4 ; ∗ 21 ∗ 4−9 6−0 0 . 1 5 5 ; ∗ 22 ∗ 4−9 6−0 0 . 1 3 1 ; ∗ 23 ∗ 4−9
”}”
” weekday−w i n t e r {”
”∗
0 ∗ 10−3 1−5 0 . 0 3 6 0 ; ∗
1 ∗ 10−3 1−5 0 . 0 1 6 0 ; ∗
2 ∗ 10−3 1−5 0 . 0 1 0 0 ; ∗
3
”∗
4 ∗ 10−3 1−5 0 . 0 0 9 0 ; ∗
5 ∗ 10−3 1−5 0 . 0 2 3 0 ; ∗
6 ∗ 10−3 1−5 0 . 0 6 1 0 ; ∗
7
”∗
8 ∗ 10−3 1−5 0 . 1 3 2 0 ; ∗
9 ∗ 10−3 1−5 0 . 1 7 5 0 ; ∗ 10 ∗ 10−3 1−5 0 . 2 0 5 0 ; ∗ 11
”∗ 12 ∗ 10−3 1−5 0 . 1 9 4 0 ; ∗ 13 ∗ 10−3 1−5 0 . 1 7 7 0 ; ∗ 14 ∗ 10−3 1−5 0 . 1 6 1 0 ; ∗ 15
”∗ 16 ∗ 10−3 1−5 0 . 1 6 4 0 ; ∗ 17 ∗ 10−3 1−5 0 . 1 7 1 0 ; ∗ 18 ∗ 10−3 1−5 0 . 1 6 1 0 ; ∗ 19
”∗ 20 ∗ 10−3 1−5 0 . 1 6 7 0 ; ∗ 21 ∗ 10−3 1−5 0 . 1 6 9 0 ; ∗ 22 ∗ 10−3 1−5 0 . 1 3 8 0 ; ∗ 23
”}”
” weekend−w i n t e r {”
”∗
0 ∗ 10−3 6−0 0 . 0 3 9 0 ; ∗
1 ∗ 10−3 6−0 0 . 0 1 9 0 ; ∗
2 ∗ 10−3 6−0 0 . 0 1 1 0 ; ∗
3
”∗
4 ∗ 10−3 6−0 0 . 0 0 8 0 ; ∗
5 ∗ 10−3 6−0 0 . 0 0 9 0 ; ∗
6 ∗ 10−3 6−0 0 . 0 1 6 0 ; ∗
7
”∗
8 ∗ 10−3 6−0 0 . 1 0 1 0 ; ∗
9 ∗ 10−3 6−0 0 . 1 8 1 0 ; ∗ 10 ∗ 10−3 6−0 0 . 2 6 4 0 ; ∗ 11
”∗ 12 ∗ 10−3 6−0 0 . 3 1 1 0 ; ∗ 13 ∗ 10−3 6−0 0 . 3 0 6 0 ; ∗ 14 ∗ 10−3 6−0 0 . 2 8 5 0 ; ∗ 15
”∗ 16 ∗ 10−3 6−0 0 . 2 6 0 0 ; ∗ 17 ∗ 10−3 6−0 0 . 2 4 5 0 ; ∗ 18 ∗ 10−3 6−0 0 . 2 2 0 0 ; ∗ 19
”∗ 20 ∗ 10−3 6−0 0 . 1 8 8 0 ; ∗ 21 ∗ 10−3 6−0 0 . 1 7 9 0 ; ∗ 22 ∗ 10−3 6−0 0 . 1 4 8 0 ; ∗ 23
”}”
},
”FREEZER” ,
{20 , f a l s e , { 0 . 1 , 0 . 0 , 0 . 9 } , 0 . 9 , 1 . 0 } ,
” t y p e : a n a l o g ; s c h e d u l e : r e s i d e n t i a l −f r e e z e r −d e f a u l t ; power : 0 . 3 8 kW” ,
” r e s i d e n t i a l −f r e e z e r −d e f a u l t ” ,
” p o s i t i v e ; n o n z e r o ; weekday−summer {”
”∗
0 ∗ 4−9 1−5 0 . 2 1 0 ; ∗
1 ∗ 4−9 1−5 0 . 2 1 3 ; ∗
2 ∗ 4−9 1−5 0 . 2 0 8 ; ∗
3
”∗
4 ∗ 4−9 1−5 0 . 2 0 3 ; ∗
5 ∗ 4−9 1−5 0 . 1 9 8 ; ∗
6 ∗ 4−9 1−5 0 . 1 9 0 ; ∗
7
”∗
8 ∗ 4−9 1−5 0 . 1 8 9 ; ∗
9 ∗ 4−9 1−5 0 . 1 9 4 ; ∗ 10 ∗ 4−9 1−5 0 . 1 9 9 ; ∗ 11
”∗ 12 ∗ 4−9 1−5 0 . 2 1 1 ; ∗ 13 ∗ 4−9 1−5 0 . 2 1 4 ; ∗ 14 ∗ 4−9 1−5 0 . 2 1 9 ; ∗ 15
”∗ 16 ∗ 4−9 1−5 0 . 2 3 0 ; ∗ 17 ∗ 4−9 1−5 0 . 2 2 8 ; ∗ 18 ∗ 4−9 1−5 0 . 2 2 9 ; ∗ 19
”∗ 20 ∗ 4−9 1−5 0 . 2 2 4 ; ∗ 21 ∗ 4−9 1−5 0 . 2 2 3 ; ∗ 22 ∗ 4−9 1−5 0 . 2 1 8 ; ∗ 23
”}”
” weekend−summer {”
”∗
0 ∗ 4−9 6−0 0 . 2 0 3 ; ∗
1 ∗ 4−9 6−0 0 . 2 0 2 ; ∗
2 ∗ 4−9 6−0 0 . 2 0 2 ; ∗
3
”∗
4 ∗ 4−9 6−0 0 . 1 9 8 ; ∗
5 ∗ 4−9 6−0 0 . 1 9 5 ; ∗
6 ∗ 4−9 6−0 0 . 1 9 1 ; ∗
7
”∗
8 ∗ 4−9 6−0 0 . 1 8 4 ; ∗
9 ∗ 4−9 6−0 0 . 1 9 2 ; ∗ 10 ∗ 4−9 6−0 0 . 1 9 7 ; ∗ 11
”∗ 12 ∗ 4−9 6−0 0 . 2 0 8 ; ∗ 13 ∗ 4−9 6−0 0 . 2 1 9 ; ∗ 14 ∗ 4−9 6−0 0 . 2 1 9 ; ∗ 15
”∗ 16 ∗ 4−9 6−0 0 . 2 2 5 ; ∗ 17 ∗ 4−9 6−0 0 . 2 2 5 ; ∗ 18 ∗ 4−9 6−0 0 . 2 2 3 ; ∗ 19
”∗ 20 ∗ 4−9 6−0 0 . 2 2 1 ; ∗ 21 ∗ 4−9 6−0 0 . 2 2 0 ; ∗ 22 ∗ 4−9 6−0 0 . 2 1 5 ; ∗ 23
”}”
” weekday−w i n t e r {”
”∗
0 ∗ 10−3 1−5 0 . 1 4 9 ; ∗
1 ∗ 10−3 1−5 0 . 1 4 8 ; ∗
2 ∗ 10−3 1−5 0 . 1 4 5 ; ∗
”∗
4 ∗ 10−3 1−5 0 . 1 4 3 ; ∗
5 ∗ 10−3 1−5 0 . 1 4 0 ; ∗
6 ∗ 10−3 1−5 0 . 1 3 8 ; ∗
”∗
8 ∗ 10−3 1−5 0 . 1 4 0 ; ∗
9 ∗ 10−3 1−5 0 . 1 4 1 ; ∗ 10 ∗ 10−3 1−5 0 . 1 4 2 ; ∗
”∗ 12 ∗ 10−3 1−5 0 . 1 5 3 ; ∗ 13 ∗ 10−3 1−5 0 . 1 5 4 ; ∗ 14 ∗ 10−3 1−5 0 . 1 5 2 ; ∗
”∗ 16 ∗ 10−3 1−5 0 . 1 6 1 ; ∗ 17 ∗ 10−3 1−5 0 . 1 7 4 ; ∗ 18 ∗ 10−3 1−5 0 . 1 7 6 ; ∗
”∗ 20 ∗ 10−3 1−5 0 . 1 7 5 ; ∗ 21 ∗ 10−3 1−5 0 . 1 6 9 ; ∗ 22 ∗ 10−3 1−5 0 . 1 6 0 ; ∗
”}”
” weekend−w i n t e r {”
”∗
0 ∗ 10−3 6−0 0 . 1 5 5 ; ∗
1 ∗ 10−3 6−0 0 . 1 5 0 ; ∗
2 ∗ 10−3 6−0 0 . 1 4 3 ; ∗
”∗
4 ∗ 10−3 6−0 0 . 1 4 1 ; ∗
5 ∗ 10−3 6−0 0 . 1 3 9 ; ∗
6 ∗ 10−3 6−0 0 . 1 3 8 ; ∗
”∗
8 ∗ 10−3 6−0 0 . 1 4 2 ; ∗
9 ∗ 10−3 6−0 0 . 1 4 2 ; ∗ 10 ∗ 10−3 6−0 0 . 1 4 5 ; ∗
”∗ 12 ∗ 10−3 6−0 0 . 1 6 1 ; ∗ 13 ∗ 10−3 6−0 0 . 1 6 2 ; ∗ 14 ∗ 10−3 6−0 0 . 1 6 0 ; ∗
”∗ 16 ∗ 10−3 6−0 0 . 1 6 5 ; ∗ 17 ∗ 10−3 6−0 0 . 1 7 7 ; ∗ 18 ∗ 10−3 6−0 0 . 1 7 9 ; ∗
”∗ 20 ∗ 10−3 6−0 0 . 1 7 1 ; ∗ 21 ∗ 10−3 6−0 0 . 1 6 8 ; ∗ 22 ∗ 10−3 6−0 0 . 1 6 0 ; ∗
”}”
},
”DISHWASHER” ,
{20 , f a l s e , { 0 . 8 , 0 , 0 . 2 } , 0 . 9 8 , 1 . 0 } ,
” t y p e : a n a l o g ; s c h e d u l e : r e s i d e n t i a l −d i s h w a s h e r −d e f a u l t ; power : 1 . 8 kW” ,
” r e s i d e n t i a l −d i s h w a s h e r −d e f a u l t ” ,
” normal ; p o s i t i v e ; n o n z e r o ; weekday−summer {”
”∗
0 ∗ 4−9 1−5 0 . 0 0 6 8 ; ∗
1 ∗ 4−9 1−5 0 . 0 0 2 9 ; ∗
2 ∗ 4−9 1−5 0 . 0 0 1 6 ; ∗
”∗
4 ∗ 4−9 1−5 0 . 0 0 1 2 ; ∗
5 ∗ 4−9 1−5 0 . 0 0 3 7 ; ∗
6 ∗ 4−9 1−5 0 . 0 0 7 5 ; ∗
”∗
8 ∗ 4−9 1−5 0 . 0 1 8 0 ; ∗
9 ∗ 4−9 1−5 0 . 0 1 7 7 ; ∗ 10 ∗ 4−9 1−5 0 . 0 1 4 4 ; ∗
”∗ 12 ∗ 4−9 1−5 0 . 0 1 1 6 ; ∗ 13 ∗ 4−9 1−5 0 . 0 1 2 8 ; ∗ 14 ∗ 4−9 1−5 0 . 0 1 0 9 ; ∗
”∗ 16 ∗ 4−9 1−5 0 . 0 1 2 4 ; ∗ 17 ∗ 4−9 1−5 0 . 0 1 5 6 ; ∗ 18 ∗ 4−9 1−5 0 . 0 2 7 8 ; ∗
”∗ 20 ∗ 4−9 1−5 0 . 0 2 7 9 ; ∗ 21 ∗ 4−9 1−5 0 . 0 2 3 4 ; ∗ 22 ∗ 4−9 1−5 0 . 0 1 9 4 ; ∗
”}”
” weekend−summer {”
”∗
0 ∗ 4−9 6−0 0 . 0 0 9 3 ; ∗
1 ∗ 4−9 6−0 0 . 0 0 4 5 ; ∗
2 ∗ 4−9 6−0 0 . 0 0 2 1 ; ∗
”∗
4 ∗ 4−9 6−0 0 . 0 0 1 3 ; ∗
5 ∗ 4−9 6−0 0 . 0 0 1 5 ; ∗
6 ∗ 4−9 6−0 0 . 0 0 2 6 ; ∗
”∗
8 ∗ 4−9 6−0 0 . 0 1 4 2 ; ∗
9 ∗ 4−9 6−0 0 . 0 2 2 1 ; ∗ 10 ∗ 4−9 6−0 0 . 0 2 5 9 ; ∗
1−5
1−5
1−5
1−5
1−5
1−5
0.005;”
0.085;”
0.185;”
0.136;”
0.130;”
0.083”
6−0
6−0
6−0
6−0
6−0
6−0
0.005;”
0.047;”
0.220;”
0.168;”
0.137;”
0.081”
∗
∗
∗
∗
∗
∗
10−3
10−3
10−3
10−3
10−3
10−3
1−5
1−5
1−5
1−5
1−5
1−5
0.0070;”
0.1030;”
0.2130;”
0.1560;”
0.1590;”
0.0820”
∗
∗
∗
∗
∗
∗
10−3
10−3
10−3
10−3
10−3
10−3
6−0
6−0
6−0
6−0
6−0
6−0
0.0070;”
0.0430;”
0.3050;”
0.2700;”
0.1980;”
0.0930”
∗
∗
∗
∗
∗
∗
4−9
4−9
4−9
4−9
4−9
4−9
1−5
1−5
1−5
1−5
1−5
1−5
0.202;”
0.186;”
0.202;”
0.222;”
0.223;”
0.214”
∗
∗
∗
∗
∗
∗
4−9
4−9
4−9
4−9
4−9
4−9
6−0
6−0
6−0
6−0
6−0
6−0
0.193;”
0.183;”
0.202;”
0.225;”
0.219;”
0.209”
3
7
11
15
19
23
∗
∗
∗
∗
∗
∗
10−3
10−3
10−3
10−3
10−3
10−3
1−5
1−5
1−5
1−5
1−5
1−5
0.144;”
0.138;”
0.147;”
0.151;”
0.176;”
0.153”
3
7
11
15
19
23
∗
∗
∗
∗
∗
∗
10−3
10−3
10−3
10−3
10−3
10−3
6−0
6−0
6−0
6−0
6−0
6−0
0.141;”
0.139;”
0.153;”
0.161;”
0.177;”
0.151”
3
7
11
15
19
23
∗
∗
∗
∗
∗
∗
4−9
4−9
4−9
4−9
4−9
4−9
1−5
1−5
1−5
1−5
1−5
1−5
0.0013;”
0.0129;”
0.0113;”
0.0105;”
0.0343;”
0.0131”
3 ∗ 4−9 6−0 0 . 0 0 1 5 ; ”
7 ∗ 4−9 6−0 0 . 0 0 6 7 ; ”
11 ∗ 4−9 6−0 0 . 0 2 3 8 ; ”
152
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+} ,
+{
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+} ,
+{
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
”∗ 12 ∗ 4−9 6−0 0 . 0 2 1 4 ; ∗ 13 ∗ 4−9 6−0 0 . 0 2 1 4 ; ∗
”∗ 16 ∗ 4−9 6−0 0 . 0 1 5 6 ; ∗ 17 ∗ 4−9 6−0 0 . 0 1 6 6 ; ∗
”∗ 20 ∗ 4−9 6−0 0 . 0 2 6 7 ; ∗ 21 ∗ 4−9 6−0 0 . 0 2 2 1 ; ∗
”}”
” weekday−w i n t e r {”
”∗
0 ∗ 10−3 1−5 0 . 0 0 6 8 ; ∗
1 ∗ 10−3 1−5 0 . 0 0 2 9 ;
”∗
4 ∗ 10−3 1−5 0 . 0 0 1 2 ; ∗
5 ∗ 10−3 1−5 0 . 0 0 3 7 ;
”∗
8 ∗ 10−3 1−5 0 . 0 1 8 0 ; ∗
9 ∗ 10−3 1−5 0 . 0 1 7 7 ;
”∗ 12 ∗ 10−3 1−5 0 . 0 1 1 6 ; ∗ 13 ∗ 10−3 1−5 0 . 0 1 2 8 ;
”∗ 16 ∗ 10−3 1−5 0 . 0 1 2 4 ; ∗ 17 ∗ 10−3 1−5 0 . 0 1 5 6 ;
”∗ 20 ∗ 10−3 1−5 0 . 0 2 7 9 ; ∗ 21 ∗ 10−3 1−5 0 . 0 2 3 4 ;
”}”
” weekend−w i n t e r {”
”∗
0 ∗ 10−3 6−0 0 . 0 0 9 3 ; ∗
1 ∗ 10−3 6−0 0 . 0 0 4 5 ;
”∗
4 ∗ 10−3 6−0 0 . 0 0 1 3 ; ∗
5 ∗ 10−3 6−0 0 . 0 0 1 5 ;
”∗
8 ∗ 10−3 6−0 0 . 0 1 4 2 ; ∗
9 ∗ 10−3 6−0 0 . 0 2 2 1 ;
”∗ 12 ∗ 10−3 6−0 0 . 0 2 1 4 ; ∗ 13 ∗ 10−3 6−0 0 . 0 2 1 4 ;
”∗ 16 ∗ 10−3 6−0 0 . 0 1 5 6 ; ∗ 17 ∗ 10−3 6−0 0 . 0 1 6 6 ;
”∗ 20 ∗ 10−3 6−0 0 . 0 2 6 7 ; ∗ 21 ∗ 10−3 6−0 0 . 0 2 2 1 ;
”}”
14 ∗ 4−9 6−0 0 . 0 1 8 8 ; ∗ 15 ∗ 4−9 6−0 0 . 0 1 6 9 ; ”
18 ∗ 4−9 6−0 0 . 0 2 4 9 ; ∗ 19 ∗ 4−9 6−0 0 . 0 2 9 8 ; ”
22 ∗ 4−9 6−0 0 . 0 1 7 4 ; ∗ 23 ∗ 4−9 6−0 0 . 0 1 4 5 ”
∗
∗
∗
∗
∗
∗
2
6
10
14
18
22
∗
∗
∗
∗
∗
∗
10−3
10−3
10−3
10−3
10−3
10−3
1−5
1−5
1−5
1−5
1−5
1−5
0.0016;
0.0075;
0.0144;
0.0109;
0.0278;
0.0194;
∗
∗
∗
∗
∗
∗
3
7
11
15
19
23
∗
∗
∗
∗
∗
∗
10−3
10−3
10−3
10−3
10−3
10−3
1−5
1−5
1−5
1−5
1−5
1−5
0.0013;”
0.0129;”
0.0113;”
0.0105;”
0.0343;”
0.0131”
∗
∗
∗
∗
∗
∗
2
6
10
14
18
22
∗
∗
∗
∗
∗
∗
10−3
10−3
10−3
10−3
10−3
10−3
6−0
6−0
6−0
6−0
6−0
6−0
0.0021;
0.0026;
0.0259;
0.0188;
0.0249;
0.0174;
∗
∗
∗
∗
∗
∗
3
7
11
15
19
23
∗
∗
∗
∗
∗
∗
10−3
10−3
10−3
10−3
10−3
10−3
6−0
6−0
6−0
6−0
6−0
6−0
0.0015;”
0.0067;”
0.0238;”
0.0169;”
0.0298;”
0.0145”
”RANGE” ,
{40 , true , {1 ,0 ,0} , 0 . 8 5 , 0 . 8 } ,
” t y p e : a n a l o g ; s c h e d u l e : r e s i d e n t i a l −r a n g e−d e f a u l t ; power : 0 . 5 6 kW” , // no d a t a
” r e s i d e n t i a l −r a n g e−d e f a u l t ” ,
” p o s i t i v e ; n o n z e r o ; weekday−summer {”
”∗
0 ∗ 4−9 1−5 0 . 0 0 9 ; ∗
1 ∗ 4−9 1−5 0 . 0 0 8 ; ∗
2 ∗ 4−9 1−5 0 . 0 0 7 ; ∗
3 ∗ 4−9 1−5
”∗
4 ∗ 4−9 1−5 0 . 0 0 8 ; ∗
5 ∗ 4−9 1−5 0 . 0 1 2 ; ∗
6 ∗ 4−9 1−5 0 . 0 2 5 ; ∗
7 ∗ 4−9 1−5
”∗
8 ∗ 4−9 1−5 0 . 0 4 4 ; ∗
9 ∗ 4−9 1−5 0 . 0 4 2 ; ∗ 10 ∗ 4−9 1−5 0 . 0 4 2 ; ∗ 11 ∗ 4−9 1−5
”∗ 12 ∗ 4−9 1−5 0 . 0 5 7 ; ∗ 13 ∗ 4−9 1−5 0 . 0 4 6 ; ∗ 14 ∗ 4−9 1−5 0 . 0 4 4 ; ∗ 15 ∗ 4−9 1−5
”∗ 16 ∗ 4−9 1−5 0 . 0 9 4 ; ∗ 17 ∗ 4−9 1−5 0 . 1 6 8 ; ∗ 18 ∗ 4−9 1−5 0 . 1 4 8 ; ∗ 19 ∗ 4−9 1−5
”∗ 20 ∗ 4−9 1−5 0 . 0 5 3 ; ∗ 21 ∗ 4−9 1−5 0 . 0 3 8 ; ∗ 22 ∗ 4−9 1−5 0 . 0 2 3 ; ∗ 23 ∗ 4−9 1−5
”}”
” weekend−summer {”
”∗
0 ∗ 4−9 6−0 0 . 0 0 9 ; ∗
1 ∗ 4−9 6−0 0 . 0 0 7 ; ∗
2 ∗ 4−9 6−0 0 . 0 0 7 ; ∗
3 ∗ 4−9 6−0
”∗
4 ∗ 4−9 6−0 0 . 0 0 7 ; ∗
5 ∗ 4−9 6−0 0 . 0 0 9 ; ∗
6 ∗ 4−9 6−0 0 . 0 1 7 ; ∗
7 ∗ 4−9 6−0
”∗
8 ∗ 4−9 6−0 0 . 0 6 0 ; ∗
9 ∗ 4−9 6−0 0 . 0 6 8 ; ∗ 10 ∗ 4−9 6−0 0 . 0 6 5 ; ∗ 11 ∗ 4−9 6−0
”∗ 12 ∗ 4−9 6−0 0 . 0 7 6 ; ∗ 13 ∗ 4−9 6−0 0 . 0 6 6 ; ∗ 14 ∗ 4−9 6−0 0 . 0 6 1 ; ∗ 15 ∗ 4−9 6−0
”∗ 16 ∗ 4−9 6−0 0 . 0 9 1 ; ∗ 17 ∗ 4−9 6−0 0 . 1 3 4 ; ∗ 18 ∗ 4−9 6−0 0 . 1 2 1 ; ∗ 19 ∗ 4−9 6−0
”∗ 20 ∗ 4−9 6−0 0 . 0 5 2 ; ∗ 21 ∗ 4−9 6−0 0 . 0 3 5 ; ∗ 22 ∗ 4−9 6−0 0 . 0 2 2 ; ∗ 23 ∗ 4−9 6−0
”}”
” weekday−w i n t e r {”
”∗
0 ∗ 10−3 1−5 0 . 0 1 0 ; ∗
1 ∗ 10−3 1−5 0 . 0 0 9 ; ∗
2 ∗ 10−3 1−5 0 . 0 0 9 ; ∗
3 ∗ 10−3
”∗
4 ∗ 10−3 1−5 0 . 0 0 9 ; ∗
5 ∗ 10−3 1−5 0 . 0 1 6 ; ∗
6 ∗ 10−3 1−5 0 . 0 3 2 ; ∗
7 ∗ 10−3
”∗
8 ∗ 10−3 1−5 0 . 0 4 5 ; ∗
9 ∗ 10−3 1−5 0 . 0 4 3 ; ∗ 10 ∗ 10−3 1−5 0 . 0 4 5 ; ∗ 11 ∗ 10−3
”∗ 12 ∗ 10−3 1−5 0 . 0 6 3 ; ∗ 13 ∗ 10−3 1−5 0 . 0 5 3 ; ∗ 14 ∗ 10−3 1−5 0 . 0 5 2 ; ∗ 15 ∗ 10−3
”∗ 16 ∗ 10−3 1−5 0 . 1 3 8 ; ∗ 17 ∗ 10−3 1−5 0 . 2 4 2 ; ∗ 18 ∗ 10−3 1−5 0 . 1 8 2 ; ∗ 19 ∗ 10−3
”∗ 20 ∗ 10−3 1−5 0 . 0 5 1 ; ∗ 21 ∗ 10−3 1−5 0 . 0 3 4 ; ∗ 22 ∗ 10−3 1−5 0 . 0 2 2 ; ∗ 23 ∗ 10−3
”}”
” weekend−w i n t e r {”
”∗
0 ∗ 10−3 6−0 0 . 0 1 3 ; ∗
1 ∗ 10−3 6−0 0 . 0 1 0 ; ∗
2 ∗ 10−3 6−0 0 . 0 1 0 ; ∗
3 ∗ 10−3
”∗
4 ∗ 10−3 6−0 0 . 0 1 0 ; ∗
5 ∗ 10−3 6−0 0 . 0 1 2 ; ∗
6 ∗ 10−3 6−0 0 . 0 1 8 ; ∗
7 ∗ 10−3
”∗
8 ∗ 10−3 6−0 0 . 0 7 3 ; ∗
9 ∗ 10−3 6−0 0 . 0 9 4 ; ∗ 10 ∗ 10−3 6−0 0 . 0 9 1 ; ∗ 11 ∗ 10−3
”∗ 12 ∗ 10−3 6−0 0 . 1 1 7 ; ∗ 13 ∗ 10−3 6−0 0 . 1 0 9 ; ∗ 14 ∗ 10−3 6−0 0 . 1 0 0 ; ∗ 15 ∗ 10−3
”∗ 16 ∗ 10−3 6−0 0 . 1 5 3 ; ∗ 17 ∗ 10−3 6−0 0 . 2 1 5 ; ∗ 18 ∗ 10−3 6−0 0 . 1 6 1 ; ∗ 19 ∗ 10−3
”∗ 20 ∗ 10−3 6−0 0 . 0 5 0 ; ∗ 21 ∗ 10−3 6−0 0 . 0 3 3 ; ∗ 22 ∗ 10−3 6−0 0 . 0 2 2 ; ∗ 23 ∗ 10−3
”}”
”MICROWAVE” ,
{40 , f a l s e , {0 ,0 ,1} , 0 . 7 , 0 . 8 } ,
” t y p e : a n a l o g ; s c h e d u l e : r e s i d e n t i a l −microwave−d e f a u l t ; power : 0 . 1 2 kW” ,
” r e s i d e n t i a l −microwave−d e f a u l t ” ,
” p o s i t i v e ; n o n z e r o ; weekday−summer {”
”∗
0 ∗ 4−9 1−5 0 . 0 0 9 ; ∗
1 ∗ 4−9 1−5 0 . 0 0 8 ; ∗
2 ∗ 4−9 1−5 0 . 0 0 7 ; ∗
3
”∗
4 ∗ 4−9 1−5 0 . 0 0 8 ; ∗
5 ∗ 4−9 1−5 0 . 0 1 2 ; ∗
6 ∗ 4−9 1−5 0 . 0 2 5 ; ∗
7
”∗
8 ∗ 4−9 1−5 0 . 0 4 4 ; ∗
9 ∗ 4−9 1−5 0 . 0 4 2 ; ∗ 10 ∗ 4−9 1−5 0 . 0 4 2 ; ∗ 11
”∗ 12 ∗ 4−9 1−5 0 . 0 5 7 ; ∗ 13 ∗ 4−9 1−5 0 . 0 4 6 ; ∗ 14 ∗ 4−9 1−5 0 . 0 4 4 ; ∗ 15
”∗ 16 ∗ 4−9 1−5 0 . 0 9 4 ; ∗ 17 ∗ 4−9 1−5 0 . 1 6 8 ; ∗ 18 ∗ 4−9 1−5 0 . 1 4 8 ; ∗ 19
”∗ 20 ∗ 4−9 1−5 0 . 0 5 3 ; ∗ 21 ∗ 4−9 1−5 0 . 0 3 8 ; ∗ 22 ∗ 4−9 1−5 0 . 0 2 3 ; ∗ 23
”}”
” weekend−summer {”
”∗
0 ∗ 4−9 6−0 0 . 0 0 9 ; ∗
1 ∗ 4−9 6−0 0 . 0 0 7 ; ∗
2 ∗ 4−9 6−0 0 . 0 0 7 ; ∗
3
”∗
4 ∗ 4−9 6−0 0 . 0 0 7 ; ∗
5 ∗ 4−9 6−0 0 . 0 0 9 ; ∗
6 ∗ 4−9 6−0 0 . 0 1 7 ; ∗
7
”∗
8 ∗ 4−9 6−0 0 . 0 6 0 ; ∗
9 ∗ 4−9 6−0 0 . 0 6 8 ; ∗ 10 ∗ 4−9 6−0 0 . 0 6 5 ; ∗ 11
”∗ 12 ∗ 4−9 6−0 0 . 0 7 6 ; ∗ 13 ∗ 4−9 6−0 0 . 0 6 6 ; ∗ 14 ∗ 4−9 6−0 0 . 0 6 1 ; ∗ 15
”∗ 16 ∗ 4−9 6−0 0 . 0 9 1 ; ∗ 17 ∗ 4−9 6−0 0 . 1 3 4 ; ∗ 18 ∗ 4−9 6−0 0 . 1 2 1 ; ∗ 19
”∗ 20 ∗ 4−9 6−0 0 . 0 5 2 ; ∗ 21 ∗ 4−9 6−0 0 . 0 3 5 ; ∗ 22 ∗ 4−9 6−0 0 . 0 2 2 ; ∗ 23
”}”
” weekday−w i n t e r {”
”∗
0 ∗ 10−3 1−5 0 . 0 1 0 ; ∗
1 ∗ 10−3 1−5 0 . 0 0 9 ; ∗
2 ∗ 10−3 1−5 0 . 0 0 9 ; ∗
”∗
4 ∗ 10−3 1−5 0 . 0 0 9 ; ∗
5 ∗ 10−3 1−5 0 . 0 1 6 ; ∗
6 ∗ 10−3 1−5 0 . 0 3 2 ; ∗
”∗
8 ∗ 10−3 1−5 0 . 0 4 5 ; ∗
9 ∗ 10−3 1−5 0 . 0 4 3 ; ∗ 10 ∗ 10−3 1−5 0 . 0 4 5 ; ∗
”∗ 12 ∗ 10−3 1−5 0 . 0 6 3 ; ∗ 13 ∗ 10−3 1−5 0 . 0 5 3 ; ∗ 14 ∗ 10−3 1−5 0 . 0 5 2 ; ∗
”∗ 16 ∗ 10−3 1−5 0 . 1 3 8 ; ∗ 17 ∗ 10−3 1−5 0 . 2 4 2 ; ∗ 18 ∗ 10−3 1−5 0 . 1 8 2 ; ∗
”∗ 20 ∗ 10−3 1−5 0 . 0 5 1 ; ∗ 21 ∗ 10−3 1−5 0 . 0 3 4 ; ∗ 22 ∗ 10−3 1−5 0 . 0 2 2 ; ∗
”}”
” weekend−w i n t e r {”
”∗
0 ∗ 10−3 6−0 0 . 0 1 3 ; ∗
1 ∗ 10−3 6−0 0 . 0 1 0 ; ∗
2 ∗ 10−3 6−0 0 . 0 1 0 ; ∗
”∗
4 ∗ 10−3 6−0 0 . 0 1 0 ; ∗
5 ∗ 10−3 6−0 0 . 0 1 2 ; ∗
6 ∗ 10−3 6−0 0 . 0 1 8 ; ∗
0.007;”
0.040;”
0.053;”
0.053;”
0.086;”
0.013”
0.007;”
0.038;”
0.067;”
0.067;”
0.080;”
0.011”
1−5
1−5
1−5
1−5
1−5
1−5
0.009;”
0.050;”
0.059;”
0.072;”
0.088;”
0.014”
6−0
6−0
6−0
6−0
6−0
6−0
0.010;”
0.040;”
0.100;”
0.108;”
0.085;”
0.014”
// 20% o f
range
∗
∗
∗
∗
∗
∗
4−9
4−9
4−9
4−9
4−9
4−9
1−5
1−5
1−5
1−5
1−5
1−5
0.007;”
0.040;”
0.053;”
0.053;”
0.086;”
0.013”
∗
∗
∗
∗
∗
∗
4−9
4−9
4−9
4−9
4−9
4−9
6−0
6−0
6−0
6−0
6−0
6−0
0.007;”
0.038;”
0.067;”
0.067;”
0.080;”
0.011”
3
7
11
15
19
23
∗
∗
∗
∗
∗
∗
10−3
10−3
10−3
10−3
10−3
10−3
1−5
1−5
1−5
1−5
1−5
1−5
0.009;”
0.050;”
0.059;”
0.072;”
0.088;”
0.014”
3 ∗ 10−3 6−0 0 . 0 1 0 ; ”
7 ∗ 10−3 6−0 0 . 0 4 0 ; ”
153
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
+
”∗
8 ∗ 10−3 6−0 0 . 0 7 3 ; ∗
9 ∗ 10−3 6−0 0 . 0 9 4 ; ∗ 10 ∗ 10−3 6−0 0 . 0 9 1 ; ∗ 11 ∗ 10−3 6−0
+
”∗ 12 ∗ 10−3 6−0 0 . 1 1 7 ; ∗ 13 ∗ 10−3 6−0 0 . 1 0 9 ; ∗ 14 ∗ 10−3 6−0 0 . 1 0 0 ; ∗ 15 ∗ 10−3 6−0
+
”∗ 16 ∗ 10−3 6−0 0 . 1 5 3 ; ∗ 17 ∗ 10−3 6−0 0 . 2 1 5 ; ∗ 18 ∗ 10−3 6−0 0 . 1 6 1 ; ∗ 19 ∗ 10−3 6−0
+
”∗ 20 ∗ 10−3 6−0 0 . 0 5 0 ; ∗ 21 ∗ 10−3 6−0 0 . 0 3 3 ; ∗ 22 ∗ 10−3 6−0 0 . 0 2 2 ; ∗ 23 ∗ 10−3 6−0
+
”}”
+} ,
+
+/// @todo add o t h e r i m p l i c i t e n d u s e s c h e d u l e s and s h a p e s a s t h e y a r e d e f i n e d
I n d e x : r e s i d e n t i a l / h o u s e e . cpp
===================================================================
( r e v i s i o n 4854)
−−− r e s i d e n t i a l / h o u s e e . cpp
+++ r e s i d e n t i a l / h o u s e e . cpp
( w o r k i n g copy )
@@ −122 ,385 +122 ,7 @@
char ∗ s c h e d u l e d e f i n i t i o n ;
} implicit enduse data [ ] =
{
−
// l i g h t i n g ( s o u r c e : ELCAP l i t −s p . d a t )
−
{
”LIGHTS” ,
−
{30 , f a l s e , { 0 . 5 , 0 . 1 , 0 . 4 } , 0 . 9 7 , 0 . 9 } ,
−
” t y p e : a n a l o g ; s c h e d u l e : r e s i d e n t i a l −l i g h t s −d e f a u l t ; power : 0 . 7 6 kW” ,
−
” r e s i d e n t i a l −l i g h t s −d e f a u l t ” ,
−
” p o s i t i v e ; n o n z e r o ; weekday−summer {”
−
”∗
0 ∗ 4−9 1−5 0 . 3 8 0 ; ∗
1 ∗ 4−9 1−5 0 . 3 4 0 ; ∗
2 ∗ 4−9 1−5 0 . 3 2 0 ; ∗
3 ∗ 4−9
−
”∗
4 ∗ 4−9 1−5 0 . 3 2 0 ; ∗
5 ∗ 4−9 1−5 0 . 3 5 0 ; ∗
6 ∗ 4−9 1−5 0 . 4 1 0 ; ∗
7 ∗ 4−9
−
”∗
8 ∗ 4−9 1−5 0 . 4 5 0 ; ∗
9 ∗ 4−9 1−5 0 . 4 5 0 ; ∗ 10 ∗ 4−9 1−5 0 . 4 5 0 ; ∗ 11 ∗ 4−9
−
”∗ 12 ∗ 4−9 1−5 0 . 4 5 0 ; ∗ 13 ∗ 4−9 1−5 0 . 4 4 0 ; ∗ 14 ∗ 4−9 1−5 0 . 4 4 0 ; ∗ 15 ∗ 4−9
−
”∗ 16 ∗ 4−9 1−5 0 . 4 7 0 ; ∗ 17 ∗ 4−9 1−5 0 . 5 1 0 ; ∗ 18 ∗ 4−9 1−5 0 . 5 4 0 ; ∗ 19 ∗ 4−9
−
”∗ 20 ∗ 4−9 1−5 0 . 6 3 0 ; ∗ 21 ∗ 4−9 1−5 0 . 7 1 0 ; ∗ 22 ∗ 4−9 1−5 0 . 6 5 0 ; ∗ 23 ∗ 4−9
−
”}”
−
” weekend−summer {”
−
”∗
0 ∗ 4−9 6−0 0 . 4 1 0 ; ∗
1 ∗ 4−9 6−0 0 . 3 6 0 ; ∗
2 ∗ 4−9 6−0 0 . 3 3 0 ; ∗
3 ∗ 4−9
−
”∗
4 ∗ 4−9 6−0 0 . 3 2 0 ; ∗
5 ∗ 4−9 6−0 0 . 3 2 0 ; ∗
6 ∗ 4−9 6−0 0 . 3 4 0 ; ∗
7 ∗ 4−9
−
”∗
8 ∗ 4−9 6−0 0 . 4 4 0 ; ∗
9 ∗ 4−9 6−0 0 . 4 7 0 ; ∗ 10 ∗ 4−9 6−0 0 . 4 7 0 ; ∗ 11 ∗ 4−9
−
”∗ 12 ∗ 4−9 6−0 0 . 4 7 0 ; ∗ 13 ∗ 4−9 6−0 0 . 4 6 0 ; ∗ 14 ∗ 4−9 6−0 0 . 4 6 0 ; ∗ 15 ∗ 4−9
−
”∗ 16 ∗ 4−9 6−0 0 . 4 7 0 ; ∗ 17 ∗ 4−9 6−0 0 . 4 9 0 ; ∗ 18 ∗ 4−9 6−0 0 . 5 2 0 ; ∗ 19 ∗ 4−9
−
”∗ 20 ∗ 4−9 6−0 0 . 6 1 0 ; ∗ 21 ∗ 4−9 6−0 0 . 6 8 0 ; ∗ 22 ∗ 4−9 6−0 0 . 6 3 0 ; ∗ 23 ∗ 4−9
−
”}”
−
” weekday−w i n t e r {”
−
”∗
0 ∗ 10−3 1−5 0 . 4 2 0 0 ; ∗
1 ∗ 10−3 1−5 0 . 3 8 0 0 ; ∗
2 ∗ 10−3 1−5 0 . 3 7 0 0 ; ∗
3
−
”∗
4 ∗ 10−3 1−5 0 . 3 7 0 0 ; ∗
5 ∗ 10−3 1−5 0 . 4 2 0 0 ; ∗
6 ∗ 10−3 1−5 0 . 5 8 0 0 ; ∗
7
−
”∗
8 ∗ 10−3 1−5 0 . 6 1 0 0 ; ∗
9 ∗ 10−3 1−5 0 . 5 6 0 0 ; ∗ 10 ∗ 10−3 1−5 0 . 5 3 0 0 ; ∗ 11
−
”∗ 12 ∗ 10−3 1−5 0 . 4 9 0 0 ; ∗ 13 ∗ 10−3 1−5 0 . 4 7 0 0 ; ∗ 14 ∗ 10−3 1−5 0 . 4 7 0 0 ; ∗ 15
−
”∗ 16 ∗ 10−3 1−5 0 . 6 3 0 0 ; ∗ 17 ∗ 10−3 1−5 0 . 8 4 0 0 ; ∗ 18 ∗ 10−3 1−5 0 . 9 7 0 0 ; ∗ 19
−
”∗ 20 ∗ 10−3 1−5 0 . 9 6 0 0 ; ∗ 21 ∗ 10−3 1−5 0 . 8 9 0 0 ; ∗ 22 ∗ 10−3 1−5 0 . 7 4 0 0 ; ∗ 23
−
”}”
−
” weekend−w i n t e r {”
−
”∗
0 ∗ 10−3 6−0 0 . 4 9 0 0 ; ∗
1 ∗ 10−3 6−0 0 . 4 2 0 0 ; ∗
2 ∗ 10−3 6−0 0 . 3 8 0 0 ; ∗
3
−
”∗
4 ∗ 10−3 6−0 0 . 3 7 0 0 ; ∗
5 ∗ 10−3 6−0 0 . 3 8 0 0 ; ∗
6 ∗ 10−3 6−0 0 . 4 3 0 0 ; ∗
7
−
”∗
8 ∗ 10−3 6−0 0 . 6 0 0 0 ; ∗
9 ∗ 10−3 6−0 0 . 6 3 0 0 ; ∗ 10 ∗ 10−3 6−0 0 . 6 3 0 0 ; ∗ 11
−
”∗ 12 ∗ 10−3 6−0 0 . 6 0 0 0 ; ∗ 13 ∗ 10−3 6−0 0 . 5 9 0 0 ; ∗ 14 ∗ 10−3 6−0 0 . 5 9 0 0 ; ∗ 15
−
”∗ 16 ∗ 10−3 6−0 0 . 7 1 0 0 ; ∗ 17 ∗ 10−3 6−0 0 . 8 8 0 0 ; ∗ 18 ∗ 10−3 6−0 0 . 9 6 0 0 ; ∗ 19
−
”∗ 20 ∗ 10−3 6−0 0 . 9 4 0 0 ; ∗ 21 ∗ 10−3 6−0 0 . 8 8 0 0 ; ∗ 22 ∗ 10−3 6−0 0 . 7 6 0 0 ; ∗ 23
−
”}”
−
},
−
// P l u g s ( s o u r c e : ELCAP l i t −s p . d a t )
−
{
”PLUGS” ,
−
{30 , f a l s e , { 0 . 0 , 0 . 0 , 1 . 0 } , 0 . 9 0 , 0 . 9 } ,
−
” t y p e : a n a l o g ; s c h e d u l e : r e s i d e n t i a l −p l u g s −d e f a u l t ; power : 0 . 3 6 kW” ,
−
” r e s i d e n t i a l −p l u g s −d e f a u l t ” ,
−
” p o s i t i v e ; n o n z e r o ; weekday−summer {”
−
”∗
0 ∗ 4−9 1−5 0 . 3 8 0 ; ∗
1 ∗ 4−9 1−5 0 . 3 4 0 ; ∗
2 ∗ 4−9 1−5 0 . 3 2 0 ; ∗
3 ∗ 4−9
−
”∗
4 ∗ 4−9 1−5 0 . 3 2 0 ; ∗
5 ∗ 4−9 1−5 0 . 3 5 0 ; ∗
6 ∗ 4−9 1−5 0 . 4 1 0 ; ∗
7 ∗ 4−9
−
”∗
8 ∗ 4−9 1−5 0 . 4 5 0 ; ∗
9 ∗ 4−9 1−5 0 . 4 5 0 ; ∗ 10 ∗ 4−9 1−5 0 . 4 5 0 ; ∗ 11 ∗ 4−9
−
”∗ 12 ∗ 4−9 1−5 0 . 4 5 0 ; ∗ 13 ∗ 4−9 1−5 0 . 4 4 0 ; ∗ 14 ∗ 4−9 1−5 0 . 4 4 0 ; ∗ 15 ∗ 4−9
−
”∗ 16 ∗ 4−9 1−5 0 . 4 7 0 ; ∗ 17 ∗ 4−9 1−5 0 . 5 1 0 ; ∗ 18 ∗ 4−9 1−5 0 . 5 4 0 ; ∗ 19 ∗ 4−9
−
”∗ 20 ∗ 4−9 1−5 0 . 6 3 0 ; ∗ 21 ∗ 4−9 1−5 0 . 7 1 0 ; ∗ 22 ∗ 4−9 1−5 0 . 6 5 0 ; ∗ 23 ∗ 4−9
−
”}”
−
” weekend−summer {”
−
”∗
0 ∗ 4−9 6−0 0 . 4 1 0 ; ∗
1 ∗ 4−9 6−0 0 . 3 6 0 ; ∗
2 ∗ 4−9 6−0 0 . 3 3 0 ; ∗
3 ∗ 4−9
−
”∗
4 ∗ 4−9 6−0 0 . 3 2 0 ; ∗
5 ∗ 4−9 6−0 0 . 3 2 0 ; ∗
6 ∗ 4−9 6−0 0 . 3 4 0 ; ∗
7 ∗ 4−9
−
”∗
8 ∗ 4−9 6−0 0 . 4 4 0 ; ∗
9 ∗ 4−9 6−0 0 . 4 7 0 ; ∗ 10 ∗ 4−9 6−0 0 . 4 7 0 ; ∗ 11 ∗ 4−9
−
”∗ 12 ∗ 4−9 6−0 0 . 4 7 0 ; ∗ 13 ∗ 4−9 6−0 0 . 4 6 0 ; ∗ 14 ∗ 4−9 6−0 0 . 4 6 0 ; ∗ 15 ∗ 4−9
−
”∗ 16 ∗ 4−9 6−0 0 . 4 7 0 ; ∗ 17 ∗ 4−9 6−0 0 . 4 9 0 ; ∗ 18 ∗ 4−9 6−0 0 . 5 2 0 ; ∗ 19 ∗ 4−9
−
”∗ 20 ∗ 4−9 6−0 0 . 6 1 0 ; ∗ 21 ∗ 4−9 6−0 0 . 6 8 0 ; ∗ 22 ∗ 4−9 6−0 0 . 6 3 0 ; ∗ 23 ∗ 4−9
−
”}”
−
” weekday−w i n t e r {”
−
”∗
0 ∗ 10−3 1−5 0 . 4 2 0 0 ; ∗
1 ∗ 10−3 1−5 0 . 3 8 0 0 ; ∗
2 ∗ 10−3 1−5 0 . 3 7 0 0 ; ∗
3
−
”∗
4 ∗ 10−3 1−5 0 . 3 7 0 0 ; ∗
5 ∗ 10−3 1−5 0 . 4 2 0 0 ; ∗
6 ∗ 10−3 1−5 0 . 5 8 0 0 ; ∗
7
−
”∗
8 ∗ 10−3 1−5 0 . 6 1 0 0 ; ∗
9 ∗ 10−3 1−5 0 . 5 6 0 0 ; ∗ 10 ∗ 10−3 1−5 0 . 5 3 0 0 ; ∗ 11
−
”∗ 12 ∗ 10−3 1−5 0 . 4 9 0 0 ; ∗ 13 ∗ 10−3 1−5 0 . 4 7 0 0 ; ∗ 14 ∗ 10−3 1−5 0 . 4 7 0 0 ; ∗ 15
−
”∗ 16 ∗ 10−3 1−5 0 . 6 3 0 0 ; ∗ 17 ∗ 10−3 1−5 0 . 8 4 0 0 ; ∗ 18 ∗ 10−3 1−5 0 . 9 7 0 0 ; ∗ 19
−
”∗ 20 ∗ 10−3 1−5 0 . 9 6 0 0 ; ∗ 21 ∗ 10−3 1−5 0 . 8 9 0 0 ; ∗ 22 ∗ 10−3 1−5 0 . 7 4 0 0 ; ∗ 23
−
”}”
−
” weekend−w i n t e r {”
−
”∗
0 ∗ 10−3 6−0 0 . 4 9 0 0 ; ∗
1 ∗ 10−3 6−0 0 . 4 2 0 0 ; ∗
2 ∗ 10−3 6−0 0 . 3 8 0 0 ; ∗
3
−
”∗
4 ∗ 10−3 6−0 0 . 3 7 0 0 ; ∗
5 ∗ 10−3 6−0 0 . 3 8 0 0 ; ∗
6 ∗ 10−3 6−0 0 . 4 3 0 0 ; ∗
7
−
”∗
8 ∗ 10−3 6−0 0 . 6 0 0 0 ; ∗
9 ∗ 10−3 6−0 0 . 6 3 0 0 ; ∗ 10 ∗ 10−3 6−0 0 . 6 3 0 0 ; ∗ 11
−
”∗ 12 ∗ 10−3 6−0 0 . 6 0 0 0 ; ∗ 13 ∗ 10−3 6−0 0 . 5 9 0 0 ; ∗ 14 ∗ 10−3 6−0 0 . 5 9 0 0 ; ∗ 15
−
”∗ 16 ∗ 10−3 6−0 0 . 7 1 0 0 ; ∗ 17 ∗ 10−3 6−0 0 . 8 8 0 0 ; ∗ 18 ∗ 10−3 6−0 0 . 9 6 0 0 ; ∗ 19
0.100;”
0.108;”
0.085;”
0.014”
1−5
1−5
1−5
1−5
1−5
1−5
0.320;”
0.450;”
0.450;”
0.450;”
0.560;”
0.490”
6−0
6−0
6−0
6−0
6−0
6−0
0.320;”
0.390;”
0.470;”
0.460;”
0.540;”
0.500”
∗
∗
∗
∗
∗
∗
10−3
10−3
10−3
10−3
10−3
10−3
1−5
1−5
1−5
1−5
1−5
1−5
0.3600;”
0.6900;”
0.5100;”
0.5100;”
0.9800;”
0.5500”
∗
∗
∗
∗
∗
∗
10−3
10−3
10−3
10−3
10−3
10−3
6−0
6−0
6−0
6−0
6−0
6−0
0.3800;”
0.5100;”
0.6100;”
0.6100;”
0.9700;”
0.5800”
1−5
1−5
1−5
1−5
1−5
1−5
0.320;”
0.450;”
0.450;”
0.450;”
0.560;”
0.490”
6−0
6−0
6−0
6−0
6−0
6−0
0.320;”
0.390;”
0.470;”
0.460;”
0.540;”
0.500”
∗
∗
∗
∗
∗
∗
10−3
10−3
10−3
10−3
10−3
10−3
1−5
1−5
1−5
1−5
1−5
1−5
0.3600;”
0.6900;”
0.5100;”
0.5100;”
0.9800;”
0.5500”
∗
∗
∗
∗
∗
10−3
10−3
10−3
10−3
10−3
6−0
6−0
6−0
6−0
6−0
0.3800;”
0.5100;”
0.6100;”
0.6100;”
0.9700;”
154
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
”∗ 20 ∗ 10−3 6−0 0 . 9 4 0 0 ; ∗ 21 ∗ 10−3 6−0 0 . 8 8 0 0 ; ∗ 22 ∗ 10−3 6−0 0 . 7 6 0 0 ; ∗ 23 ∗ 10−3 6−0 0 . 5 8 0 0 ”
”}”
},
{
”CLOTHESWASHER” ,
{20 , f a l s e , { 0 . 0 , 0 . 0 , 1 . 0 } , 0 . 9 , 1 . 0 } ,
” t y p e : a n a l o g ; s c h e d u l e : r e s i d e n t i a l −c l o t h e s w a s h e r −d e f a u l t ; power : 1 kW” , // e n e r g y : 0 . 7 5 kWh ;
” r e s i d e n t i a l −c l o t h e s w a s h e r −d e f a u l t ” ,
” p o s i t i v e ; n o n z e r o ; weekday−summer {”
”∗
0 ∗ 4−9 1−5 0 . 0 0 2 9 ; ∗
1 ∗ 4−9 1−5 0 . 0 0 1 9 ; ∗
2 ∗ 4−9 1−5 0 . 0 0 1 4 ; ∗
3 ∗ 4−9 1−5 0 . 0 0 1 3 ; ”
”∗
4 ∗ 4−9 1−5 0 . 0 0 1 8 ; ∗
5 ∗ 4−9 1−5 0 . 0 0 2 6 ; ∗
6 ∗ 4−9 1−5 0 . 0 0 5 5 ; ∗
7 ∗ 4−9 1−5 0 . 0 1 2 6 ; ”
”∗
8 ∗ 4−9 1−5 0 . 0 1 8 1 ; ∗
9 ∗ 4−9 1−5 0 . 0 2 0 8 ; ∗ 10 ∗ 4−9 1−5 0 . 0 2 2 9 ; ∗ 11 ∗ 4−9 1−5 0 . 0 2 1 6 ; ”
”∗ 12 ∗ 4−9 1−5 0 . 0 1 9 3 ; ∗ 13 ∗ 4−9 1−5 0 . 0 1 7 0 ; ∗ 14 ∗ 4−9 1−5 0 . 0 1 4 5 ; ∗ 15 ∗ 4−9 1−5 0 . 0 1 3 5 ; ”
”∗ 16 ∗ 4−9 1−5 0 . 0 1 3 5 ; ∗ 17 ∗ 4−9 1−5 0 . 0 1 4 2 ; ∗ 18 ∗ 4−9 1−5 0 . 0 1 4 5 ; ∗ 19 ∗ 4−9 1−5 0 . 0 1 4 8 ; ”
”∗ 20 ∗ 4−9 1−5 0 . 0 1 4 6 ; ∗ 21 ∗ 4−9 1−5 0 . 0 1 4 1 ; ∗ 22 ∗ 4−9 1−5 0 . 0 1 1 0 ; ∗ 23 ∗ 4−9 1−5 0 . 0 0 6 2 ”
”}”
” weekend−summer {”
”∗
0 ∗ 4−9 6−0 0 . 0 0 3 1 ; ∗
1 ∗ 4−9 6−0 0 . 0 0 1 9 ; ∗
2 ∗ 4−9 6−0 0 . 0 0 1 3 ; ∗
3 ∗ 4−9 6−0 0 . 0 0 1 2 ; ”
”∗
4 ∗ 4−9 6−0 0 . 0 0 1 2 ; ∗
5 ∗ 4−9 6−0 0 . 0 0 1 6 ; ∗
6 ∗ 4−9 6−0 0 . 0 0 2 7 ; ∗
7 ∗ 4−9 6−0 0 . 0 0 6 6 ; ”
”∗
8 ∗ 4−9 6−0 0 . 0 1 5 7 ; ∗
9 ∗ 4−9 6−0 0 . 0 2 2 0 ; ∗ 10 ∗ 4−9 6−0 0 . 0 2 5 8 ; ∗ 11 ∗ 4−9 6−0 0 . 0 2 5 1 ; ”
”∗ 12 ∗ 4−9 6−0 0 . 0 2 3 1 ; ∗ 13 ∗ 4−9 6−0 0 . 0 2 1 7 ; ∗ 14 ∗ 4−9 6−0 0 . 0 1 8 6 ; ∗ 15 ∗ 4−9 6−0 0 . 0 1 5 7 ; ”
”∗ 16 ∗ 4−9 6−0 0 . 0 1 5 6 ; ∗ 17 ∗ 4−9 6−0 0 . 0 1 5 1 ; ∗ 18 ∗ 4−9 6−0 0 . 0 1 4 7 ; ∗ 19 ∗ 4−9 6−0 0 . 0 1 5 0 ; ”
”∗ 20 ∗ 4−9 6−0 0 . 0 1 5 6 ; ∗ 21 ∗ 4−9 6−0 0 . 0 1 4 8 ; ∗ 22 ∗ 4−9 6−0 0 . 0 1 0 6 ; ∗ 23 ∗ 4−9 6−0 0 . 0 0 6 5 ”
”}”
” weekday−w i n t e r {”
”∗
0 ∗ 10−3 1−5 0 . 0 0 3 6 ; ∗
1 ∗ 10−3 1−5 0 . 0 0 2 4 ; ∗
2 ∗ 10−3 1−5 0 . 0 0 2 0 ; ∗
3 ∗ 10−3 1−5 0 . 0 0 1 9 ; ”
”∗
4 ∗ 10−3 1−5 0 . 0 0 2 6 ; ∗
5 ∗ 10−3 1−5 0 . 0 0 4 0 ; ∗
6 ∗ 10−3 1−5 0 . 0 0 6 2 ; ∗
7 ∗ 10−3 1−5 0 . 0 1 1 8 ; ”
”∗
8 ∗ 10−3 1−5 0 . 0 1 7 7 ; ∗
9 ∗ 10−3 1−5 0 . 0 2 1 1 ; ∗ 10 ∗ 10−3 1−5 0 . 0 2 1 5 ; ∗ 11 ∗ 10−3 1−5 0 . 0 2 0 3 ; ”
”∗ 12 ∗ 10−3 1−5 0 . 0 1 7 6 ; ∗ 13 ∗ 10−3 1−5 0 . 0 1 5 5 ; ∗ 14 ∗ 10−3 1−5 0 . 0 1 3 3 ; ∗ 15 ∗ 10−3 1−5 0 . 0 1 3 0 ; ”
”∗ 16 ∗ 10−3 1−5 0 . 0 1 4 5 ; ∗ 17 ∗ 10−3 1−5 0 . 0 1 5 9 ; ∗ 18 ∗ 10−3 1−5 0 . 0 1 6 6 ; ∗ 19 ∗ 10−3 1−5 0 . 0 1 6 4 ; ”
”∗ 20 ∗ 10−3 1−5 0 . 0 1 5 4 ; ∗ 21 ∗ 10−3 1−5 0 . 0 1 4 9 ; ∗ 22 ∗ 10−3 1−5 0 . 0 1 1 0 ; ∗ 23 ∗ 10−3 1−5 0 . 0 0 6 5 ”
”}”
” weekend−w i n t e r {”
”∗
0 ∗ 10−3 6−0 0 . 0 0 4 4 ; ∗
1 ∗ 10−3 6−0 0 . 0 0 3 0 ; ∗
2 ∗ 10−3 6−0 0 . 0 0 2 2 ; ∗
3 ∗ 10−3 6−0 0 . 0 0 2 0 ; ”
”∗
4 ∗ 10−3 6−0 0 . 0 0 2 1 ; ∗
5 ∗ 10−3 6−0 0 . 0 0 2 1 ; ∗
6 ∗ 10−3 6−0 0 . 0 0 3 0 ; ∗
7 ∗ 10−3 6−0 0 . 0 0 6 7 ; ”
”∗
8 ∗ 10−3 6−0 0 . 0 1 4 5 ; ∗
9 ∗ 10−3 6−0 0 . 0 2 4 4 ; ∗ 10 ∗ 10−3 6−0 0 . 0 3 1 0 ; ∗ 11 ∗ 10−3 6−0 0 . 0 3 2 3 ; ”
”∗ 12 ∗ 10−3 6−0 0 . 0 3 0 8 ; ∗ 13 ∗ 10−3 6−0 0 . 0 2 8 5 ; ∗ 14 ∗ 10−3 6−0 0 . 0 2 5 1 ; ∗ 15 ∗ 10−3 6−0 0 . 0 2 2 4 ; ”
”∗ 16 ∗ 10−3 6−0 0 . 0 2 1 5 ; ∗ 17 ∗ 10−3 6−0 0 . 0 2 0 3 ; ∗ 18 ∗ 10−3 6−0 0 . 0 1 9 4 ; ∗ 19 ∗ 10−3 6−0 0 . 0 1 8 8 ; ”
”∗ 20 ∗ 10−3 6−0 0 . 0 1 8 0 ; ∗ 21 ∗ 10−3 6−0 0 . 0 1 5 1 ; ∗ 22 ∗ 10−3 6−0 0 . 0 1 2 2 ; ∗ 23 ∗ 10−3 6−0 0 . 0 0 7 3 ”
”}”
},
{
”WATERHEATER” ,
{30 , true , { 0 . 0 , 0 . 0 , 1 . 0 } , 1 . 0 , 0 . 5 } ,
” t y p e : a n a l o g ; s c h e d u l e : r e s i d e n t i a l −w a t e r h e a t e r −d e f a u l t ; power : 5 kW” , // e n e r g y : 1 kWh ;
” r e s i d e n t i a l −w a t e r h e a t e r −d e f a u l t ” ,
” p o s i t i v e ; n o n z e r o ; weekday−summer {”
”∗
0 ∗ 4−9 1−5 0 . 2 1 ; ∗
1 ∗ 4−9 1−5 0 . 1 6 ; ∗
2 ∗ 4−9 1−5 0 . 1 3 ; ∗
3 ∗ 4−9 1−5 0 . 1 2 ; ”
”∗
4 ∗ 4−9 1−5 0 . 1 5 ; ∗
5 ∗ 4−9 1−5 0 . 2 6 ; ∗
6 ∗ 4−9 1−5 0 . 5 1 ; ∗
7 ∗ 4−9 1−5 0 . 7 6 ; ”
”∗
8 ∗ 4−9 1−5 0 . 7 7 ; ∗
9 ∗ 4−9 1−5 0 . 7 6 ; ∗ 10 ∗ 4−9 1−5 0 . 7 1 ; ∗ 11 ∗ 4−9 1−5 0 . 6 1 ; ”
”∗ 12 ∗ 4−9 1−5 0 . 5 4 ; ∗ 13 ∗ 4−9 1−5 0 . 4 9 ; ∗ 14 ∗ 4−9 1−5 0 . 4 3 ; ∗ 15 ∗ 4−9 1−5 0 . 4 1 ; ”
”∗ 16 ∗ 4−9 1−5 0 . 4 3 ; ∗ 17 ∗ 4−9 1−5 0 . 5 2 ; ∗ 18 ∗ 4−9 1−5 0 . 6 0 ; ∗ 19 ∗ 4−9 1−5 0 . 6 0 ; ”
”∗ 20 ∗ 4−9 1−5 0 . 5 9 ; ∗ 21 ∗ 4−9 1−5 0 . 6 0 ; ∗ 22 ∗ 4−9 1−5 0 . 5 5 ; ∗ 23 ∗ 4−9 1−5 0 . 3 7 ”
”}”
” weekend−summer {”
”∗
0 ∗ 4−9 6−0 0 . 2 3 ; ∗
1 ∗ 4−9 6−0 0 . 1 7 ; ∗
2 ∗ 4−9 6−0 0 . 1 4 ; ∗
3 ∗ 4−9 6−0 0 . 1 3 ; ”
”∗
4 ∗ 4−9 6−0 0 . 1 3 ; ∗
5 ∗ 4−9 6−0 0 . 1 7 ; ∗
6 ∗ 4−9 6−0 0 . 2 6 ; ∗
7 ∗ 4−9 6−0 0 . 4 5 ; ”
”∗
8 ∗ 4−9 6−0 0 . 6 9 ; ∗
9 ∗ 4−9 6−0 0 . 8 5 ; ∗ 10 ∗ 4−9 6−0 0 . 8 4 ; ∗ 11 ∗ 4−9 6−0 0 . 7 6 ; ”
”∗ 12 ∗ 4−9 6−0 0 . 6 5 ; ∗ 13 ∗ 4−9 6−0 0 . 5 8 ; ∗ 14 ∗ 4−9 6−0 0 . 4 9 ; ∗ 15 ∗ 4−9 6−0 0 . 4 6 ; ”
”∗ 16 ∗ 4−9 6−0 0 . 4 6 ; ∗ 17 ∗ 4−9 6−0 0 . 5 0 ; ∗ 18 ∗ 4−9 6−0 0 . 5 4 ; ∗ 19 ∗ 4−9 6−0 0 . 5 5 ; ”
”∗ 20 ∗ 4−9 6−0 0 . 5 6 ; ∗ 21 ∗ 4−9 6−0 0 . 5 6 ; ∗ 22 ∗ 4−9 6−0 0 . 4 9 ; ∗ 23 ∗ 4−9 6−0 0 . 3 8 ”
”}”
” weekday−w i n t e r {”
”∗
0 ∗ 10−3 1−5 0 . 2 5 ; ∗
1 ∗ 10−3 1−5 0 . 1 9 ; ∗
2 ∗ 10−3 1−5 0 . 1 6 ; ∗
3 ∗ 10−3 1−5 0 . 1 5 ; ”
”∗
4 ∗ 10−3 1−5 0 . 1 8 ; ∗
5 ∗ 10−3 1−5 0 . 3 4 ; ∗
6 ∗ 10−3 1−5 0 . 7 4 ; ∗
7 ∗ 10−3 1−5 1 . 2 0 ; ”
”∗
8 ∗ 10−3 1−5 1 . 1 0 ; ∗
9 ∗ 10−3 1−5 0 . 9 4 ; ∗ 10 ∗ 10−3 1−5 0 . 8 2 ; ∗ 11 ∗ 10−3 1−5 0 . 7 1 ; ”
”∗ 12 ∗ 10−3 1−5 0 . 6 2 ; ∗ 13 ∗ 10−3 1−5 0 . 5 5 ; ∗ 14 ∗ 10−3 1−5 0 . 4 8 ; ∗ 15 ∗ 10−3 1−5 0 . 4 7 ; ”
”∗ 16 ∗ 10−3 1−5 0 . 5 4 ; ∗ 17 ∗ 10−3 1−5 0 . 6 8 ; ∗ 18 ∗ 10−3 1−5 0 . 8 3 ; ∗ 19 ∗ 10−3 1−5 0 . 8 2 ; ”
”∗ 20 ∗ 10−3 1−5 0 . 7 4 ; ∗ 21 ∗ 10−3 1−5 0 . 6 8 ; ∗ 22 ∗ 10−3 1−5 0 . 5 7 ; ∗ 23 ∗ 10−3 1−5 0 . 4 0 ”
”}”
” weekend−w i n t e r {”
”∗
0 ∗ 10−3 6−0 0 . 2 9 ; ∗
1 ∗ 10−3 6−0 0 . 2 2 ; ∗
2 ∗ 10−3 6−0 0 . 1 7 ; ∗
3 ∗ 10−3 6−0 0 . 1 5 ; ”
”∗
4 ∗ 10−3 6−0 0 . 1 6 ; ∗
5 ∗ 10−3 6−0 0 . 1 9 ; ∗
6 ∗ 10−3 6−0 0 . 2 7 ; ∗
7 ∗ 10−3 6−0 0 . 4 7 ; ”
”∗
8 ∗ 10−3 6−0 0 . 8 2 ; ∗
9 ∗ 10−3 6−0 1 . 0 8 ; ∗ 10 ∗ 10−3 6−0 1 . 1 5 ; ∗ 11 ∗ 10−3 6−0 1 . 0 8 ; ”
”∗ 12 ∗ 10−3 6−0 0 . 9 8 ; ∗ 13 ∗ 10−3 6−0 0 . 8 7 ; ∗ 14 ∗ 10−3 6−0 0 . 7 7 ; ∗ 15 ∗ 10−3 6−0 0 . 6 9 ; ”
”∗ 16 ∗ 10−3 6−0 0 . 7 2 ; ∗ 17 ∗ 10−3 6−0 0 . 7 8 ; ∗ 18 ∗ 10−3 6−0 0 . 8 3 ; ∗ 19 ∗ 10−3 6−0 0 . 7 9 ; ”
”∗ 20 ∗ 10−3 6−0 0 . 7 2 ; ∗ 21 ∗ 10−3 6−0 0 . 6 4 ; ∗ 22 ∗ 10−3 6−0 0 . 5 3 ; ∗ 23 ∗ 10−3 6−0 0 . 4 3 ”
”}”
},
{
”REFRIGERATOR” ,
{20 , f a l s e , { 0 . 1 , 0 . 0 , 0 . 9 } , 0 . 9 , 1 . 0 } ,
” t y p e : a n a l o g ; s c h e d u l e : r e s i d e n t i a l −r e f r i g e r a t o r −d e f a u l t ; power : 750 W” , // e n e r g y : 1 kWh ;
” r e s i d e n t i a l −r e f r i g e r a t o r −d e f a u l t ” ,
” p o s i t i v e ; n o n z e r o ; weekday−summer {”
”∗
0 ∗ 4−9 1−5 0 . 1 8 7 ; ∗
1 ∗ 4−9 1−5 0 . 1 8 2 ; ∗
2 ∗ 4−9 1−5 0 . 1 7 6 ; ∗
3 ∗ 4−9 1−5 0 . 1 7 0 ; ”
”∗
4 ∗ 4−9 1−5 0 . 1 6 8 ; ∗
5 ∗ 4−9 1−5 0 . 1 6 8 ; ∗
6 ∗ 4−9 1−5 0 . 1 7 7 ; ∗
7 ∗ 4−9 1−5 0 . 1 7 4 ; ”
”∗
8 ∗ 4−9 1−5 0 . 1 7 7 ; ∗
9 ∗ 4−9 1−5 0 . 1 8 0 ; ∗ 10 ∗ 4−9 1−5 0 . 1 8 0 ; ∗ 11 ∗ 4−9 1−5 0 . 1 8 3 ; ”
”∗ 12 ∗ 4−9 1−5 0 . 1 9 2 ; ∗ 13 ∗ 4−9 1−5 0 . 1 9 2 ; ∗ 14 ∗ 4−9 1−5 0 . 1 9 4 ; ∗ 15 ∗ 4−9 1−5 0 . 1 9 6 ; ”
155
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
”∗ 16 ∗ 4−9 1−5 0 . 2 0 5 ; ∗
”∗ 20 ∗ 4−9 1−5 0 . 2 1 6 ; ∗
”}”
” weekend−summer {”
”∗
0 ∗ 4−9 6−0 0 . 1 8 7 ; ∗
”∗
4 ∗ 4−9 6−0 0 . 1 6 6 ; ∗
”∗
8 ∗ 4−9 6−0 0 . 1 8 0 ; ∗
”∗ 12 ∗ 4−9 6−0 0 . 1 9 5 ; ∗
”∗ 16 ∗ 4−9 6−0 0 . 2 0 9 ; ∗
”∗ 20 ∗ 4−9 6−0 0 . 2 1 7 ; ∗
”}”
” weekday−w i n t e r {”
”∗
0 ∗ 10−3 1−5 0 . 1 5 3 0 ;
”∗
4 ∗ 10−3 1−5 0 . 1 4 0 0 ;
”∗
8 ∗ 10−3 1−5 0 . 1 5 8 0 ;
”∗ 12 ∗ 10−3 1−5 0 . 1 6 3 0 ;
”∗ 16 ∗ 10−3 1−5 0 . 1 6 9 0 ;
”∗ 20 ∗ 10−3 1−5 0 . 1 8 0 0 ;
”}”
” weekend−w i n t e r {”
”∗
0 ∗ 10−3 6−0 0 . 1 5 6 0 ;
”∗
4 ∗ 10−3 6−0 0 . 1 4 2 0 ;
”∗
8 ∗ 10−3 6−0 0 . 1 6 1 0 ;
”∗ 12 ∗ 10−3 6−0 0 . 1 7 4 0 ;
”∗ 16 ∗ 10−3 6−0 0 . 1 7 9 0 ;
”∗ 20 ∗ 10−3 6−0 0 . 1 8 4 0 ;
”}”
},
{
{
17 ∗ 4−9 1−5 0 . 2 1 7 ; ∗ 18 ∗ 4−9 1−5 0 . 2 2 5 ; ∗ 19 ∗ 4−9 1−5 0 . 2 2 1 ; ”
21 ∗ 4−9 1−5 0 . 2 1 4 ; ∗ 22 ∗ 4−9 1−5 0 . 2 0 7 ; ∗ 23 ∗ 4−9 1−5 0 . 1 9 5 ”
1
5
9
13
17
21
∗
∗
∗
∗
∗
∗
4−9
4−9
4−9
4−9
4−9
4−9
6−0
6−0
6−0
6−0
6−0
6−0
0.181;
0.164;
0.184;
0.200;
0.218;
0.216;
∗
∗
∗
∗
∗
∗
∗
∗
∗
∗
∗
∗
2
6
10
14
18
22
4−9
4−9
4−9
4−9
4−9
4−9
6−0
6−0
6−0
6−0
6−0
6−0
0.176;
0.167;
0.187;
0.201;
0.222;
0.207;
∗
∗
∗
∗
∗
∗
3
7
11
15
19
23
∗
∗
∗
∗
∗
∗
4−9
4−9
4−9
4−9
4−9
4−9
6−0
6−0
6−0
6−0
6−0
6−0
0.169;”
0.169;”
0.187;”
0.203;”
0.221;”
0.196”
∗
1 ∗ 10−3
∗
5 ∗ 10−3
∗
9 ∗ 10−3
∗ 13 ∗ 10−3
∗ 17 ∗ 10−3
∗ 21 ∗ 10−3
1−5
1−5
1−5
1−5
1−5
1−5
0.1500;
0.1450;
0.1580;
0.1620;
0.1850;
0.1760;
∗
∗
∗
∗
∗
∗
2
6
10
14
18
22
∗
∗
∗
∗
∗
∗
10−3
10−3
10−3
10−3
10−3
10−3
1−5
1−5
1−5
1−5
1−5
1−5
0.1460;
0.1520;
0.1560;
0.1590;
0.1920;
0.1670;
∗
∗
∗
∗
∗
∗
3
7
11
15
19
23
∗
∗
∗
∗
∗
∗
10−3
10−3
10−3
10−3
10−3
10−3
1−5
1−5
1−5
1−5
1−5
1−5
0.1420;”
0.1600;”
0.1560;”
0.1620;”
0.1820;”
0.1590”
∗
1 ∗ 10−3
∗
5 ∗ 10−3
∗
9 ∗ 10−3
∗ 13 ∗ 10−3
∗ 17 ∗ 10−3
∗ 21 ∗ 10−3
6−0
6−0
6−0
6−0
6−0
6−0
0.1520;
0.1430;
0.1690;
0.1760;
0.1910;
0.1780;
∗
∗
∗
∗
∗
∗
2
6
10
14
18
22
∗
∗
∗
∗
∗
∗
10−3
10−3
10−3
10−3
10−3
10−3
6−0
6−0
6−0
6−0
6−0
6−0
0.1470;
0.1430;
0.1670;
0.1740;
0.1930;
0.1700;
∗
∗
∗
∗
∗
∗
3
7
11
15
19
23
∗
∗
∗
∗
∗
∗
10−3
10−3
10−3
10−3
10−3
10−3
6−0
6−0
6−0
6−0
6−0
6−0
0.1430;”
0.1500;”
0.1660;”
0.1750;”
0.1870;”
0.1600”
”DRYER” ,
{30 , true , { 0 . 9 , 0 . 0 , 0 . 1 } , 0 . 9 9 , 0.15} ,
” t y p e : a n a l o g ; s c h e d u l e : r e s i d e n t i a l −d r y e r −d e f a u l t ; power : 5 kW” , // e n e r g y : 2 . 5 kWh ;
” r e s i d e n t i a l −d r y e r −d e f a u l t ” ,
” p o s i t i v e ; n o n z e r o ; weekday−summer {”
”∗
0 ∗ 4−9 1−5 0 . 0 3 6 ; ∗
1 ∗ 4−9 1−5 0 . 0 1 3 ; ∗
2 ∗ 4−9 1−5 0 . 0 0 7 ; ∗
3 ∗ 4−9 1−5 0 . 0 0 5 ; ”
”∗
4 ∗ 4−9 1−5 0 . 0 0 5 ; ∗
5 ∗ 4−9 1−5 0 . 0 1 7 ; ∗
6 ∗ 4−9 1−5 0 . 0 4 8 ; ∗
7 ∗ 4−9 1−5 0 . 0 8 5 ; ”
”∗
8 ∗ 4−9 1−5 0 . 1 1 5 ; ∗
9 ∗ 4−9 1−5 0 . 1 5 6 ; ∗ 10 ∗ 4−9 1−5 0 . 1 7 9 ; ∗ 11 ∗ 4−9 1−5 0 . 1 8 5 ; ”
”∗ 12 ∗ 4−9 1−5 0 . 1 7 2 ; ∗ 13 ∗ 4−9 1−5 0 . 1 6 2 ; ∗ 14 ∗ 4−9 1−5 0 . 1 4 5 ; ∗ 15 ∗ 4−9 1−5 0 . 1 3 6 ; ”
”∗ 16 ∗ 4−9 1−5 0 . 1 3 3 ; ∗ 17 ∗ 4−9 1−5 0 . 1 3 4 ; ∗ 18 ∗ 4−9 1−5 0 . 1 2 7 ; ∗ 19 ∗ 4−9 1−5 0 . 1 3 0 ; ”
”∗ 20 ∗ 4−9 1−5 0 . 1 4 1 ; ∗ 21 ∗ 4−9 1−5 0 . 1 5 4 ; ∗ 22 ∗ 4−9 1−5 0 . 1 3 8 ; ∗ 23 ∗ 4−9 1−5 0 . 0 8 3 ”
”}”
” weekend−summer {”
”∗
0 ∗ 4−9 6−0 0 . 0 4 1 ; ∗
1 ∗ 4−9 6−0 0 . 0 1 7 ; ∗
2 ∗ 4−9 6−0 0 . 0 0 8 ; ∗
3 ∗ 4−9 6−0 0 . 0 0 5 ; ”
”∗
4 ∗ 4−9 6−0 0 . 0 0 5 ; ∗
5 ∗ 4−9 6−0 0 . 0 0 7 ; ∗
6 ∗ 4−9 6−0 0 . 0 1 8 ; ∗
7 ∗ 4−9 6−0 0 . 0 4 7 ; ”
”∗
8 ∗ 4−9 6−0 0 . 1 0 0 ; ∗
9 ∗ 4−9 6−0 0 . 1 6 8 ; ∗ 10 ∗ 4−9 6−0 0 . 2 0 5 ; ∗ 11 ∗ 4−9 6−0 0 . 2 2 0 ; ”
”∗ 12 ∗ 4−9 6−0 0 . 2 1 1 ; ∗ 13 ∗ 4−9 6−0 0 . 2 1 0 ; ∗ 14 ∗ 4−9 6−0 0 . 1 8 8 ; ∗ 15 ∗ 4−9 6−0 0 . 1 6 8 ; ”
”∗ 16 ∗ 4−9 6−0 0 . 1 5 4 ; ∗ 17 ∗ 4−9 6−0 0 . 1 4 6 ; ∗ 18 ∗ 4−9 6−0 0 . 1 3 8 ; ∗ 19 ∗ 4−9 6−0 0 . 1 3 7 ; ”
”∗ 20 ∗ 4−9 6−0 0 . 1 4 4 ; ∗ 21 ∗ 4−9 6−0 0 . 1 5 5 ; ∗ 22 ∗ 4−9 6−0 0 . 1 3 1 ; ∗ 23 ∗ 4−9 6−0 0 . 0 8 1 ”
”}”
” weekday−w i n t e r {”
”∗
0 ∗ 10−3 1−5 0 . 0 3 6 0 ; ∗
1 ∗ 10−3 1−5 0 . 0 1 6 0 ; ∗
2 ∗ 10−3 1−5 0 . 0 1 0 0 ; ∗
3 ∗ 10−3 1−5
”∗
4 ∗ 10−3 1−5 0 . 0 0 9 0 ; ∗
5 ∗ 10−3 1−5 0 . 0 2 3 0 ; ∗
6 ∗ 10−3 1−5 0 . 0 6 1 0 ; ∗
7 ∗ 10−3 1−5
”∗
8 ∗ 10−3 1−5 0 . 1 3 2 0 ; ∗
9 ∗ 10−3 1−5 0 . 1 7 5 0 ; ∗ 10 ∗ 10−3 1−5 0 . 2 0 5 0 ; ∗ 11 ∗ 10−3 1−5
”∗ 12 ∗ 10−3 1−5 0 . 1 9 4 0 ; ∗ 13 ∗ 10−3 1−5 0 . 1 7 7 0 ; ∗ 14 ∗ 10−3 1−5 0 . 1 6 1 0 ; ∗ 15 ∗ 10−3 1−5
”∗ 16 ∗ 10−3 1−5 0 . 1 6 4 0 ; ∗ 17 ∗ 10−3 1−5 0 . 1 7 1 0 ; ∗ 18 ∗ 10−3 1−5 0 . 1 6 1 0 ; ∗ 19 ∗ 10−3 1−5
”∗ 20 ∗ 10−3 1−5 0 . 1 6 7 0 ; ∗ 21 ∗ 10−3 1−5 0 . 1 6 9 0 ; ∗ 22 ∗ 10−3 1−5 0 . 1 3 8 0 ; ∗ 23 ∗ 10−3 1−5
”}”
” weekend−w i n t e r {”
”∗
0 ∗ 10−3 6−0 0 . 0 3 9 0 ; ∗
1 ∗ 10−3 6−0 0 . 0 1 9 0 ; ∗
2 ∗ 10−3 6−0 0 . 0 1 1 0 ; ∗
3 ∗ 10−3 6−0
”∗
4 ∗ 10−3 6−0 0 . 0 0 8 0 ; ∗
5 ∗ 10−3 6−0 0 . 0 0 9 0 ; ∗
6 ∗ 10−3 6−0 0 . 0 1 6 0 ; ∗
7 ∗ 10−3 6−0
”∗
8 ∗ 10−3 6−0 0 . 1 0 1 0 ; ∗
9 ∗ 10−3 6−0 0 . 1 8 1 0 ; ∗ 10 ∗ 10−3 6−0 0 . 2 6 4 0 ; ∗ 11 ∗ 10−3 6−0
”∗ 12 ∗ 10−3 6−0 0 . 3 1 1 0 ; ∗ 13 ∗ 10−3 6−0 0 . 3 0 6 0 ; ∗ 14 ∗ 10−3 6−0 0 . 2 8 5 0 ; ∗ 15 ∗ 10−3 6−0
”∗ 16 ∗ 10−3 6−0 0 . 2 6 0 0 ; ∗ 17 ∗ 10−3 6−0 0 . 2 4 5 0 ; ∗ 18 ∗ 10−3 6−0 0 . 2 2 0 0 ; ∗ 19 ∗ 10−3 6−0
”∗ 20 ∗ 10−3 6−0 0 . 1 8 8 0 ; ∗ 21 ∗ 10−3 6−0 0 . 1 7 9 0 ; ∗ 22 ∗ 10−3 6−0 0 . 1 4 8 0 ; ∗ 23 ∗ 10−3 6−0
”}”
},
”FREEZER” ,
{20 , f a l s e , { 0 . 1 , 0 . 0 , 0 . 9 } , 0 . 9 , 1 . 0 } ,
” t y p e : a n a l o g ; s c h e d u l e : r e s i d e n t i a l −f r e e z e r −d e f a u l t ; power : 500 W” , // e n e r g y : 750
” r e s i d e n t i a l −f r e e z e r −d e f a u l t ” ,
” p o s i t i v e ; n o n z e r o ; weekday−summer {”
”∗
0 ∗ 4−9 1−5 0 . 2 1 0 ; ∗
1 ∗ 4−9 1−5 0 . 2 1 3 ; ∗
2 ∗ 4−9 1−5 0 . 2 0 8 ; ∗
3 ∗ 4−9 1−5
”∗
4 ∗ 4−9 1−5 0 . 2 0 3 ; ∗
5 ∗ 4−9 1−5 0 . 1 9 8 ; ∗
6 ∗ 4−9 1−5 0 . 1 9 0 ; ∗
7 ∗ 4−9 1−5
”∗
8 ∗ 4−9 1−5 0 . 1 8 9 ; ∗
9 ∗ 4−9 1−5 0 . 1 9 4 ; ∗ 10 ∗ 4−9 1−5 0 . 1 9 9 ; ∗ 11 ∗ 4−9 1−5
”∗ 12 ∗ 4−9 1−5 0 . 2 1 1 ; ∗ 13 ∗ 4−9 1−5 0 . 2 1 4 ; ∗ 14 ∗ 4−9 1−5 0 . 2 1 9 ; ∗ 15 ∗ 4−9 1−5
”∗ 16 ∗ 4−9 1−5 0 . 2 3 0 ; ∗ 17 ∗ 4−9 1−5 0 . 2 2 8 ; ∗ 18 ∗ 4−9 1−5 0 . 2 2 9 ; ∗ 19 ∗ 4−9 1−5
”∗ 20 ∗ 4−9 1−5 0 . 2 2 4 ; ∗ 21 ∗ 4−9 1−5 0 . 2 2 3 ; ∗ 22 ∗ 4−9 1−5 0 . 2 1 8 ; ∗ 23 ∗ 4−9 1−5
”}”
” weekend−summer {”
”∗
0 ∗ 4−9 6−0 0 . 2 0 3 ; ∗
1 ∗ 4−9 6−0 0 . 2 0 2 ; ∗
2 ∗ 4−9 6−0 0 . 2 0 2 ; ∗
3 ∗ 4−9 6−0
”∗
4 ∗ 4−9 6−0 0 . 1 9 8 ; ∗
5 ∗ 4−9 6−0 0 . 1 9 5 ; ∗
6 ∗ 4−9 6−0 0 . 1 9 1 ; ∗
7 ∗ 4−9 6−0
”∗
8 ∗ 4−9 6−0 0 . 1 8 4 ; ∗
9 ∗ 4−9 6−0 0 . 1 9 2 ; ∗ 10 ∗ 4−9 6−0 0 . 1 9 7 ; ∗ 11 ∗ 4−9 6−0
”∗ 12 ∗ 4−9 6−0 0 . 2 0 8 ; ∗ 13 ∗ 4−9 6−0 0 . 2 1 9 ; ∗ 14 ∗ 4−9 6−0 0 . 2 1 9 ; ∗ 15 ∗ 4−9 6−0
”∗ 16 ∗ 4−9 6−0 0 . 2 2 5 ; ∗ 17 ∗ 4−9 6−0 0 . 2 2 5 ; ∗ 18 ∗ 4−9 6−0 0 . 2 2 3 ; ∗ 19 ∗ 4−9 6−0
”∗ 20 ∗ 4−9 6−0 0 . 2 2 1 ; ∗ 21 ∗ 4−9 6−0 0 . 2 2 0 ; ∗ 22 ∗ 4−9 6−0 0 . 2 1 5 ; ∗ 23 ∗ 4−9 6−0
”}”
” weekday−w i n t e r {”
”∗
0 ∗ 10−3 1−5 0 . 1 4 9 ; ∗
1 ∗ 10−3 1−5 0 . 1 4 8 ; ∗
2 ∗ 10−3 1−5 0 . 1 4 5 ; ∗
3 ∗ 10−3
0.0070;”
0.1030;”
0.2130;”
0.1560;”
0.1590;”
0.0820”
0.0070;”
0.0430;”
0.3050;”
0.2700;”
0.1980;”
0.0930”
Wh;
0.202;”
0.186;”
0.202;”
0.222;”
0.223;”
0.214”
0.193;”
0.183;”
0.202;”
0.225;”
0.219;”
0.209”
1−5 0 . 1 4 4 ; ”
156
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
{
},
{
},
”∗
4 ∗ 10−3 1−5 0 . 1 4 3 ; ∗
5 ∗ 10−3 1−5 0 . 1 4 0 ; ∗
6 ∗ 10−3 1−5 0 . 1 3 8 ; ∗
7 ∗ 10−3 1−5 0 . 1 3 8 ; ”
”∗
8 ∗ 10−3 1−5 0 . 1 4 0 ; ∗
9 ∗ 10−3 1−5 0 . 1 4 1 ; ∗ 10 ∗ 10−3 1−5 0 . 1 4 2 ; ∗ 11 ∗ 10−3 1−5 0 . 1 4 7 ; ”
”∗ 12 ∗ 10−3 1−5 0 . 1 5 3 ; ∗ 13 ∗ 10−3 1−5 0 . 1 5 4 ; ∗ 14 ∗ 10−3 1−5 0 . 1 5 2 ; ∗ 15 ∗ 10−3 1−5 0 . 1 5 1 ; ”
”∗ 16 ∗ 10−3 1−5 0 . 1 6 1 ; ∗ 17 ∗ 10−3 1−5 0 . 1 7 4 ; ∗ 18 ∗ 10−3 1−5 0 . 1 7 6 ; ∗ 19 ∗ 10−3 1−5 0 . 1 7 6 ; ”
”∗ 20 ∗ 10−3 1−5 0 . 1 7 5 ; ∗ 21 ∗ 10−3 1−5 0 . 1 6 9 ; ∗ 22 ∗ 10−3 1−5 0 . 1 6 0 ; ∗ 23 ∗ 10−3 1−5 0 . 1 5 3 ”
”}”
” weekend−w i n t e r {”
”∗
0 ∗ 10−3 6−0 0 . 1 5 5 ; ∗
1 ∗ 10−3 6−0 0 . 1 5 0 ; ∗
2 ∗ 10−3 6−0 0 . 1 4 3 ; ∗
3 ∗ 10−3 6−0 0 . 1 4 1 ; ”
”∗
4 ∗ 10−3 6−0 0 . 1 4 1 ; ∗
5 ∗ 10−3 6−0 0 . 1 3 9 ; ∗
6 ∗ 10−3 6−0 0 . 1 3 8 ; ∗
7 ∗ 10−3 6−0 0 . 1 3 9 ; ”
”∗
8 ∗ 10−3 6−0 0 . 1 4 2 ; ∗
9 ∗ 10−3 6−0 0 . 1 4 2 ; ∗ 10 ∗ 10−3 6−0 0 . 1 4 5 ; ∗ 11 ∗ 10−3 6−0 0 . 1 5 3 ; ”
”∗ 12 ∗ 10−3 6−0 0 . 1 6 1 ; ∗ 13 ∗ 10−3 6−0 0 . 1 6 2 ; ∗ 14 ∗ 10−3 6−0 0 . 1 6 0 ; ∗ 15 ∗ 10−3 6−0 0 . 1 6 1 ; ”
”∗ 16 ∗ 10−3 6−0 0 . 1 6 5 ; ∗ 17 ∗ 10−3 6−0 0 . 1 7 7 ; ∗ 18 ∗ 10−3 6−0 0 . 1 7 9 ; ∗ 19 ∗ 10−3 6−0 0 . 1 7 7 ; ”
”∗ 20 ∗ 10−3 6−0 0 . 1 7 1 ; ∗ 21 ∗ 10−3 6−0 0 . 1 6 8 ; ∗ 22 ∗ 10−3 6−0 0 . 1 6 0 ; ∗ 23 ∗ 10−3 6−0 0 . 1 5 1 ”
”}”
},
”DISHWASHER” ,
{20 , f a l s e , { 0 . 8 , 0 , 0 . 2 } , 0 . 9 8 , 1 . 0 } ,
” t y p e : a n a l o g ; s c h e d u l e : r e s i d e n t i a l −d i s h w a s h e r −d e f a u l t ; power : 1 . 0 kW” , // e n e r g y : 1 . 0 kWh ;
” r e s i d e n t i a l −d i s h w a s h e r −d e f a u l t ” ,
” normal ; p o s i t i v e ; n o n z e r o ; weekday−summer {”
”∗
0 ∗ 4−9 1−5 0 . 0 0 6 8 ; ∗
1 ∗ 4−9 1−5 0 . 0 0 2 9 ; ∗
2 ∗ 4−9 1−5 0 . 0 0 1 6 ; ∗
3 ∗ 4−9 1−5 0 . 0 0 1 3 ; ”
”∗
4 ∗ 4−9 1−5 0 . 0 0 1 2 ; ∗
5 ∗ 4−9 1−5 0 . 0 0 3 7 ; ∗
6 ∗ 4−9 1−5 0 . 0 0 7 5 ; ∗
7 ∗ 4−9 1−5 0 . 0 1 2 9 ; ”
”∗
8 ∗ 4−9 1−5 0 . 0 1 8 0 ; ∗
9 ∗ 4−9 1−5 0 . 0 1 7 7 ; ∗ 10 ∗ 4−9 1−5 0 . 0 1 4 4 ; ∗ 11 ∗ 4−9 1−5 0 . 0 1 1 3 ; ”
”∗ 12 ∗ 4−9 1−5 0 . 0 1 1 6 ; ∗ 13 ∗ 4−9 1−5 0 . 0 1 2 8 ; ∗ 14 ∗ 4−9 1−5 0 . 0 1 0 9 ; ∗ 15 ∗ 4−9 1−5 0 . 0 1 0 5 ; ”
”∗ 16 ∗ 4−9 1−5 0 . 0 1 2 4 ; ∗ 17 ∗ 4−9 1−5 0 . 0 1 5 6 ; ∗ 18 ∗ 4−9 1−5 0 . 0 2 7 8 ; ∗ 19 ∗ 4−9 1−5 0 . 0 3 4 3 ; ”
”∗ 20 ∗ 4−9 1−5 0 . 0 2 7 9 ; ∗ 21 ∗ 4−9 1−5 0 . 0 2 3 4 ; ∗ 22 ∗ 4−9 1−5 0 . 0 1 9 4 ; ∗ 23 ∗ 4−9 1−5 0 . 0 1 3 1 ”
”}”
” weekend−summer {”
”∗
0 ∗ 4−9 6−0 0 . 0 0 9 3 ; ∗
1 ∗ 4−9 6−0 0 . 0 0 4 5 ; ∗
2 ∗ 4−9 6−0 0 . 0 0 2 1 ; ∗
3 ∗ 4−9 6−0 0 . 0 0 1 5 ; ”
”∗
4 ∗ 4−9 6−0 0 . 0 0 1 3 ; ∗
5 ∗ 4−9 6−0 0 . 0 0 1 5 ; ∗
6 ∗ 4−9 6−0 0 . 0 0 2 6 ; ∗
7 ∗ 4−9 6−0 0 . 0 0 6 7 ; ”
”∗
8 ∗ 4−9 6−0 0 . 0 1 4 2 ; ∗
9 ∗ 4−9 6−0 0 . 0 2 2 1 ; ∗ 10 ∗ 4−9 6−0 0 . 0 2 5 9 ; ∗ 11 ∗ 4−9 6−0 0 . 0 2 3 8 ; ”
”∗ 12 ∗ 4−9 6−0 0 . 0 2 1 4 ; ∗ 13 ∗ 4−9 6−0 0 . 0 2 1 4 ; ∗ 14 ∗ 4−9 6−0 0 . 0 1 8 8 ; ∗ 15 ∗ 4−9 6−0 0 . 0 1 6 9 ; ”
”∗ 16 ∗ 4−9 6−0 0 . 0 1 5 6 ; ∗ 17 ∗ 4−9 6−0 0 . 0 1 6 6 ; ∗ 18 ∗ 4−9 6−0 0 . 0 2 4 9 ; ∗ 19 ∗ 4−9 6−0 0 . 0 2 9 8 ; ”
”∗ 20 ∗ 4−9 6−0 0 . 0 2 6 7 ; ∗ 21 ∗ 4−9 6−0 0 . 0 2 2 1 ; ∗ 22 ∗ 4−9 6−0 0 . 0 1 7 4 ; ∗ 23 ∗ 4−9 6−0 0 . 0 1 4 5 ”
”}”
” weekday−w i n t e r {”
”∗
0 ∗ 10−3 1−5 0 . 0 0 6 8 ; ∗
1 ∗ 10−3 1−5 0 . 0 0 2 9 ; ∗
2 ∗ 10−3 1−5 0 . 0 0 1 6 ; ∗
3 ∗ 10−3 1−5 0 . 0 0 1 3 ; ”
”∗
4 ∗ 10−3 1−5 0 . 0 0 1 2 ; ∗
5 ∗ 10−3 1−5 0 . 0 0 3 7 ; ∗
6 ∗ 10−3 1−5 0 . 0 0 7 5 ; ∗
7 ∗ 10−3 1−5 0 . 0 1 2 9 ; ”
”∗
8 ∗ 10−3 1−5 0 . 0 1 8 0 ; ∗
9 ∗ 10−3 1−5 0 . 0 1 7 7 ; ∗ 10 ∗ 10−3 1−5 0 . 0 1 4 4 ; ∗ 11 ∗ 10−3 1−5 0 . 0 1 1 3 ; ”
”∗ 12 ∗ 10−3 1−5 0 . 0 1 1 6 ; ∗ 13 ∗ 10−3 1−5 0 . 0 1 2 8 ; ∗ 14 ∗ 10−3 1−5 0 . 0 1 0 9 ; ∗ 15 ∗ 10−3 1−5 0 . 0 1 0 5 ; ”
”∗ 16 ∗ 10−3 1−5 0 . 0 1 2 4 ; ∗ 17 ∗ 10−3 1−5 0 . 0 1 5 6 ; ∗ 18 ∗ 10−3 1−5 0 . 0 2 7 8 ; ∗ 19 ∗ 10−3 1−5 0 . 0 3 4 3 ; ”
”∗ 20 ∗ 10−3 1−5 0 . 0 2 7 9 ; ∗ 21 ∗ 10−3 1−5 0 . 0 2 3 4 ; ∗ 22 ∗ 10−3 1−5 0 . 0 1 9 4 ; ∗ 23 ∗ 10−3 1−5 0 . 0 1 3 1 ”
”}”
” weekend−w i n t e r {”
”∗
0 ∗ 10−3 6−0 0 . 0 0 9 3 ; ∗
1 ∗ 10−3 6−0 0 . 0 0 4 5 ; ∗
2 ∗ 10−3 6−0 0 . 0 0 2 1 ; ∗
3 ∗ 10−3 6−0 0 . 0 0 1 5 ; ”
”∗
4 ∗ 10−3 6−0 0 . 0 0 1 3 ; ∗
5 ∗ 10−3 6−0 0 . 0 0 1 5 ; ∗
6 ∗ 10−3 6−0 0 . 0 0 2 6 ; ∗
7 ∗ 10−3 6−0 0 . 0 0 6 7 ; ”
”∗
8 ∗ 10−3 6−0 0 . 0 1 4 2 ; ∗
9 ∗ 10−3 6−0 0 . 0 2 2 1 ; ∗ 10 ∗ 10−3 6−0 0 . 0 2 5 9 ; ∗ 11 ∗ 10−3 6−0 0 . 0 2 3 8 ; ”
”∗ 12 ∗ 10−3 6−0 0 . 0 2 1 4 ; ∗ 13 ∗ 10−3 6−0 0 . 0 2 1 4 ; ∗ 14 ∗ 10−3 6−0 0 . 0 1 8 8 ; ∗ 15 ∗ 10−3 6−0 0 . 0 1 6 9 ; ”
”∗ 16 ∗ 10−3 6−0 0 . 0 1 5 6 ; ∗ 17 ∗ 10−3 6−0 0 . 0 1 6 6 ; ∗ 18 ∗ 10−3 6−0 0 . 0 2 4 9 ; ∗ 19 ∗ 10−3 6−0 0 . 0 2 9 8 ; ”
”∗ 20 ∗ 10−3 6−0 0 . 0 2 6 7 ; ∗ 21 ∗ 10−3 6−0 0 . 0 2 2 1 ; ∗ 22 ∗ 10−3 6−0 0 . 0 1 7 4 ; ∗ 23 ∗ 10−3 6−0 0 . 0 1 4 5 ”
”}”
”RANGE” ,
{40 , true , {1 ,0 ,0} , 0 . 8 5 , 0 . 8 } ,
” t y p e : a n a l o g ; s c h e d u l e : r e s i d e n t i a l −r a n g e−d e f a u l t ; power : 0 . 5 kW” , // e n e r g y : 1 . 0 kWh ;
” r e s i d e n t i a l −r a n g e−d e f a u l t ” ,
” p o s i t i v e ; n o n z e r o ; weekday−summer {”
”∗
0 ∗ 4−9 1−5 0 . 0 0 9 ; ∗
1 ∗ 4−9 1−5 0 . 0 0 8 ; ∗
2 ∗ 4−9 1−5 0 . 0 0 7 ; ∗
3 ∗ 4−9 1−5 0 . 0 0 7 ; ”
”∗
4 ∗ 4−9 1−5 0 . 0 0 8 ; ∗
5 ∗ 4−9 1−5 0 . 0 1 2 ; ∗
6 ∗ 4−9 1−5 0 . 0 2 5 ; ∗
7 ∗ 4−9 1−5 0 . 0 4 0 ; ”
”∗
8 ∗ 4−9 1−5 0 . 0 4 4 ; ∗
9 ∗ 4−9 1−5 0 . 0 4 2 ; ∗ 10 ∗ 4−9 1−5 0 . 0 4 2 ; ∗ 11 ∗ 4−9 1−5 0 . 0 5 3 ; ”
”∗ 12 ∗ 4−9 1−5 0 . 0 5 7 ; ∗ 13 ∗ 4−9 1−5 0 . 0 4 6 ; ∗ 14 ∗ 4−9 1−5 0 . 0 4 4 ; ∗ 15 ∗ 4−9 1−5 0 . 0 5 3 ; ”
”∗ 16 ∗ 4−9 1−5 0 . 0 9 4 ; ∗ 17 ∗ 4−9 1−5 0 . 1 6 8 ; ∗ 18 ∗ 4−9 1−5 0 . 1 4 8 ; ∗ 19 ∗ 4−9 1−5 0 . 0 8 6 ; ”
”∗ 20 ∗ 4−9 1−5 0 . 0 5 3 ; ∗ 21 ∗ 4−9 1−5 0 . 0 3 8 ; ∗ 22 ∗ 4−9 1−5 0 . 0 2 3 ; ∗ 23 ∗ 4−9 1−5 0 . 0 1 3 ”
”}”
” weekend−summer {”
”∗
0 ∗ 4−9 6−0 0 . 0 0 9 ; ∗
1 ∗ 4−9 6−0 0 . 0 0 7 ; ∗
2 ∗ 4−9 6−0 0 . 0 0 7 ; ∗
3 ∗ 4−9 6−0 0 . 0 0 7 ; ”
”∗
4 ∗ 4−9 6−0 0 . 0 0 7 ; ∗
5 ∗ 4−9 6−0 0 . 0 0 9 ; ∗
6 ∗ 4−9 6−0 0 . 0 1 7 ; ∗
7 ∗ 4−9 6−0 0 . 0 3 8 ; ”
”∗
8 ∗ 4−9 6−0 0 . 0 6 0 ; ∗
9 ∗ 4−9 6−0 0 . 0 6 8 ; ∗ 10 ∗ 4−9 6−0 0 . 0 6 5 ; ∗ 11 ∗ 4−9 6−0 0 . 0 6 7 ; ”
”∗ 12 ∗ 4−9 6−0 0 . 0 7 6 ; ∗ 13 ∗ 4−9 6−0 0 . 0 6 6 ; ∗ 14 ∗ 4−9 6−0 0 . 0 6 1 ; ∗ 15 ∗ 4−9 6−0 0 . 0 6 7 ; ”
”∗ 16 ∗ 4−9 6−0 0 . 0 9 1 ; ∗ 17 ∗ 4−9 6−0 0 . 1 3 4 ; ∗ 18 ∗ 4−9 6−0 0 . 1 2 1 ; ∗ 19 ∗ 4−9 6−0 0 . 0 8 0 ; ”
”∗ 20 ∗ 4−9 6−0 0 . 0 5 2 ; ∗ 21 ∗ 4−9 6−0 0 . 0 3 5 ; ∗ 22 ∗ 4−9 6−0 0 . 0 2 2 ; ∗ 23 ∗ 4−9 6−0 0 . 0 1 1 ”
”}”
” weekday−w i n t e r {”
”∗
0 ∗ 10−3 1−5 0 . 0 1 0 ; ∗
1 ∗ 10−3 1−5 0 . 0 0 9 ; ∗
2 ∗ 10−3 1−5 0 . 0 0 9 ; ∗
3 ∗ 10−3 1−5 0 . 0 0 9 ; ”
”∗
4 ∗ 10−3 1−5 0 . 0 0 9 ; ∗
5 ∗ 10−3 1−5 0 . 0 1 6 ; ∗
6 ∗ 10−3 1−5 0 . 0 3 2 ; ∗
7 ∗ 10−3 1−5 0 . 0 5 0 ; ”
”∗
8 ∗ 10−3 1−5 0 . 0 4 5 ; ∗
9 ∗ 10−3 1−5 0 . 0 4 3 ; ∗ 10 ∗ 10−3 1−5 0 . 0 4 5 ; ∗ 11 ∗ 10−3 1−5 0 . 0 5 9 ; ”
”∗ 12 ∗ 10−3 1−5 0 . 0 6 3 ; ∗ 13 ∗ 10−3 1−5 0 . 0 5 3 ; ∗ 14 ∗ 10−3 1−5 0 . 0 5 2 ; ∗ 15 ∗ 10−3 1−5 0 . 0 7 2 ; ”
”∗ 16 ∗ 10−3 1−5 0 . 1 3 8 ; ∗ 17 ∗ 10−3 1−5 0 . 2 4 2 ; ∗ 18 ∗ 10−3 1−5 0 . 1 8 2 ; ∗ 19 ∗ 10−3 1−5 0 . 0 8 8 ; ”
”∗ 20 ∗ 10−3 1−5 0 . 0 5 1 ; ∗ 21 ∗ 10−3 1−5 0 . 0 3 4 ; ∗ 22 ∗ 10−3 1−5 0 . 0 2 2 ; ∗ 23 ∗ 10−3 1−5 0 . 0 1 4 ”
”}”
” weekend−w i n t e r {”
”∗
0 ∗ 10−3 6−0 0 . 0 1 3 ; ∗
1 ∗ 10−3 6−0 0 . 0 1 0 ; ∗
2 ∗ 10−3 6−0 0 . 0 1 0 ; ∗
3 ∗ 10−3 6−0 0 . 0 1 0 ; ”
”∗
4 ∗ 10−3 6−0 0 . 0 1 0 ; ∗
5 ∗ 10−3 6−0 0 . 0 1 2 ; ∗
6 ∗ 10−3 6−0 0 . 0 1 8 ; ∗
7 ∗ 10−3 6−0 0 . 0 4 0 ; ”
”∗
8 ∗ 10−3 6−0 0 . 0 7 3 ; ∗
9 ∗ 10−3 6−0 0 . 0 9 4 ; ∗ 10 ∗ 10−3 6−0 0 . 0 9 1 ; ∗ 11 ∗ 10−3 6−0 0 . 1 0 0 ; ”
”∗ 12 ∗ 10−3 6−0 0 . 1 1 7 ; ∗ 13 ∗ 10−3 6−0 0 . 1 0 9 ; ∗ 14 ∗ 10−3 6−0 0 . 1 0 0 ; ∗ 15 ∗ 10−3 6−0 0 . 1 0 8 ; ”
”∗ 16 ∗ 10−3 6−0 0 . 1 5 3 ; ∗ 17 ∗ 10−3 6−0 0 . 2 1 5 ; ∗ 18 ∗ 10−3 6−0 0 . 1 6 1 ; ∗ 19 ∗ 10−3 6−0 0 . 0 8 5 ; ”
”∗ 20 ∗ 10−3 6−0 0 . 0 5 0 ; ∗ 21 ∗ 10−3 6−0 0 . 0 3 3 ; ∗ 22 ∗ 10−3 6−0 0 . 0 2 2 ; ∗ 23 ∗ 10−3 6−0 0 . 0 1 4 ”
”}”
157
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
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925
926
927
928
929
930
931
932
933
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937
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940
941
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944
945
946
947
948
949
−
{
”MICROWAVE” ,
−
{40 , f a l s e , {0 ,0 ,1} , 0 . 7 , 0 . 8 } ,
−
” t y p e : a n a l o g ; s c h e d u l e : r e s i d e n t i a l −microwave−d e f a u l t ; power : 0 . 2 kW” , // e n e r g y : 1 . 0 kWh ;
−
” r e s i d e n t i a l −microwave−d e f a u l t ” ,
−
” p o s i t i v e ; n o n z e r o ; weekday−summer {”
−
”∗
0 ∗ 4−9 1−5 0 . 0 0 9 ; ∗
1 ∗ 4−9 1−5 0 . 0 0 8 ; ∗
2 ∗ 4−9 1−5 0 . 0 0 7 ; ∗
3 ∗ 4−9 1−5 0 . 0 0 7 ; ”
−
”∗
4 ∗ 4−9 1−5 0 . 0 0 8 ; ∗
5 ∗ 4−9 1−5 0 . 0 1 2 ; ∗
6 ∗ 4−9 1−5 0 . 0 2 5 ; ∗
7 ∗ 4−9 1−5 0 . 0 4 0 ; ”
−
”∗
8 ∗ 4−9 1−5 0 . 0 4 4 ; ∗
9 ∗ 4−9 1−5 0 . 0 4 2 ; ∗ 10 ∗ 4−9 1−5 0 . 0 4 2 ; ∗ 11 ∗ 4−9 1−5 0 . 0 5 3 ; ”
−
”∗ 12 ∗ 4−9 1−5 0 . 0 5 7 ; ∗ 13 ∗ 4−9 1−5 0 . 0 4 6 ; ∗ 14 ∗ 4−9 1−5 0 . 0 4 4 ; ∗ 15 ∗ 4−9 1−5 0 . 0 5 3 ; ”
−
”∗ 16 ∗ 4−9 1−5 0 . 0 9 4 ; ∗ 17 ∗ 4−9 1−5 0 . 1 6 8 ; ∗ 18 ∗ 4−9 1−5 0 . 1 4 8 ; ∗ 19 ∗ 4−9 1−5 0 . 0 8 6 ; ”
−
”∗ 20 ∗ 4−9 1−5 0 . 0 5 3 ; ∗ 21 ∗ 4−9 1−5 0 . 0 3 8 ; ∗ 22 ∗ 4−9 1−5 0 . 0 2 3 ; ∗ 23 ∗ 4−9 1−5 0 . 0 1 3 ”
−
”}”
−
” weekend−summer {”
−
”∗
0 ∗ 4−9 6−0 0 . 0 0 9 ; ∗
1 ∗ 4−9 6−0 0 . 0 0 7 ; ∗
2 ∗ 4−9 6−0 0 . 0 0 7 ; ∗
3 ∗ 4−9 6−0 0 . 0 0 7 ; ”
−
”∗
4 ∗ 4−9 6−0 0 . 0 0 7 ; ∗
5 ∗ 4−9 6−0 0 . 0 0 9 ; ∗
6 ∗ 4−9 6−0 0 . 0 1 7 ; ∗
7 ∗ 4−9 6−0 0 . 0 3 8 ; ”
−
”∗
8 ∗ 4−9 6−0 0 . 0 6 0 ; ∗
9 ∗ 4−9 6−0 0 . 0 6 8 ; ∗ 10 ∗ 4−9 6−0 0 . 0 6 5 ; ∗ 11 ∗ 4−9 6−0 0 . 0 6 7 ; ”
−
”∗ 12 ∗ 4−9 6−0 0 . 0 7 6 ; ∗ 13 ∗ 4−9 6−0 0 . 0 6 6 ; ∗ 14 ∗ 4−9 6−0 0 . 0 6 1 ; ∗ 15 ∗ 4−9 6−0 0 . 0 6 7 ; ”
−
”∗ 16 ∗ 4−9 6−0 0 . 0 9 1 ; ∗ 17 ∗ 4−9 6−0 0 . 1 3 4 ; ∗ 18 ∗ 4−9 6−0 0 . 1 2 1 ; ∗ 19 ∗ 4−9 6−0 0 . 0 8 0 ; ”
−
”∗ 20 ∗ 4−9 6−0 0 . 0 5 2 ; ∗ 21 ∗ 4−9 6−0 0 . 0 3 5 ; ∗ 22 ∗ 4−9 6−0 0 . 0 2 2 ; ∗ 23 ∗ 4−9 6−0 0 . 0 1 1 ”
−
”}”
−
” weekday−w i n t e r {”
−
”∗
0 ∗ 10−3 1−5 0 . 0 1 0 ; ∗
1 ∗ 10−3 1−5 0 . 0 0 9 ; ∗
2 ∗ 10−3 1−5 0 . 0 0 9 ; ∗
3 ∗ 10−3 1−5 0 . 0 0 9 ; ”
−
”∗
4 ∗ 10−3 1−5 0 . 0 0 9 ; ∗
5 ∗ 10−3 1−5 0 . 0 1 6 ; ∗
6 ∗ 10−3 1−5 0 . 0 3 2 ; ∗
7 ∗ 10−3 1−5 0 . 0 5 0 ; ”
−
”∗
8 ∗ 10−3 1−5 0 . 0 4 5 ; ∗
9 ∗ 10−3 1−5 0 . 0 4 3 ; ∗ 10 ∗ 10−3 1−5 0 . 0 4 5 ; ∗ 11 ∗ 10−3 1−5 0 . 0 5 9 ; ”
−
”∗ 12 ∗ 10−3 1−5 0 . 0 6 3 ; ∗ 13 ∗ 10−3 1−5 0 . 0 5 3 ; ∗ 14 ∗ 10−3 1−5 0 . 0 5 2 ; ∗ 15 ∗ 10−3 1−5 0 . 0 7 2 ; ”
−
”∗ 16 ∗ 10−3 1−5 0 . 1 3 8 ; ∗ 17 ∗ 10−3 1−5 0 . 2 4 2 ; ∗ 18 ∗ 10−3 1−5 0 . 1 8 2 ; ∗ 19 ∗ 10−3 1−5 0 . 0 8 8 ; ”
−
”∗ 20 ∗ 10−3 1−5 0 . 0 5 1 ; ∗ 21 ∗ 10−3 1−5 0 . 0 3 4 ; ∗ 22 ∗ 10−3 1−5 0 . 0 2 2 ; ∗ 23 ∗ 10−3 1−5 0 . 0 1 4 ”
−
”}”
−
” weekend−w i n t e r {”
−
”∗
0 ∗ 10−3 6−0 0 . 0 1 3 ; ∗
1 ∗ 10−3 6−0 0 . 0 1 0 ; ∗
2 ∗ 10−3 6−0 0 . 0 1 0 ; ∗
3 ∗ 10−3 6−0 0 . 0 1 0 ; ”
−
”∗
4 ∗ 10−3 6−0 0 . 0 1 0 ; ∗
5 ∗ 10−3 6−0 0 . 0 1 2 ; ∗
6 ∗ 10−3 6−0 0 . 0 1 8 ; ∗
7 ∗ 10−3 6−0 0 . 0 4 0 ; ”
−
”∗
8 ∗ 10−3 6−0 0 . 0 7 3 ; ∗
9 ∗ 10−3 6−0 0 . 0 9 4 ; ∗ 10 ∗ 10−3 6−0 0 . 0 9 1 ; ∗ 11 ∗ 10−3 6−0 0 . 1 0 0 ; ”
−
”∗ 12 ∗ 10−3 6−0 0 . 1 1 7 ; ∗ 13 ∗ 10−3 6−0 0 . 1 0 9 ; ∗ 14 ∗ 10−3 6−0 0 . 1 0 0 ; ∗ 15 ∗ 10−3 6−0 0 . 1 0 8 ; ”
−
”∗ 16 ∗ 10−3 6−0 0 . 1 5 3 ; ∗ 17 ∗ 10−3 6−0 0 . 2 1 5 ; ∗ 18 ∗ 10−3 6−0 0 . 1 6 1 ; ∗ 19 ∗ 10−3 6−0 0 . 0 8 5 ; ”
−
”∗ 20 ∗ 10−3 6−0 0 . 0 5 0 ; ∗ 21 ∗ 10−3 6−0 0 . 0 3 3 ; ∗ 22 ∗ 10−3 6−0 0 . 0 2 2 ; ∗ 23 ∗ 10−3 6−0 0 . 0 1 4 ”
−
”}”
−
},
−
−
/// @todo add o t h e r i m p l i c i t e n d u s e s c h e d u l e s and s h a p e s a s t h e y a r e d e f i n e d
+#i n c l u d e ” b s r a 2 0 1 4 . h”
};
EXPORT CIRCUIT ∗ a t t a c h e n d u s e h o u s e e (OBJECT ∗ o b j ,
enduse ∗ t a r g e t ,
double breaker amps ,
int
is220 )
158
Appendix C
Glossary
Area control error
The instantaneous difference between the actual and scheduled interchange,
taking into account the effects of frequency bias.
Baseload
The minimum load for a given control area. This load is constant.
Baseload generating capacity
The fleet of generators that operate continuously around the clock, as opposed
to midload and peakload generators (see peaker ).
Bulk (power) system
See grid.
Clearing price/quantity
The price (and quantity) that results in a market with supply equal to demand.
Congestion
A congested line is one that would be overloaded were a limit not enforced. In a
market, the path from A to B is congested if the price at B is greater than the
price at A.
Contingency
The unexpected failure or outage of a system component, such as a generator,
transmission asset, or load. A single contingency may involve multiple assets.
Control area
The bounded region of an electricity interconnection through which interchange
is observed and within which generation is controlled to manage the scheduled
flow of power.
Demand
The amount of power that would be consumed by loads if the system were
operating at normal frequency and voltage for all consumers, including losses.
159
Demand elasticity
The responsiveness of the quantity demanded of a good to its own price. Short
for price elasticity of demand. Defined as the fractional change in demand in
response to a fractional change in price, or (P/Q)(dQ/dP ).
Dispatch
The operation and control a power system by determining the outputs of
generators necessary to satisfy demand.
Double auction
A market where both buyers and sellers of a commodity meet at one place or
communicate with a central auctioneer to buy or sell an asset.
Economic dispatch
Dispatch that satisfies a minimum production cost objective given transmission
constraints.
Equilibrium demand/load
The demand/load in the absence of any changing conditions after a quasi-infinite
time.
Frequency
The rate at which alternating current complete a cycle of two reversals of
direction. There is a single frequency for an entire interconnection and control
areas cannot have different individual frequencies.
Grid
An electricity transmission network.
Interconnection
One of the five major bulk power systems in North America: Eastern, Western,
Texas, Quebec, and Alaska. An interconnection is the largest synchronized
portion of any power system and must have a single frequency. Thus only DC
lines can connect interconnections.
Independent system operator
A non-profit entity that runs the real-time balancing market and often also the
day-ahead energy markets.
Locational Marginal Price
The different derivatives of price with respect to a change in quantity for nodes
in an interconnection that arise from congestion in the system.
Load
An end-use device or customer that consumes power from the electric system.
Sometimes used interchangeably with demand but it is in fact subtly different.
160
Demand is what the load would have been were there no constraints or incentives
for it to change. Sometimes demand refers to the entire ranges of possible loads,
while load refers to realized value given system conditions.
Marginal Forgone Retail Rate
The consumer’s opportunity cost for not consuming the last increment of demand.
Marginal production cost
The derivative of the total cost with respect to output. Includes fixed, variable,
startup, no-load and shutdown costs.
Fixed cost: Includes debt and equity costs that do not depend on output.
Variable cost: Includes costs that vary as a function of output.
Startup/shutdown cost: Includes costs that are incurred as a result of starting and stopping.
No-load cost: Includes costs of running a plant at zero-output.
Market
Any situation in which the sale and purchase of goods or services takes place.
Market power
The ability of a market participant to profitably alter prices away from the
competitive equilibrium, i.e., the conditions where (a) there are one or more
price-taking producers to supply a given quantity and (b) there are one or more
price-taking consumers to demand the same quantity.
Monopoly
The condition where a single market participant can act as the sole supplier of a
good or service for which there are no substitutes and many buyers.
Monopsony
The condition where a single market participant can act as the sole consumer of
a good or service for which there are no substitutes and many sellers.
Reserve (resources)
Generating resources available on short notice in excess of demand.
Regulation reserves: Generation that is used constantly to balance fluctuations in load and intermittent generation.
Spinning reserves: Generation that is available online that can respond to a
contingency.
Non-spinning reserves: Generation that is available offline to replace spinning
reservces in the event of a contingency.
Planning reserve: The difference between the peak load and the peak capacity.
161
Outage
The forced or planned removal of a generation unit, transmission line or load
from service.
Peaker
A peak-load plant that follows diurnal load fluctuations on the most heavily
loaded days of the year. Peakers usually have higher variable costs and lower
fixed costs.
Power
The rate of flow of energy, specified as real or reactive power.
Real power The power delivered to the load that can be converted to actual
work.
Reactive power The power necessary to deliver real power to the load while
maintaining the AC voltage and current relationship in the transmission
system.
Losses The power lost as heat in the transmission system between the generation
and load.
Price
The price of electric energy (measured in $/M W h or ¢/kW h).
Profit
The income from the sale of a good or service less the production costs.
Quantity
The amount of electric energy (measured in MWh or kWh).
Ramping
The increasing or decreasing output capability of a generator.
Reliability
The ability of power system to deliver power within the normal voltage and
frequency constraints. A power system is reliable if it satisfies both adequacy
and security limits.
Adequacy: The ability of the power system to supply the load at all times,
taking into account scheduled and reasonable unscheduled outages.
Security: The ability of the power system to withstand disturbances such as
electrical faults and sudden loss of assets and services.
Strike Price
The price at which a market participant exercises an option to buy or sell a
good or service.
162
Surplus
The difference between the product’s value to the consumer and its cost of
production. Also the sum of the consumer and producer surpluses.
Consumer surplus The benefit to the consumer who consumes the good
relative to the benefit to the same consumer when he does not consume
the good.
Producer surplus The profit to the producer who produces the good relative
to the profit to the same producer when he does not produce the good.
Tariff
The body of rules governing the prices at which energy is bought and sold in a
market.
Unit commitment
The starting of a generator. Solving the optimal unit commitment is a complex
mathematical problem.
Withholding
Reducing output below the competitive price-taking level at the market clearing
price. It can be financial by increasing the asking price, or physical by decreasing
the offer quantity.
163
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