MODELING AND CONTROL OF HYBRID AC/DC MICRO GRID Department of Electrical Engineering
MODELING AND CONTROL OF HYBRID
AC/DC MICRO GRID
A Thesis Submitted in Partial Fulfillment of the Requirements for the Award of the Degree of
Master of Technology
in
Power Control & Drives
by
Lipsa Priyadarshanee
Department of Electrical Engineering
National Institute of Technology
Rourkela769008
MODELING AND CONTROL OF HYBRID
AC/DC MICROGRID
A Thesis Submitted in Partial Fulfillment of the Requirements for the Award of the Degree of
Master of Technology
in
Power Control & Drives
by
Lipsa Priyadarshanee
(210EE2108)
Under the supervision of
Prof. Anup Kumar Panda
Department of Electrical Engineering
National Institute of Technology
Rourkela769008
Dedicated to my beloved parents & brother
DEPARTMENT OF ELECTRICAL ENGINEERING
NATIONAL INSTITUTE OF TECHNOLOGY, ROURKELA
ODISHA, INDIA
CERTIFICATE
This is to certify that the Thesis Report entitled “MODELING AND CONTROL OF
HYBRID AC/DC MICROGRID”, submitted by Ms. LIPSA PRIYADARSHANEE bearing roll no. 210EE2108 in partial fulfillment of the requirements for the award of Master of
Technology in Electrical Engineering with specialization in “Power Control and Drives” during session 20102012 at National Institute of Technology, Rourkela is an authentic work carried out by him under our supervision and guidance.
To the best of our knowledge, the matter embodied in the thesis has not been submitted to any other university/institute for the award of any Degree or Diploma.
Date: Prof. A. K. Panda
Place: Rourkela Department of Electrical Engineering
National Institute of Technology
Rourkela – 769008
Email: [email protected]
i
ACKNOWLEDGEMENT
With deep regards and profound respect, I avail this opportunity to express my deep sense of gratitude and indebtedness to my supervisor Professor Anup Kumar Panda, Electrical
Engineering Department, National Institute of Technology, Rourkela for his inspiring guidance, constructive criticism and valuable suggestion throughout this work. It would have not been possible for me to bring out this thesis without his help and constant encouragement.
I am also thankful to all faculty members and research students of Electrical
Department, NIT Rourkela. I am especially grateful to Power Electronics Laboratory staff Mr. Rabindra Nayak without him the work would have not progressed.
Most important of all, I would like to express my gratitude to my parents, my brother and best of friends for their constant love, affection, endless encouragement and noble devotion to my education. I am enormously grateful to all my close friends of NIT Rourkela for supporting me in all circumstances and making my stay here memorable. I am truly indebted to all my friends and relatives for their kind support. I am also equally thankful to all those who have contributed, directly or indirectly, to this present work. Last but not the least; I am sure this section would not come to an end without remaining indebted to God Almighty, the
Guide of all guides who has dispelled the envelope of my ignorance with his radiance of knowledge. I dedicate this thesis to my family, brother Pintu and best friends Tapu and Subh.
Lipsa Priyadarshanee ii
ABSTRACT
Renewable energy based distributed generators (DGs) play a dominant role in electricity production, with the increase in the global warming. Distributed generation based on wind, solar energy, biomass, minihydro along with use of fuel cells and microturbines will give significant momentum in near future. Advantages like environmental friendliness, expandability and flexibility have made distributed generation, powered by various renewable and nonconventional microsources, an attractive option for configuring modern electrical grids. A microgrid consists of cluster of loads and distributed generators that operate as a single controllable system. As an integrated energy delivery system microgrid can operate in parallel with or isolated from the main power grid. The microgrid concept introduces the reduction of multiple reverse conversions in an individual AC or DC grid and also facilitates connections to variable renewable AC and DC sources and loads to power systems. The interconnection of DGs to the utility/grid through power electronic converters has risen concerned about safe operation and protection of equipment’s. To the customer the microgrid can be designed to meet their special requirements; such as, enhancement of local reliability, reduction of feeder losses, local voltages support, increased efficiency through use of waste heat, correction of voltage sag or uninterruptible power supply.
In the present work the performance of hybrid AC/DC microgrid system is analyzed in the grid tied mode. Here photovoltaic system, wind turbine generator and battery are used for the development of microgrid. Also control mechanisms are implemented for the converters to properly coordinate the AC subgrid to DC subgrid. The results are obtained from the MATLAB/
SIMULINK environment.
iii
Certificate
Acknowledgements
Abstract
List of figures
List of tables
Acronyms
Chapter 1 Introduction to microgrid
1.1 Introduction
1.1.1 General information regarding microgrid
1.1.2 Technical challenges in microgrid
1.2 Literature review
1.3 Motivation
1.4 Objective
1.5 Thesis organization
Chapter 2 Photovoltaic system and battery
2.1 Photovoltaic system
2.1.1 Photovoltaic arrangements
2.1.1.1 Photovoltaic cell
2.1.1.2 Photovoltaic module
2.1.1.3 Photovoltaic array
2.1.2 Working of PV cell
2.1.3 Modeling of PV panel
2.2 Maximum power point tracking
2.2.1 Necessity of maximum power point tracking
2.2.2 Algorithm for tracking of maximum power point
TABLE OF CONTENTS
11
11
11
12
9
9
1
3
4
8
1
17
17
18
12
13
14 iii vii i ii ix x
iv
2.2.2.1 Perturb and observe
2.2.2.2 Incremental conductance
2.2.2.3 Parasitic capacitances
2.2.2.4 Voltage control maximum power point tracker
2.2.2.5 Current control maximum power tracker
2.3 Battery
2.3.1 Modeling of battery
2.4 Summary
Chapter 3 Doubly fed induction generator
3.1 Wind turbine
3.2 DFIG system
3.2.1 Mathematical modeling of induction generator
3.2.1.1 Modeling of DFIG in synchronously rotating frame
3.2.1.2 Dynamic modeling of DFIG in state space equations
3.3 Summary
Chapter 4 AC/DC Microgrid
4.1 Configuration of hybrid microgrid
4.2 Operation of grid
4.3 Modeling and control of converters
4.3.1 Modeling and control of boost converter
4.3.2 Modeling and control of main converter
4.3.3 Modeling and control of DFIG
4.3.3.1 Control of grid side converter
4.3.3.2 Control of machine side converter
4.3.4 Modeling and control of battery
4.4 Summary
26
26
28
25
26
32
33
36
22
22
23
24
18
20
21
22
38
40
42
36
36
37
45
45
v
Chapter 5 Results and discussions
5.1 Simulation of PV array
5.2 Simulation of doubly fed induction generator
5.3 Simulation results of hybrid grid
5.4 Summary
Chapter 6 Conclusion and suggestions for future work
6.1 Conclusions
6.2 Suggestions for future work
References
56
57
57
58
46
48
50
vi
LIST OF FIGURES
Fig 1.1. Microgrid power system
Fig 2.1. Basic structure of PV cell
Fig 2.2. Photovoltaic system
Fig 2.3. Working of PV cell
Fig 2.4. Equivalent circuit of a solar cell
Fig 2.5. MPP characteristic
Fig 2.6. Perturb and observe algorithm
Fig 2.7. Flowchart Perturb and observe algorithm
Fig 2.8. Incremental conductance algorithm
Fig 2.9. Model of battery
Fig 3.1. Dynamic dq equivalent circuit of DFIG (qaxis circuit)
Fig 3.2. Dynamic dq equivalent circuit of DFIG (qaxis circuit)
Fig 4.1. A hybrid AC/DC microgrid system
Fig 4.2. Representation of hybrid microgrid
Fig 4.3. Control block diagram of boost converter
Fig 4.4. Control block diagram of main converter
Fig. 4.5. Overall DFIG system
Fig 4.6. Schematic diagram of grid side converter
Fig 4.7. Control block diagram of grid side converter
Fig 4.8. Control block diagram of machine side converter
Fig 4.9. Control block diagram of battery
Fig 5.1. IV output characteristics of PV array for different temperatures
Fig 5.2. PV output characteristics of PV array for different temperatures
Fig 5.3. PI output characteristics of PV array for different temperatures
Fig 5.4. IV characteristics of PV array for different irradiance levels
40
41
43
37
38
39
45
46
47
47
47
19
20
23
14
17
18
1
11
13
13
26
27
33
34
vii
Fig 5.5. PV characteristics of PV array for different irradiance levels
Fig 5.6. PI characteristics of PV array for different irradiance levels
Fig 5.7. Response of wind speed
Fig 5.8. Three phase stator voltage of DFIG
Fig 5.9. Three phase rotor voltage of DFIG
Fig 5.10. Irradiation signal of the PV array
Fig 5.11. Output voltage of PV array
Fig 5.12. Output current of PV array
Fig 5.13. Output power of PV array
Fig 5.14. Generated PWM signal for the boost converter
Fig 5.15. Output voltage across DC load
Fig 5.16. State of charge of battery
Fig 5.17. Voltage of battery
Fig 5.18. Current of battery
Fig 5.19. Output voltage across AC load
Fig 5.20. Output current across AC load
Fig 5.21. AC side voltage of the main converter
Fig 5.22. AC side current of the main converter
Fig 5.23. Output power of DFIG
Fig 5.24. Three phase supply voltage of utility grid
Fig 5.25. Three phase PWM inverter voltage
49
50
50
51
48
48
49
49
52
52
53
51
51
52
53
53
54
54
55
55
55
viii
LIST OF TABLES
2.1 Parameters for photovoltaic panel
3.1 Parameters for DFIG System
4.1 Component parameters for hybrid grid 35
16
32
ix
I
V
T
T
λ q k
A
K
E
I
I
I
I
V
R
R
N
N
P
V
ACRONYMS
Terminal voltage of PV module
Output current of PV module
Light generated current or photocurrent
Module reverse saturated current
Cell's shortcircuit current
Cell's reverse saturation current at reference temperature
Open circuit voltage
Electron charge
Boltzmann’s constant
Ideal factor cell’s shortcircuit current temperature coefficient
Energy of the band gap of the silicon
Cell's working Temperature
Cell's reference Temperature
Solar irradiation
Series resistance of PV cell
Parallel resistance of PV cell
Number of cells in parallel
Number of cells in series
Maximum power
Terminal voltage of PV cell at MPP
x
I
γ
B
SOC
P
$%
ρ
A
V
∞
C
Q
A i
K
V
V
R
λ
ω
R v
( v
* v
(%
Output current of PV cell at MPP
Cell fill factor of PV cell
Terminal voltage of battery
Open circuit voltage of battery
Internal resistance of battery
Battery charging current
Polarization voltage
Battery capacity
Exponential voltage
Exponential capacity
State of charge of battery
Power contained in wind
The air density
The swept area
The wind velocity without rotor interference
Power coefficient
Tip speed ratio
Rotational speed of rotor
The radius of the swept area d axis stator voltage q axis stator voltage d
%
axis rotor voltage
xi
i
( i
* i
(% i
*%
λ
(
λ
*
λ
(%
λ
*%
λ
(
λ
* i
( i
* i
(% i
*% v
*% v
( v
* v
(% v
*%
λ
(%
λ
*%
θ q
%
axis rotor voltage daxis stator voltage qaxis stator voltage daxis rotor voltage qaxis rotor voltage d axis stator current q axis stator current d
%
axis rotor current q
%
axis rotor current daxis stator current qaxis stator current daxis rotor current qaxis rotor current d axis stator flux linkage q axis stator flux linkage d
%
axis rotor flux linkage q
%
axis rotor flux linkage daxis stator flux linkage qaxis stator flux linkage daxis rotor flux linkage qaxis rotor flux linkage
Angle of synchronously rotating frame
xii
θ
R
R
%
ω
ω
%
ω
ω
L
%
L
P f
L
/
L
/%
L
T
T
0
J
B
P
Q v
( v
*
Angle of stationary reference frame
Stator resistance
Rotor resistance with respect to stator
Synchronous speed
Rotor electrical speed
Rotor mechanical speed
Angular frequency
Supply frequency
Stator leakage inductance
Rotor leakage inductance
Stator inductance
Rotor inductance
Magnetizing inductance
Number of poles
Electromagnetic torque
Load torque
Rotor inertia
Damping constant
Active power in the grid
Reactive power in the grid daxis grid voltage qaxis grid voltage
xiii
C v
(2 m
/ m
4
P
25%
P
25
X
/
X
/%
X
X
%
X
C
R
L i
( i
*
L
/
C
(
L
4
R
4
C
4
L
7 daxis grid current qaxis grid current
Line resistance
Line inductance
DClink capacitance
DClink voltage
Modulation index of supply side converter
Modulation index of machine side converter
Rotor copper loss
Stator copper loss
Stator leakage reactance
Rotor leakage reactance
Stator reactance
Rotor reactance
Magnetizing reactance
Capacitor across the solar panel
Inductor for the boost converter
Capacitor across the DClink
Filtering inductor for the inverter
Equivalent resistance of the inverter
Filtering capacitor for the inverter
Inductor for the battery converter
xiv
R
7 f f
V
(
Resistance of L
7
Frequency of the AC grid
Switching frequency for the power converter
Rated DC bus voltage
xv
CHAPTER 1
INTRODUCTION TO MICROGRID
1.1. Introduction
1.1.1. General information regarding microgrid
As electric distribution technology steps into the next century, many trends are becoming noticeable that will change the requirements of energy delivery. These modifications are being driven from both the demand side where higher energy availability and efficiency are desired and from the supply side where the integration of distributed generation and peakshaving technologies must be accommodated [1].
Fig 1.1. Microgrid power system
1
Power systems currently undergo considerable change in operating requirements mainly as a result of deregulation and due to an increasing amount of distributed energy resources
(DER). In many cases DERs include different technologies that allow generation in small scale (microsources) and some of them take advantage of renewable energy resources (RES) such as solar, wind or hydro energy. Having microsources close to the load has the advantage of reducing transmission losses as well as preventing network congestions. Moreover, the possibility of having a power supply interruption of endcustomers connected to a low voltage (LV) distribution grid (in Europe 230 V and in the USA 110 V) is diminished since adjacent microsources, controllable loads and energy storage systems can operate in the islanded mode in case of severe system disturbances. This is identified nowadays as a microgrid. Figure 1.1 depicts a typical microgrid. The distinctive microgrid has the similar size as a low voltage distribution feeder and will rare exceed a capacity of 1 MVA and a geographic span of 1 km. Generally more than 90% of low voltage domestic customers are supplied by underground cable when the rest is supplied by overhead lines. The microgrid often psupplies both electricity and heat to the customers by means of combined heat and power plants (CHP), gas turbines, fuel cells, photovoltaic (PV) systems, wind turbines, etc.
The energy storage systems usually include batteries and flywheels [2].The storing device in the microgrid is equivalent to the rotating reserve of large generators in the conventional grid which ensures the balance between energy generation and consumption especially during rapid changes in load or generation [3].
From the customer point of view, microgrids deliver both thermal and electricity requirements and in addition improve local reliability, reduce emissions, improve power excellence by supportive voltage and reducing voltage dips and potentially lower costs of energy supply. From the utility viewpoint, application of distributed energy sources can potentially reduce the demand for distribution and transmission facilities. Clearly, distributed generation located close to loads will reduce flows in transmission and distribution circuits with two important effects: loss reduction and ability to potentially substitute for network assets. In addition, the presence of generation close to demand could increase service quality seen by end customers. Microgrids can offer network support during the time of stress by relieving congestions and aiding restoration after faults. The development of microgrids can contribute to the reduction of emissions and the mitigation of climate changes. This is due to the availability and developing technologies for distributed generation units are based on renewable sources and micro sources that are characterized by very low emissions
[4]
.
2
There are various advantages offered by microgrids to endconsumers, utilities and society, such as: improved energy efficiency, minimized overall energy consumption, reduced greenhouse gases and pollutant emissions, improved service quality and reliability, cost efficient electricity infrastructure replacement [2].
Technical challenges linked with the operation and controls of microgrids are immense.
Ensuring stable operation during network disturbances, maintaining stability and power quality in the islanding mode of operation necessitates the improvement of sophisticated control strategies for microgrid’s inverters in order to provide stable frequency and voltage in the presence of arbitrarily varying loads [4]. In light of these, the microgrid concept has stimulated many researchers and attracted the attention of governmental organizations in
Europe, USA and Japan. Nevertheless, there are various technical issues associated with the integration and operation of microgrids.
1.1.2. Technical challenges in microgrid
Protection system is one of the major challenges for microgrid which must react to both main grid and microgrid faults. The protection system should cut off the microgrid from the main grid as rapidly as necessary to protect the microgrid loads for the first case and for the second case the protection system should isolate the smallest part of the microgrid when clears the fault [30]. A segmentation of microgrid, i.e. a design of multiple islands or submicrogrids must be supported by microsource and load controllers. In these conditions problems related to selectivity (false, unnecessary tripping) and sensitivity (undetected faults or delayed tripping) of protection system may arise. Mainly, there are two main issues concerning the protection of microgrids, first is related to a number of installed DER units in the microgrid and second is related to an availability of a sufficient level of shortcircuit current in the islanded operating mode of microgrid since this level may substantially drop down after a disconnection from a stiff main grid. In [30] the authors have made shortcircuit current calculations for radial feeders with DER and studied that shortcircuit currents which are used in overcurrent (OC) protection relays depend on a connection point of and a feedin power from DER. The directions and amplitudes of short circuit currents will vary because of these conditions. In reality the operating conditions of microgrid are persistently varying because of the intermittent microsources (wind and solar) and periodic load variation. Also the network topology can be changed frequently which aims to minimize loss or to achieve other economic or operational targets. In addition controllable islands of different size and content can be formed as a result of faults in the main grid or inside microgrid. In such
3
situations a loss of relay coordination may happen and generic OC protection with a single setting group may become insufficient, i.e. it will not guarantee a selective operation for all possible faults. Hence, it is vital to ensure that settings chosen for OC protection relays take into account a grid topology and changes in location, type and amount of generation.
Otherwise, unwanted operation or failure may occur during necessary condition. To deal with bidirectional power flows and low shortcircuit current levels in microgrids dominated by microsources with power electronic interfaces a new protection philosophy is essential, where setting parameters of relays must be checked/updated periodically to make sure that they are still appropriate.
1.2. Literature review
The popularity of distributed generation systems is growing faster from last few years because of their higher operating efficiency and low emission levels. Distributed generators make use of several microsources for their operation like photovoltaic cells, batteries, micro turbines and fuel cells. During peak load hours DGs provide peak generation when the energy cost is high and stand by generation during system outages. Microgrid is built up by combining cluster of loads and parallel distributed generation systems in a certain local area.
Microgrids have large power capacity and more control flexibility which accomplishes the reliability of the system as well as the requirement of power quality. Operation of microgrid needs implementation of high performance power control and voltage regulation algorithm
[1][5].
To realize the emerging potential of distributed generation, a system approach i.e. microgrid is proposed which considers generation and associated loads as a subsystem. This approach involves local control of distributed generation and hence reduces the need for central dispatch. During disturbances by islanding generation and loads, local reliability can be higher in microgrid than the whole power system. This application makes the system efficiency double. The current implementation of microgrid incorporates sources with loads, permits for intentional islanding and use available waste heat of power generation systems
[6].
Microgrid operates as a single controllable system which offers both power and heat to its local area. This concept offers a new prototype for the operation of distributed generation. To the utility microgrid can be regarded as a controllable cell of power system. In case of faults in microgrid, the main utility should be isolated from the distribution section as fast as
4
necessary to protect loads. The isolation depends on customer’s load on the microgrid. Sag compensation can be used in some cases with isolation from the distribution system to protect the critical loads [2].
The microgrid concept lowers the cost and improves the reliability of small scale distributed generators. The main purpose of this concept is to accelerate the recognition of the advantage offered by small scale distributed generators like ability to supply waste heat during the time of need. From a grid point of view, microgrid is an attractive option as it recognizes that the nation’s distribution system is extensive, old and will change very slowly.
This concept permits high penetration of distribution generation without requiring redesign of the distribution system itself [7].
The microgrid concept acts as solution to the problem of integrating large amount of micro generation without interrupting the utility network’s operation. The microgrid or distribution network subsystem will create less trouble to the utility network than the conventional micro generation if there is proper and intelligent coordination of micro generation and loads. In case of disturbances on the main network, microgrid could potentially disconnect and continue to operate individually, which helps in improving power quality to the consumer [8].
With advancement in DGs and microgrids there is development of various essential power conditioning interfaces and their associated control for tying multiple microsources to the microgrid, and then tying the microgrids to the traditional power systems. Microgrid operation becomes highly flexible, with such interconnection and can be operated freely in the grid connected or islanded mode of operation. Each microsource can be operated like a current source with maximum power transferred to the grid for the former case. The islanded mode of operation with more balancing requirements of supplydemand would be triggered when the main grid is not comparatively larger or is simply disconnected due to the occurrence of a fault. Without a strong grid and a firm system voltage, each microsource must now regulate its own terminal voltage within an allowed range, determined by its internally generated reference. The microsource thus appears as a controlled voltage source, whose output should rightfully share the load demand with the other sources. The sharing should preferably be in proportion to their power ratings, so as not to overstress any individual entity [9].
5
The installation of distributed generators involves technical studies of two major fields.
First one is the dealing with the influences induced by distributed generators without making large modifications to the control strategy of conventional distribution system and the other one is generating a new concept for utilization of distributed generators. The concept of the microgrid follows the later approach. There includes several advantages with the installation of microgrid. Efficiently microgrid can integrate distributed energy resources with loads.
Microgrid considered as a ‘grid friendly entity” and does not give undesirable influence to the connecting distribution network i.e. operation policy of distribution grid does not have to be modified. It can also operate independently in the occurrence of any fault. In case of large disturbances there is possibility of imbalance of supply and demand as microgrid does not have large central generator. Also microgrid involves different DERs. Even if energy balance is being maintained there continues undesirable oscillation [10].
For each component of the microgrid, a peertopeer and plugandplay model is used to improve the reliability of the system. The concept of peertopeer guarantees that with loss of any component or generator, microgrid can continue its operation. Plugandplay feature implies that without reengineering the controls a unit can be placed at any point on the electrical system thereby helps to reduce the possibilities of engineering errors [11].
The economy of a country mainly depends upon its electric energy supply which should be secure and with high quality. The necessity of customer’s for power quality and energy supply is fulfilled by distributed energy supply. The distribution system mainly includes renewable energy resources, storage systems small size power generating systems and these are normally installed close to the customer’s premises. The benefits of the DERs include power quality with better supply, higher reliability and high efficiency of energy by utilization of waste heat. It is an attractive option from the environmental considerations as there is generation of little pollution. Also it helps the electric utility by reducing congestion on the grid, reducing need for new generation and transmission and services like voltage support and demand response. Microgrid is an integrated system. The integration of the
DERs connected to microgrid is critical. Also there is additional problem regarding the control and grouping and control of DERs in an efficient and reliable manner [12].
Integration of wind turbines and photovoltaic systems with grid leads to grid instability.
One of the solutions to this problem can be achieved by the implementation of microgrid.
Even though there are several advantages associated with microgrid operation, there are high transmission line losses. In a microgrid there are several units which can be utilized in a
6
house or country. In a house renewable energy resources and storage devices are connected to
DC bus with different converter topology from which DC loads can get power supply.
Inverters are implemented for power transfer between AC and DC buses. Common and sensitive loads are connected to AC bus having different coupling points. During fault in the utility grid microgrid operates in islanded mode. If in any case renewable source can’t supply enough power and state of charge of storage devices are low microgrid disconnects common loads and supply power to the sensitive loads [13].
Renewable energy resources are integrated with microgrid to reduce the emission of CO
2 and consumption of fuel. The renewable resources are very fluctuant in nature, and also the production and consumption of these sources are very difficult. Therefore new renewable energy generators should be designed having more flexibility and controllability [14].
In conventional AC power systems AC voltage source is converted into DC power using an AC/DC inverter to supply DC loads. AC/DC/AC converters are also used in industrial drives to control motor speed. Because of the environmental issues associated with conventional power plant renewable resources are connected as distributed generators or ac microgrids. Also more and more DC loads like light emitting diode lights and electric vehicles are connected to AC power systems to save energy and reduce carbon dioxide
(
CO )emission. Long distance high voltage transmission is no longer necessary when power can be supplied by local renewable power sources. AC sources in a DC grid have to be converted into DC and AC loads connected into DC grid using DC/AC inverters [15].
DC systems use power electronic based converters to convert AC sources to DC and distribute the power using DC lines. DC distribution becomes attractive for an industrial park with heavy motor controlled loads and sensitive electronic loads. The fast response capability of these power electronic converters help in providing highly reliable power supply and also facilitate effective filtering against disturbances. The employment of power electronic based converters help to suppress two main challenges associated with DC systems as reliable conversion from AC/DC/AC and interruption of DC current under normal as well as fault condition [16]. Over a conventional AC grid system, DC grid has the advantage that power supply connected with the DC grid can be operated cooperatively because DC load voltage are controlled. The DC grid system operates in standalone mode in the case of the abnormal or fault situations of AC utility line, in which the generated power is supplied to the loads connected with the DC grid. Changes in the generated power and the load consumed power
7
can be compensated as a lump of power in the DC gird. The system cost and loss reduce because of the requirement of only one AC grid connected inverter [17].
Therefore the efficiency is reduced due to multistage conversions in an AC or a DC grid.
So to reduce the process of multiple DC/AC/DC or AC/DC/AC conversions in an individual
AC or DC grid, hybrid AC/DC microgrid is proposed, which also helps in reducing the energy loss due to reverse conversion [15].
Mostly renewable power plants are implemented in rural areas which are far away from the main grid network and there is possibility of weak transmission line connection. The microgrid (MG) concept provides an effective solution for such weak systems. The operation can be smoothened by the hybrid generation technologies while minimizing the disturbances due to intermittent nature of energy from PV and wind generation. Also there is possibility of power exchange with the main grid when excess/shortage occurs in the microgrid [18].
Distributed generation is gaining more popularity because of their advantages like environmental friendliness, expandability and availability without making any alternation to the existing transmission and distribution grid. Modern sources depend upon environmental and climatic conditions hence make them uncontrollable. Because of this problem microgrid concept comes into feature which cluster multiple distributed energy resources having different operating principles. In grid tied mode distributed green sources operates like controlled current source with surplus energy channeled by the mains to other distant loads.
There is need of continuous tuning of source outputs which can be achieved with or without external communication links. In case of any malfunctions grid tied mode is proved less reliable as this leads to instability [19].
1.3. Motivation of project work
The microgrid concept acts as a solution to the conundrum of integrating large amounts of micro generation without disrupting the operation of the utility network. With intelligent coordination of loads and microgeneration, the distribution network subsystem (or
'microgrid') would be less troublesome to the utility network, than conventional microgeneration. The net microgrid could even provide ancillary services such as local voltage control. In case of disturbances on the main network, microgrids could potentially disconnect and continue to operate separately. This operation improves power quality to the customer. From the grid’s perception, the benefit of a microgrid is that it can be considered as a controlled entity within the power system that can be
8
functioned as a single aggregated load. Customers can get benefits from a microgrid because it is designed and operated to meet their local needs for heat and power as well as provide uninterruptible power, enhance local reliability, reduce feeder losses, and support local voltages/correct voltage sag. In addition to generating technologies, microgrid also includes storage, load control and heat recovery equipment. The ability of the microgrid to operate when connected to the grid as well as smooth transition to and from the island mode is another important function.
1.4. Objective of the thesis
The main objective of this thesis is the development of a hybrid microgrid which will reduce the process of multiple reverse conversions associated with individual AC and DC grid by the combination of
AC and DC subgrid
Photovoltaic (PV) system and
Wind turbine generator
In order to analyze the operation of microgrid system both the modeling and controlling of the system are important issues. Hence the control and modeling (to be discussed detail in Chapter 4) are also the part of this thesis work. As a part of the thesis work the overall system is simulated using MATLAB environment. In simulation work the system is modeled using different state equations.
1.5. Thesis organization
The thesis has been organized into six chapters. Following the chapter on introduction, the rest of the thesis is outlind as follows.
Chapter 2 explains detailed modeling of PV array with the implantation of maximum power point tracking. Also the battery model is studied.
Chapter 3 represents explains the modeling of the overall DFIG system in detail. In this chapter the detail explanation is made using block diagrams and different algebraic equations.
In chapter 4 the overall configuration of the hybrid microgrid system was implemented.
Along with the operation of the grid and modeling and control of the used converters are described.
9
Chapter 5 presents all the simulation results which are found using MATLAB/
SIMULINK environment.
Chapter 6 provides comprehensive summary and conclusions of the work undertaken in this thesis and also acknowledge about the future work. The references taken for the purpose of research work are also the part of this chapter.
10
2.1. Photovoltaic system
The photoelectric effect was first noted by French physicist Edmund Becquerel in 1839.
He proposed that certain materials have property of producing small amounts of electric in explained the nature of light and the photoelectric effect which has become the basic principle for photovoltaic technology. In
1954 the first photovoltaic module was built by Bell Laboratories.
s to convert solar energy into electricity. It consists of various components which include the photovoltaic modules, mechanical and electrical connections and mountings and means of regulating and/or modifying the electrical output.
2.1.1.1. Photovoltaic cell
Fig 2.1. Basic structure of PV cell
11
The basic ingredients of PV cells are semiconductor materials, such as silicon. For solar cells, a thin semiconductor wafer creates an electric field, on one side positive and negative on the other. When light energy hits the solar cell, electrons are knocked loose from the atoms in the semiconductor material. When electrical conductors are connected to the positive and negative sides an electrical circuit is formed and electrons are captured in the form of an electric current that is, electricity. This electricity is used to power a load. A
PV cell can either be circular or square in construction.
2.1.1.2. Photovoltaic module
Because of the low voltage generation in a PV cell (around 0.5V), several PV cells are connected in series (for high voltage) and in parallel (for high current) to form a PV module for desired output. In case of partial or total shading, and at night there may be requirement of separate diodes to avoid reverse currents The pn junctions of monocrystalline silicon cells may have adequate reverse current characteristics and these are not necessary. There is wastage of power because of reverse currents which directs to overheating of shaded cells. At higher temperatures solar cells provide less efficiency and installers aim to offer good ventilation behind solar panel. Usually there are of 36 or 72 cells in general PV modules. The modules consist of transparent front side, encapsulated PV cell and back side. The front side is usually made up of lowiron and tempered glass material. The efficiency of a PV module is less than a PV cell. This is because of some radiation is reflected by the glass cover and frame shadowing etc.
2.1.1.3. Photovoltaic array
A photovoltaic array (PV system) is an interconnection of modules which in turn is made up of many PV cells in series or parallel. The power produced by single module is not enough to meet the requirements of commercial applications, so modules are connected to form array to supply the load. In an array the connection of the modules is same as that of cells in a module. The modules in a PV array are usually first connected in series to obtain the desired voltages; the individual modules are then connected in parallel to allow the system to produce more current. In urban uses, generally the arrays are mounted on a rooftop. PV array output can directly feed to a DC motor in agricultural applications.
12
Fig 2.2. Photovoltaic system
2.1.2. Working of PV cell
The basic principle behind the operation of a PV cell is photoelectric effect. In this effect electron gets ejected from the conduction band as a result of the absorption of sunlight of a certain wavelength by the matter (metallic or nonmetallic solids, liquids or gases). So, in a photovoltaic cell, when sunlight hits its surface, some portion of the solar energy is absorbed in the semiconductor material.
Fig 2.3. Working of PV cell
The electron from valence band jumps to the conduction band when absorbed energy is greater than the band gap energy of the semiconductor. By these holeelectrons pairs are created in the illuminated region of the semiconductor. The electrons created in the conduction band are now free to move. These free electrons are enforced to move in a particular direction by the action of electric field present in the PV cells. These electrons
13
flowing comprise current and can be drawn for external use by connecting a metal plate on top and bottom of PV cell. This current and the voltage produces required power.
2.1.3. Modeling of PV panel
The photovoltaic system can generate direct current electricity without environmental impact when is exposed to sunlight. The basic building block of PV arrays is the solar cell, which is basically a pn junction that directly converts light energy into electricity. The output characteristic of PV module depends on the cell temperature, solar irradiation, and output voltage of the module. The figure shows the equivalent circuit of a PV array with a load [20].
R s
I pv
I ph
V pv
R p
Fig 2.4. Equivalent circuit of a solar cell
Usually the equivalent circuit of a general PV model consists of a photocurrent, a diode, a parallel resistor which expresses a leakage current, and a series resistor which describes an internal resistance to the current flow. The voltage current characteristic equation of a solar cell is given as
I = I − I [exp (q(V + I R )/kT A) − 1] − (V + I R )/R (2.1)
The photocurrent mainly depends on the cell’s working temperature and solar irradiation, which is explained as
I = [I + K (T − T
!"
)]λ/1000 (2.2)
The saturation current of the cell varies with the cell temperature, which is represented as
I = I (T /T
!"
)
% exp [qE
'
(1/T
!"
− 1/T )/kA] (2.3)
The shunt resistance
R of the cell is inversely related with shunt leakage current to the ground. Usually efficiency of PV array is insensitive to variation in
R and the shuntleakage resistance can be assumed to approach infinity without leakage current to ground.
14
Alternatively a small variation in series resistance
R will significantly affect output power of the PV cell. The appropriate model of PV solar cell with suitable complexity is shown in
Fig.2.4. Equation (2.1) can be modified to be
I = I − I [exp (q(V + I R )/kT A) − 1] (2.4)
There is no series loss and no leakage to ground for an ideal PV cell, i.e.,
R = 0
and
R
P
=
∞.
So equation (2.1) can be rewritten as
I = I − I [exp (qV /kT A) − 1] (2.5)
A PV array is a group of several PV modules which are electrically connected in series and parallel circuits to generate the required current and voltage. So the current and voltage equation of the array with
N parallel and N series cells can be represented as
I = N I − N I [exp (q(V /N + I /N )/kT A) − 1] − (N V /N + I )/R (2.6)
The efficiency of a PV cell is sensitive to small change in series resistance but insensitive to variation in shunt resistance. The role of series resistance is very important for a PV module and the shunt resistance is approached to be infinity which can also be assumed as open. The mathematical equation of the model can be described by considering series and parallel resistance as
I = N I − N I [exp (q(V /N + I R /N )/kT A) − 1] (2.7)
The equation (2.7) can be simplified as
I = N I − N I [exp (qV /N kT A) − 1] (2.8)
The opencircuit voltage
V
+
and shortcircuit current
I are the two most important parameters used which describes the cell electrical performance. The above mentioned equations are implicit and nonlinear; hence, it is not easy to arrive at an analytical solution for the specific temperature and irradiance. Normally
I ≫ I , so by neglecting the small diode and groundleakage currents under zeroterminal voltage, the shortcircuit current is approximately equal to the photocurrent, i.e.
I = I (2.9)
The opencircuit voltage parameter is obtained by assuming the zero output current. With the given opencircuit voltage at reference temperature and ignoring the shuntleakage current, the reverse saturation current can be acquired as
15
I = I /[exp (qV
+
/N kAT ) − 1] (2.10)
Additionally, the maximum power can be stated as
P
./
= V
./
I
./
= γV
+
I (2.11)
The parameters used for the modeling of photovoltaic panel are shown in the table 2.1 [16].
Symbol Value
V
+ q k
A
403 V
1.602 × 10
1.38 × 10
1.50
567
5 %
C
K
I
K
T
!"
I
T
λ
3.27 A
1.7 × 10
5%
301.18 K
2.0793 × 10
350 K
5<
A
01500
W/m
N
40
N
?
900
E
'
1.1 eV
Table 2.1. Parameters for photovoltaic panel
16
2.2. Maximum power point tracking
As an electronic system maximum power point tracker (MPPT) functions the photovoltaic (PV) modules in a way that allows the PV modules to produce all the power they are capable of. It is not a mechanical tracking system which moves physically the modules to make them point more directly at the sun. Since MPPT is a fully electronic system, it varies the module’s operating point so that the modules will be able to deliver maximum available power. As the outputs of PV system are dependent on the temperature, irradiation, and the load characteristic MPPT cannot deliver the output voltage perfectly. For this reason MPPT is required to be implementing in the PV system to maximize the PV array output voltage.
2.2.1. Necessity of maximum power point tracking
Fig 2.5. MPP characteristic
In the power versus voltage curve of a PV module there exists a single maxima of power, i.e. there exists a peak power corresponding to a particular voltage and current. The efficiency of the solar PV module is low about 13%. Since the module efficiency is low it is desirable to operate the module at the peak power point so that the maximum power can be delivered to the load under varying temperature and irradiation conditions. This maximized power helps to improve the use of the solar PV module. A maximum power point tracker (MPPT) extracts maximum power from the PV module and transfers that power to the load. As an interfacing device DC/DC converter transfers this maximum
17
power from the solar PV module to the load. By changing the duty cycle, the load impedance is varied and matched at the point of the peak power with the source so as to transfer the maximum power.
2.2.2. Algorithms for tracking of maximum power point
There are different algorithms which help to track the peak power point of the solar PV module automatically. The algorithms can be written as a. Perturb and observe b. Incremental conductance c. Parasitic capacitance d. Voltage based peak power tracking e. Current Based peak power tracking
2.2.2.1. Perturb and observe
In this algorithm a slight perturbation is introduced in the system. The power of the module changes due to this perturbation. If the power increases due to the perturbation then the perturbation is continued in that direction. When power attains its peak point, the next instant power decreases and so also the perturbation reverses. During the steady state condition the algorithm oscillates around the peak point. The perturbation size is kept very small to keep the power variation small. It is examined that there is some power loss because of this perturbation and also it fails to track the power under fast varying atmospheric conditions. But still this algorithm is very popular and simple [22], [23].
200
150
100
50
0
0 2 4 6 8 10
Voltage(V)
12 14
Fig 2.6. Perturb and observe algorithm
16
18
In the present work this algorithm is chosen. Figure 2.7 represents the flow chart of the algorithm. The algorithm observes output power of the array and perturbs the power based on increment of the array voltage. The algorithm continuously increments or decrements the reference voltage based on the value of the previous power sample.
∆
P
∆
V
=
=
P (
V k
(
) k )
−
−
P ( k
V ( k
−
1
−
)
1 )
∆
P
>
0
∆
V
>
0
∆
V
<
0
V ref
=
V ref
+ ∆
V V ref
=
V ref
− ∆
V
V ref
=
V ref
− ∆
V
V ref
=
V ref
+ ∆
V k
= k
+
1
Fig 2.7. Flowchart Perturb and observe algorithm
Here a reference voltage
V
@!"
is set corresponding to the peak power point of the module.
The value of current and voltage can be obtained from the solar PV module. From the measured voltage and current power is calculated. The value of voltage and power at k
AB instant are stored. Then values at
(k + 1)
AB
instant are measured again and power is calculated from the measured values. The power and voltage at
(k + 1)
AB
instant are subtracted with the values from
k
AB
instant. If we observe the power voltage curve of the solar PV module we see that in the right hand side curve where the voltage is almost constant
19
the slope of power voltage is negative
CdP dV positive
CdP dV
V(k)] after subtraction the algorithm decides whether to increase or to reduce the reference voltage.
The P&O method is claimed to have slow dynamic response and high steady state error.
In fact, the dynamic response is low when a small increment value and a low sampling rate are employed. To decrease the steady state error low increments are essential because the
P&O always makes the operating point oscillate near the MPP, but never at the MPP exactly.
When the increment is lower, the system will be closer to the array MPP. In case of greater increment, the algorithm will work faster, but the steady state error will be increased. The small increments tend to make the algorithm more stable and accurate when the operating conditions of the PV array change. In case of large increments the algorithm becomes confused since the response of the converter to large voltage or current variations will cause oscillations, overshoot and the settling time of the converter itself confuse the algorithm [24].
2.2.2.2. Incremental conductance
The incremental conductance method can overwhelm the problems of tracking peak power under fast varying atmospheric condition [22], [23].
200
JK
E
JL
150
JK
E
JL
E
100
JK
E
JL
E
50
0
0 2 4 6 8 10
Voltage(V)
12 14 16
Fig 2.8. Incremental conductance algorithm
The algorithm uses the equation
P = V ∗ I (2.12)
(Where P=power of the module, V= voltage of the module, I= current of the module);
20
Differentiating with respect to
dV dP dV
E (2.13)
The algorithm works depending on this equation.
At peak power point dP dV dI dV
E (2.15)
If the operating point is to the right of the power curve then we have dP dV dI dV
E (2.17)
If operating point is to the left of the power curve then we have dP dV dI dV
E (2.19)
The algorithm works using equations (2.15), (2.17), & (2.18).
When the incremental conductance decides that the MPPT has reached the MPP, it stops perturbing the operating point. If this condition is not achieved, MPPT operating point it is to the left of the MPP. This algorithm has benefits over perturb and observe in that it can determine when the MPPT has reached the MPP, where perturb and observe oscillates around the MPP. Also, this algorithm can track rapidly increasing and decreasing irradiance conditions with higher accuracy than perturb and observe. The drawback of this algorithm is that there is increased complexity when compared to perturb and observe.
2.2.2.3. Parasitic capacitances
The improvement of the incremental conductance leads to the method of parasitic capacitance which considers the parasitic capacitances of the solar cells. This method makes use of the switching ripple of the MPPT which helps to perturb the array. The average ripple in the PV array voltage and power, generated by the switching frequency are measured
21
using a series of filters and multipliers and then used to calculate the array conductance. Then the algorithm decides the direction of movement of MPPT operating point. There is one disadvantage in this algorithm that the parasitic capacitance in each module is very small, and can perform well in large PV arrays where several PV modules are connected in parallel. There is sizable input capacitor in the DCDC converter which filters out small ripple in the array power. This capacitor may cover the overall effects of the parasitic capacitance of the PV array [23].
2.2.2.4. Voltage control maximum power point tracker
The maximum power point (MPP) of a PV module is assumed to lie about 0.75 times the open circuit voltage of the module. Hence a reference voltage can be generated by calculating the open circuit voltage and then the feed forward voltage control scheme can be implemented to bring the solar PV module voltage to the point of maximum power. The difficulty associated with this technique is that there is variation of open circuit voltage with the temperature. As there is increase in temperature because of the change in open circuit voltage of the module, module’s open circuit is needed to be calculated frequently. In this process the load must be disconnected from the module to measure open circuit voltage. So the power during that instant cannot be utilized [25].
2.2.2.5. Current control maximum power point tracker
The module’s peak power lies at the point which is about 0.9 times the short circuit current of the module. The module has to be shortcircuited to measure this point. After that module current is adjusted to the value by using the current mode control which is approximately 0.9 times the short circuit current. In this case a high power resistor is required which can sustain the shortcircuit current. This is the problem with this algorithm. The module has to be short circuited to measure the short circuit current as it goes on varying with the changes in irradiation level [25].
2.3. Battery
In our modern society the role of batteries is important as energy carriers, because of its presence in devices for everyday use. At the end of the 20 th
century the demand for batteries rapidly increased due to the large interest in wireless devices. Today, the battery industry comes under the category of largescale industry which produces several million batteries per month. Improving the energy capacity is one major development issue, however, for consumer products, safety is probably considered equally important today. With the
22
introduction of hybrid electric vehicles into the market there is technological development in the battery field which leads to reduction of fuel consumption and gas emissions. Battery development is a major task for both industry and academic research.
2.3.1. Modeling of battery
The battery is modeled as a nonlinear voltage source whose output voltage depends not only on the current but also on the battery state of charge (SOC), which is a nonlinear function of the current and time [26]. Fig 2.9
represents a basic model of battery.
Two parameters to represent state of a battery i.e. terminal voltage and state of charge can be written as:
V
M
= V
N
+ R
M i
M
− K
P
P5QR
S
TA
+ A ∗ exp (B Q i
M dt) (2.20)
SOC = 100(1 +
Q R
S
P
TA
) (2.21)
I batt
V b
V batt
V b
=
V
0
−
k
Q
−
Q
∫
i b dt
.
it
+
A
.
exp(
−
B
.
it
)
it t
∫
0
Fig 2.9. Model of battery
The original Shepherd model has a nonlinear term equal to
K
P
P5QR
S
TA
. This term represents a nonlinear voltage that changes with the amplitude of the current and the actual charge of the battery. So when there is complete discharge of battery and no flow of current, the voltage of the battery will be nearly zero. As soon as a current circulates again, the voltage falls abruptly. This model yields accurate results and also represents the behaviour of the battery.
23
2.4. Summary
This chapter summarizes the modeling of solar panel with the implementation of maximum power point tracking algorithm. Various MPPT algorithms are introduced for the study of PV array to track maximum power under various solar irradiation and temperature conditions. Also the model of battery is explained in detail for the modeling of microgrid.
24
CHAPTER 3
DOUBLY FED INDUCTION GENERATOR
3.1. Wind turbines
With the use of power of the wind, wind turbines produce electricity to drive an electrical generator. Usually wind passes over the blades, generating lift and exerting a turning force.
Inside the nacelle the rotating blades turn a shaft then goes into a gearbox. The gearbox helps in increasing the rotational speed for the operation of the generator and utilizes magnetic fields to convert the rotational energy into electrical energy. Then the output electrical power goes to a transformer, which converts the electricity to the appropriate voltage for the power collection system. A wind turbine extracts kinetic energy from the swept area of the blades.
The power contained in the wind is given by the kinetic energy of the flowing air mass per unit time [28]. The equation for the power contained in the wind can then be written as
P
=
6
(air mass per unit time)(V
∞
)
=
6
(ρAV
^
)(V
^
)
=
6
ρAV
^
%
(3.1)
Although Eq. (3.1) describes the availability of power in the wind, power transferred to the wind turbine rotor is reduced by the power coefficient
C .
C = _`ab cdeS`af
(3.2) g`e
A maximum value of
C is defined by the Betz limit, which states that a turbine can never extract more than 59.3% of the power from an air stream. In reality, wind turbine rotors have maximum
C values in the range 2545%.
P hRiT [email protected]!
= C × P
(3.3)
It is also conventional to define a tip speed ratio λ as
λ
= k l
∞
(3.4)
25
3.2. DFIG system
The doubly fed induction machine is the most widely machine in these days. The induction machine can be used as a generator or motor. Though demand in the direction of motor is less because of its mechanical wear at the slip rings but they have gained their prominence for generator application in wind and water power plant because of its obvious adoptability capacity and nature of tractability. This section describes the detail analysis of overall DFIG system along with back to back PWM voltage source converters.
3.2.1. Mathematical modeling of induction generator
DFIG is a wound rotor type induction machine, its stator consists of stator frame, stator core, poly phase (3phase) distributed winding, two end covers, bearing etc. The stator core is stack of cylindrical steel laminations which are slotted along their inner periphery for housing the 3phase winding. Its rotor consists of slots in the outer periphery to house the windings like stator. The machine works on the principle of Electromagnetic Induction and the energy transfer takes place by means of transfer action. So the machine can represent as a transformer which is rotatory in action not stationary. This section explains the basic mathematical modeling of DFIG. In this section the machine modeling is explained by taking two phase parameters into consideration.
3.2.1.1. Modeling of DFIG in synchronously rotating frame
Fig 3.1 and 3.2 demonstrates the equivalent circuit diagram of an induction machine. The machine is signified as a two phase machine in this figure.
Fig 3.1. Dynamic dq equivalent circuit of DFIG (qaxis circuit)
26
Fig 3.2. Dynamic dq equivalent circuit of DFIG (daxis circuit)
Equations for the stator circuit can be written as v v
= R
= R
?
?
i i
+
T
TA
λ
+
T
TA
λ
(3.5)
(3.6)
In dq frame Eq. (3.5) and (3.6) can be written [29] as v n?
= R
?
i n?
+
T
TA
λ n?
+ (ω
!
λ
T?
) (3.7) v
T?
= R
?
i
T?
+
T
TA
λ
T?
− (ω
!
λ n?
) (3.8)
Where all the variables are in synchronously rotating frame. The bracketed terms indicate the back emf or speed emf or counter emf due to the rotation of axes as in the case of DC machines. When the angular speed
ω
!
is zero, the speed e.m.f due to d and q axis is zero and the equations changes to stationary form. If the rotor is blocked or not moving, i.e.
ω
@
= 0, the machine equations can be written as v [email protected]
= R
+
T
TA
+ (ω
!
λ
) (3.9) v
= R
@ i
+
T
TA
λ
− (ω
!
) (3.10)
Let the rotor rotates at an angular speed
ω
@
, then the dq axes fixed on the rotor fictitiously will move at a relative speed
(ω
!
− ω
@
) to the synchronously rotating frame.
By replacing
(ω
!
− ω
@
) in place of ω
!
the dq frame rotor equations can be written as v [email protected]
= R
+
T
TA
+ (ω
!
−ω
@
) λ
(3.11)
27
v
= R
@ i
+
T
TA
λ
− (ω
!
−ω
@
(3.12)
The flux linkage expressions in terms of current can be written from Fig 3.1 and 3.2 as follows:
λ n?
= L
6?
i n?
+ L

(i n?
)
= L
i n?
+ L
(3.13)
λ
T?
= L
6?
i
T?
+ L

(i
T?
+ i
)
= L
i
T?
+ L
i
(3.14)
= L
[email protected] i [email protected]
+ L

(i n?
)
= L
+ L
i n?
(3.15)
λ
= L
+ L

(i
T?
+ i
)
= L
@ i
+ L
i
T?
(3.16)
λ n
= L

(i n?
) (3.17)
λ
T
= L

(i
T?
+ i
) (3.18)
Eq. (3.5) to (3.18) describes the complete electrical modeling of DFIG. Whereas Eq. (3.19) express the relations of mechanical parameters which are essential part of the modeling.
The electrical speed
ω
@
cannot be treated as constant in the above equations. It can be connected to the torque as
T
!
= T q
+ J
Tk
TA s + Bω

= T q
+ J
Tk
TA e + Bω
@
(3.19)
3.2.1.2. Dynamic modeling of DFIG in state space equations
The dynamic modeling in state space form is necessary to carried out simulation using different tools such as MATLAB. The basic sate space form helps to analyze the system in transient condition.
In the DFIG system the state variables are normally currents, fluxes etc. In the following section the state space equations for the DFIG in synchronously rotating frame has been derived with flux linkages as the state variables. As the machine and power system parameters are nearly always given in ohms or percent or per unit of base impedance, it is appropriate to
28
express the voltage and flux linkage equations in terms of reactances rather than inductances.
The above stated voltage and flux equations can be reworked as follows: v n?
= R
?
i n?
+
6
ω
S
T
TA
ψ n?
+ ω f
ω
S
ψ n?
(3.20) v
T?
= R
?
i
T?
+
6
ω
S
T
TA
ψ
T?
−
ω f
ω
S
ψ
T?
(3.21) v [email protected]
= R
+
6
ω S
T
TA
+
(ω f
5ω e
)
ω S
ψ
(3.22) v
= R
@ i
+
6
ω
S
T
TA
ψ
−
(ω f
5ω e
)
ω
S
(3.23)
Equations related to flux linkage i.e. Eq. (3.13)(3.18) can be written in terms of reactances as follows:
ψ n?
= X v?
i n?
+ X

(i n?
) (3.24)
ψ
T?
= X v?
i
T?
+ X

(i
T?
+ i
) (3.25)
= X [email protected] i [email protected]
+ X

(i n?
) (3.26)
ψ
= X [email protected] i
+ X

(i
T?
+ i
) (3.27)
ψ n
= X

(i n?
) (3.28)
ψ
T
= X

(i
T?
+ i
) (3.29)
Where reactances (
X v?
, X [email protected],
X

) are found by multiplying base frequency ω
M
with inductances
(
L
6?
, L
, L

).
From eq. (3.24)(3.29) we can find the expressions for currents in terms of flux linkages and also the mutual flux linkages (
ψ n
, ψ
T
) are found using current expressions.
The equations are given as follows [29]. i n?
= x yz
5x ys
{
z
(3.30) i [email protected]
= x ye
5x ys
{
e
(3.31) i
T?
= x bz
5x bs
{
z
(3.32) i
= x be
{
5x bs
e
(3.33)
29
Substituting equations (3.30)(3.33) in (3.28)(3.29) we get
ψ n
= X

{
ψ yz
5ψ ys
{
z
+
ψ ye
5ψ
{
e ys
}
⟹ ψ n
=
{ s
{
z
ψ n?
−
{ s
{
z
ψ n
+
{ s
{
e
−
{ s
{
e
ψ n
⟹ ψ n
+
{ s
{
z
ψ n
+
{ s
{
e
ψ n
=
{ s
{
z
ψ n?
+
{ s
{
z
⟹ ψ n
C1 +
{ s
{
z
+
{ s
{
e
G =
{ s
{
z
ψ n?
+
{ s
{
e
⟹ ψ n
C
{
z
{ e
€{ s
{
z
{
e
{
e
€{ s
{
z G =
{
{ s
z
ψ n?
+
{
{ s
e
⟹ ψ n
X

C
{
z
{ e
€{
{ s
z
{
{
e
e
{
€{ s s
{
z G =
{ s
{
z
ψ n?
+
{ s
{
e
⟹ ψ n
{
{ s fy
=
{ s
{
z
ψ n?
+
{ s
{
e
⟹ ψ n
=
{ fy
{
z
ψ n?
+
{ fy
{
e
ψ n
=
{ fy
{
z
ψ n?
+
{ fy
{
e
(3.34)
Similarly we can find the value of ψ
T
as follows
ψ
T
=
{ fy
{
z
ψ
T?
+
{ fy
{
e
ψ
(3.35)
Substituting the current equations from (3.30)(3.33) in voltage equations (3.20)(3.23) we will get, v n?
=
{ z
z
Cψ n?
− ψ n
G +
6
ω S
Tψ yz
TA
+
ω f
ω S
ψ
T?
(3.36) v
T?
= z
{
z
(ψ
T?
− ψ
T
) +
6 k
S
Tx bz
TA
− k f k
S
ψ n?
(3.37) v [email protected]
= z
{
e
− ψ n
G +
6
ω
S
Tψ ye
TA
+
(ω f
5ω e)
ω
S
ψ
(3.38) v
=
{ z
e
•ψ
− ψ
T
‚ +
6
ω
S
Tψ be
TA
−
(ω f
5ω e
)
ω
S
(3.39)
The state variables can be expressed using the above equations as follows
Tψ yz
TA
= ω
M
ƒv n?
− ω f
ω
S
ψ
T?
− z
{
z
Cψ n?
− ψ n
G„ (3.40)
30
Tψ bz
TA
= ω
M
ƒv n?
+ ω f
ω
S
ψ n?
− z
{
z
•ψ
T?
− ψ
T
‚„ (3.41)
Tψ ye
TA
= ω
M
−
(ω f
5ω e
)
ω
S
ψ
− e
{
e
− ψ n
G„ (3.42)
Tψ be
TA
= ω
M
ƒv
+
(ω f
5ω e
)
ω
S
− e
{
e
•ψ
− ψ
T
‚„ (3.43)
The state space matrix can be written as follows
ψ
•
ψ
ψ
ψ
• qs
• ds
• qr dr
=
−
−
R
X
ω e
ω b
1 s
0
0 s
−
−
ω e
ω b
R s
X
1 s
0
0
0
0
−
R
ω e
X
−
ω b r
1 r
ω r
−
0
0
ω e
−
ω b
ω r
−
R r
X
1 r
ψ
ψ
ψ
ψ qs ds qr dr
+
1
0
0
0
0
0
0
1
1
0
0
0
0
0
0
1
v
v
v
v qs ds qr dr
+
R
X
1 s
0
R
X
1 r
0 s r
R
0
X
1 s
R
0
X
1 r s r
ψ
ψ qm dm
(3.44)
The electromotive torque is developed by the interaction of airgap flux and the rotor mmf. At synchronous speed the rotor cannot move and as a result there is no question of induced emf as well as the current, hence there is zero torque, but at any speed other than synchronous speed the machine will experience torque which is the case of motor, where as in case of generator electrical torque in terms of mechanical is provided by means of prime mover which is wind in this case.
The torque can be represented in terms of flux linkages and currents as
T
!
=
%
•λ
T?
i n?
− λ n?
i
T?
‚
=
%
L

•i n?
i
− i
T?
‚
=
%
•λ
[email protected] i [email protected]
− λ [email protected] i
‚ (3.45)
Equation (3.45) can be written in terms of state variables as follows
T
!
=
% 6 k
S
•ψ
T?
i n?
− ψ n?
i
T?
‚
=
% 6 k
S
•ψ
[email protected] i [email protected]
− ψ [email protected] i
‚ (3.46)
Equation (3.40)(3.46) describe the complete DFIG model in state space form, where
ψ n?
,
ψ
T?
, ψ
are the state variables.
31
Symbol
P i…
V i…
R
‡
L
‡
R
ˆ
L
ˆ
L
‰
J
Value
50 kW
400 V
0.00706 pu
0.171 pu
0.005 pu
0.156 pu
2.9 pu
3.1 s n
6
V
TŠ_i…
P

800 V
45 kW
Table 3.1. Parameters for DFIG
The parameters used for the modeling of induction generator are shown in the table 3.1 [16].
3.3. Summary
This chapter explains the basic introduction of wind turbine. Also the detailed modeling of the doubly fed induction generator (DFIG) system is analyzed which plays a vital role in the modeling and control structure of hybrid microgrid.
32
CHAPTER 4
AC/DC MICROGRID
The concept of microgrid is considered as a collection of loads and microsources which functions as a single controllable system that provides both power and heat to its local area.
This idea offers a new paradigm for the definition of the distributed generation operation. To the utility the microgrid can be thought of as a controlled cell of the power system. For example this cell could be measured as a single dispatch able load, which can reply in seconds to meet the requirements of the transmission system. To the customer the microgrid can be planned to meet their special requirements; such as, enhancement of local reliability, reduction of feeder losses, local voltages support, increased efficiency through use waste heat, voltage sag correction [3]. The main purpose of this concept is to accelerate the recognition of the advantage offered by small scale distributed generators like ability to supply waste heat during the time of need [4]. The microgrid or distribution network subsystem will create less trouble to the utility network than the conventional microgeneration if there is proper and intelligent coordination of micro generation and loads
[5]. Microgrid considered as a ‘grid friendly entity” and does not give undesirable influences to the connecting distribution network i.e. operation policy of distribution grid does not have to be modified [7].
4.1.Configuration of the hybrid microgrid
Fig 4.1. A hybrid AC/DC microgrid system
33
The configuration of the hybrid system is shown in Figure 1 where various AC and DC sources and loads are connected to the corresponding AC and DC networks. The AC and DC links are linked together through two transformers and two four quadrant operating threephase converters. The AC bus of the hybrid grid is tied to the utility grid.
Figure 4.2 describes the hybrid system configuration which consists of AC and DC grid.
The AC and DC grids have their corresponding sources, loads and energy storage elements, and are interconnected by a three phase converter. The AC bus is connected to the utility grid through a transformer and circuit breaker.
Fig 4.2. Representation of hybrid microgrid
In the proposed system, PV arrays are connected to the DC bus through boost converter to simulate DC sources. A DFIG wind generation system is connected to AC bus to simulate
AC sources. A battery with bidirectional DC/DC converter is connected to DC bus as energy storage. A variable DC and AC load are connected to their DC and AC buses to simulate various loads.
PV modules are connected in series and parallel. As solar radiation level and ambient temperature changes the output power of the solar panel alters. A capacitor
C is added to the PV terminal in order to suppress high frequency ripples of the PV output voltage. The bidirectional DC/DC converter is designed to maintain the stable DC bus voltage through charging or discharging the battery when the system operates in the autonomous operation mode. The three converters (boost converter, main converter, and bidirectional converter) share a common DC bus. A wind generation system consists of doubly fed induction
34
generator (DFIG) with back to back AC/DC/AC PWM converter connected between the rotor through slip rings and AC bus. The AC and DC buses are coupled through a three phase transformer and a main bidirectional power flow converter to exchange power between DC and AC sides. The transformer helps to step up the AC voltage of the main converter to utility voltage level and to isolate AC and DC grids.
Symbol Value
C
L
6
C
T
L
R
110
μF
2.5 mH
4700
μF
0.43 mH
0.3 ohm
C
L
%
R
% f
60
μF
3 mH
0.1 ohm
50 Hz f
?
10 kHz
V
T
400 V
V
400 V
Table 4.1. Component parameters for the hybrid grid
The parameters used for the modeling of hybrid grid are show in the table 4.1 [16].
35
4.2.Operation of grid
The hybrid grid performs its operation in two modes.
4.2.1. Grid tied mode
In this mode the main converter is to provide stable DC bus voltage, and required reactive power to exchange power between AC and DC buses. Maximum power can be obtained by controlling the boost converter and wind turbine generators. When output power of DC sources is greater than DC loads the converter acts as inverter and in this situation power flows from DC to AC side. When generation of total power is less than the total load at DC side, the converter injects power from AC to DC side. The converter helps to inject power to the utility grid in case the total power generation is greater than the total load in the hybrid grid,. Otherwise hybrid receives power from the utility grid. The role of battery converter is not important in system operation as power is balanced by utility grid.
4.2.2. Autonomous mode
The battery plays very important role for both power balance and voltage stability. DC bus voltage is maintained stable by battery converter or boost converter. The main converter is controlled to provide stable and high quality AC bus voltage.
4.3.Modeling and control of converters
In the present work five types of converters are used for the proper coordination with utility grid which will be helpful for uninterrupted and high quality power to AC and DC loads under variable solar radiation and wind speed when grid operates in grid tied mode. The control algorithms are described in the following section.
4.3.1.
Modeling and control of boost converter
The main objective of the boost converter is to track the maximum power point of the PV array by regulating the solar panel terminal voltage using the power voltage characteristic curve.
For the boost converter the input output equations can be written as
V − V
’
= L
6
TR

TA
+ R
6 i
6
(4.1)
I − i
V
’
6
= C
Tl
“”
TA
(4.2)
= V
T
(1 − d
6
) (4.3)
36
V
*
pv i
*
1
I pv
1 /(
sL
3
+
R
3
)
i
1
1
/(
sC pv
)
V pv
V
'
d
Fig 4.3. Control block diagram of boost converter
With the implementation of P&O algorithm a reference value i.e.
V
∗
is calculated which mainly depends upon solar irradiation and temperature of PV array [32]. Here for the boost converter dual loop control is proposed [33]. Here the control objective is to provide a high quality DC voltage with good dynamic response. The outer voltage loop helps in tracking of reference voltage with zero steady state error and inner current loop help in improvisation of dynamic response.
4.3.2.
Modeling and control of main converter
The role of the main converter is to exchange power between AC and DC bus. The key purpose of main converter is to maintain a stable DClink voltage in grid tied mode. When the converter operates in grid tied mode, it has to supply a given active and reactive power.
Here PQ control scheme is used for the control of main converter. The PQ control is achieved using a current controlled voltage source. Two PI controllers are used for real and reactive power control. When resource conditions or load capacities change, the DC bus voltage is setteled to constant through PI regulation. The PI controller is set as the instantaneous active current i
T
reference and the instantaneous reactive current i n
reference is determined by reactive power compensation command.
The model of the converter can be represented in ABC coordinate as
L
T
TA
• i i
‘
— + R • i
– i i
– i
‘
— = • v v v
‘
–
— − • v v v
‘
–
— (4.4)
The above equation can be written in the dq coordinate as
L
T
TA
˜ i i
Tn
™ = ˜ −R
ωL
−R ™ ˜ i i
Tn
™ + C v
?T
v
?n
G − C v v
ŠT
Šn
G (4.5)
37
Where
(i
‘
, i
–
, i ) and (v
‘
, v
–
, v ) are three phase current and voltages of the main converter. Three phase voltage of AC bus voltage are represented by the notations as
(v
‘
, v
–
, v ). The variables (i
T
, i n
), •v
ŠT
, v
Šn
‚, •v
?T
, v
?n
‚ are dq coordinates of three phase currents, voltages of main converter and voltage of AC bus respectively.
V d
*
i
*
dm u sd v cd
PI
PI
V d i dm
L
2
ω
L
2
i qm i
*
qm v cq
PI
Fig 4.4. Control block diagram of main converter
In case of sudden DC load drop, there is power surplus at DC side and the main converter is controlled to transfer power from DC to AC side. The active power absorbed by the capacitor
C
T
leads to rising of DClink voltage
V
T
. The negative error caused by the increase of
V
T
produces a higher active current reference i reference i
will force active current reference i
T
through PI control. A higher positive
to increase through the inner current control loop. Therefore the power surplus of the DC grid can be transferred to the AC side.
Also a sudden increase of DC load causes the power shortage and
V
T
drop at the DC grid.
The main converter is controlled to supply power from the AC to DC side. The positive voltage error caused by
V
T
drop makes the magnitude of i
Since
i
T
and i
increase through the PI control. are both negative, the magnitude of
i
T
is increased through the inner current control loop. Hence power is transferred from AC grid to the DC side.
4.3.3. Modeling and control of DFIG
The section 3.2.1 explains the detailed modeling of DFIG. The state space equations are considered for induction machine modeling. The parameters and specifications of the DFIG are given in table 3.1. Flux linkages are used as the state variables in the model. Here two back to back converters are used in the rotor circuit. The main purpose of the machineside
38
converter is to control the active and reactive power by controlling the dq components of rotor current, while the gridside converter controls the dclink voltage and ensures the operation at unity power factor by making the reactive power drawn by the system from the utility grid to zero.
Two back to back converters are connected to the rotor circuit is shown in Fig 4.5. The firing pulses are given to the devices (IGBTs) using PWM techniques. Two converters are linked to each other by means of dclink capacitor.
Fig. 4.5. Overall DFIG system
Basically converter 1 controls the grid parameters whereas the converter 2 serves for machine. The converters use six IGBTs (for three phase bridge type) as the controlled device.
The PWM technique uses the controlled voltage v a
*
, v b
*
, v c
*
.The triangular career waves are being compared with sinusoidal reference waves to ensure pulses for the devices. The modulation indices are different for the both converters which are determined by the equation
(4.6) & (4.7).
V
?
= m
6 l bš
√
√%
(4.6)
V
@
= ±s l z i
= m l bš
√
⟹ s = ± i
•


√%
(4.7)
39
4.3.3.1. Modeling and control of grid side converter
When the voltages in the grid changes due to different unbalance conditions, it makes an effect on the dc link voltage. The relation between stator voltage and the DC link voltage is represented by the equation (4.6) & (4.7).
Fig 4.6. Schematic diagram of grid side converter
Since the machine is grid connected the grid voltage as well as the stator voltage is same, there exists a relation between the grid voltage and DC link voltage. The main objective of the grid side converter is to maintain DC link voltage constant for the necessary action. The voltage oriented vector control method is approached to solve this problem.
The detail mathematical modeling of grid side converter is given below. The control strategies are made following the mathematical modeling and it is shown in Fig. 4.7. The
PWM converter is current regulated with the direct axis current is used to regulate the DC link voltage whereas the quadrature axis current component is used to regulate the reactive power. The reactive power demand is set to zero to ensure the unit power factor operation
[35]. Fig. 4.6 shows the schematic diagram of the grid side converter.
The voltage balance across the line is given by Eq. (4.8), where R and L are the line resistance and reactance respectively. With the use of dq theory the three phase quantities are transferred to the two phase quantities. ž v v
.
v
M
Š
Ÿ = R ž i i
.
i
M
Š
Ÿ + L
T
TA ž i i
.
i
M
Š
Ÿ + ž v v v
.
M
Š
Ÿ (4.8)
For the grid side converter the mathematical modeling can be represented as v
T
= Ri
T
+ L
TR b
TA
− ω
!
Li n
+ v
Tv
(4.9) v n
= Ri n
+ L
TR y
TA
− ω
!
Li
T
+ v nv
(4.10)
40
Where v
T6
and v n6
are the two phase voltages found from v
.
, v
M
, v
Š
using dq theory. Since the DC link voltage needs to be constant and the power factor of the overall system sets to be unity, the reference values are to be set consequently.
The d and q reference voltages are found from the Eq. (4.11) and (4.12). v = v
T
− i R + i ω
!
L − v
T
(4.11) v = v n
− i R + i ω
!
L − v n¢
(4.12)
V
*
dc
V dc i
*
q i
*
d v
'
q v q
1
v
'
d v
*
d v d
1
v
*
q i q i d
I abc
θ
e
i
*
q
L
ω
e i
*
d
R v d v q
θ
e
V abc i
*
q
R i
*
d
L
ω
e
Fig 4.7. Control block diagram of grid side converter
The control scheme utilizes current control loops for i
T
and i n
with the i
T
demand being derived from the dclink voltage error through a standard PI controller. The i n
demand determines the displacement factor on the grid side of the choke. The i n
demand is set to zero to guarantee unit power factor. There are two loops for the control design, i.e. inner current loop and outer voltage loop to provide necessary control action. Line resistance and reactance decide the plant for the current loop, whereas DC link capacitor is taken as the plant for the voltage loop. The plants for the current loop and the voltage loop are given in
Eq. (4.13) and (4.14) respectively.
F(s) =
R b b
(?)
(?)
=
R y
(?) y
(?)
=
6 q?€
(4.13)
41
G(s) =
R bš
(?) b
(?)
=
%

√ ?
(4.14)
The active and reactive power is controlled independently using the vector control strategy. Aligning the daxis of the reference frame along the stator voltage position is found by Eq. (4.15), v n
= 0, since the amplitude of supply voltage is constant the active power and reactive power are controlled independently by means of i
T
and i n respectively following Eq. (4.16) and (4.17). tan θ
!
= y b
(4.15)
P
?
=
%
•v
T i
T
+ v n i n
‚ (4.16)
Q
?
=
%
•v
T i n
+ v n i
T
‚ (4.17)
4.3.3.2. Modeling and control of machine side converter
The control strategy made for the machine side converter is shown in Fig.4.8. The main purpose of the machine side converter is to maintain the rotor speed constant irrespective of the wind speed and also the control strategy has been implemented to control the active power and reactive power flow of the machine using the rotor current components. The active power flow is controlled through i
and the reactive power flow is controlled through
. To ensure unit power factor operation like grid side converter the reactive power demand is also set to zero here. The mathematical modeling of the machine side converter is given in the following equations [35].
Since the stator is connected to the utility grid and the influence of stator resistance is small, the stator magnetizing current i

can be considered as constant. Under voltage orientation the relationship between the torque and the dq axis voltages, currents and fluxes can be written as follows. Neglecting leakage inductances the stator flux Equations can be written as
λ
T?
= 0 (4.18)
λ n?
= L
?
i n?
+ L
= (L
6?
+ L

)i n?
+ L
= L
6?
i n?
+ •i n?
‚L

≈ L
i

(4.19)
42
ω *
r
T
*
e
ω
r
Q
ϕϕϕϕ
s
L s
×
L m
Q
*
i i
*
qr
*
dr i qr
(
ω
e r
)[
L m i ds
+
L r i dr
]
v
'
qr i qr
R r v
'
dr v
*
qr i dr
R r v
*
dr i dr
(
ωω
e r
)[
L m i qs
+
L r i qr
]
i dr i qr
(
θθθθ − θθθθ
r
)
(
θθθθ
r
)
θθθθ
r
(
ω
e r
)
i abcr
θθθθ
e i ds i qs
V abcs i abcs
Fig 4.8. Control block diagram of machine side converter
The rotor voltage equations can be written from the equations (4.20) and (4.21). v [email protected]
= R
+
T
TA
C q q z i

+ σL
G + (ω
!
− ω
@
)σL
@ i
= R
+
T
TA
•0 + σL
‚ + (ω
!
− ω
@
)σL
@ i
= R
@
= R
+ σL
@
T
TA i [email protected]
+ (ω
!
− ω
@
)σL
@ i
(4.22) v
= R
@ i
+ σL
@
TR be
TA
− (ω
!
− ω
@
) C q q z i

+ σL
G (4.23)
The reference value
V
and
V are given in Eq. (4.24) and (4.25) which are being found from Eq. (4.22) and (4.23). v = v + i
R
@
− (ω − ω
@
L
@
+ L
i n?
© (4.24)
43
v = v + i [email protected]
R
@
− (ω − ω
@
)[i
L
@
+ L
i
T?
] (4.25)
Where v
and v
are found from the current errors processing through standard PI controllers. The reference current i
can be found either from the reference torque given by Eq. (4.27) or form the speed errors (for the purpose of speed control) through standard PI controllers. Similarly i is found from the reactive power errors. The speed and reactive power is controlled using the current control loops.
The electromagnetic torque can be represented as
T
!
=
%
•λ
T?
i n?
− λ n?
i
T?
‚
= −
%
•λ
T?
i n?
‚
= −
%
λ n?
C− q s q z i
G
=
% q s q z
λ n?
i
(4.26)
The value of i is found using Eq. (4.26) i =
ª
’ f∗ yz
×q
×q z s
(4.27)
Where
T
!
∗
= sfš«
5 k e
¬zz
P v…??
= mechanical losses + electrical losses(P j?
)
Mechanical losses include friction and windage losses whereas electrical losses include stator copper loss and rotor copper losses. The maximum mechanical power can be extracted from the wind is proportional to the cube of the rotor speed. The plant for the current loop is decided by the line resistance and reactance, whereas dc link capacitor is taken as the plant for the voltage loop. The plants for the current loop and the voltage loop are given in Eq. (4.28) and (4.29) respectively.
F(S) =
R be
( ) b²
( )
=
R ye y²
( )
( )
=
6
(4.28)
G(S) = −
% q s
´q z
ª yz
(µ?€–)
=
¶
(µ?€–)
(4.29)
44
4.3.4. Modeling and control of battery
The battery converter is a bidirectional DC/DC converter and the main purpose of the battery converter is to give assurance of stable DC link voltage.
V d
*
i
*
b
V
D
V b
1
/(
sL
3
+
R
3
)
i b
I in
1 /(
sC d
)
V d
V d
Fig 4.9. Control block diagram of battery
The boost converter injects current i
6
(1 − d
6
) to the DC link. The inverter and the DC loads draw current
i
.Š
and i
TŠ
from the DC link respectively.
The battery converter can be modeled as
V
·
− V
M
= L
%
TR
S
TA
+ R
% i
M
(4.30)
V
·
= V
T
∙ d
%
(4.31) i
6
(1 − d
6
) − i
.Š
− i
TŠ
− i
M d
%
= C
T
Tl b
TA
(4.32)
4.4. Summary
This chapter explains the configuration of the hybrid microgrid. Two modes of operation for the function of microgrid are also discussed. Also the modeling and control schemes for the used converters are analyzed in the above section.
45
CHAPTER 5
RESULT AND DISCUSSION
A hybrid microgrid whose parameters are given in table 4.1 is simulated using
MATLAB/SIMULINK environment. The operation is carried out for the grid connected mode. Along with the hybrid microgrid, the performance of the doubly fed induction generator, photovoltaic system is analyzed. The solar irradiation, cell temperature and wind speed are also taken into consideration for the study of hybrid microgrid. The performance analysis is done using simulated results which are found using MATLAB.
5.1. Simulation of PV array
Figure (5.1)(5.6) represents IV, PV, PI characteristics with variation in temperature and solar irradiation. The nonlinear nature of PV cell is noticeable as shown in the figures, i.e., the output current and power of PV cell depend on the cell’s terminal operating voltage and temperature, and solar irradiation as well.
Figures (5.1) and (5.2) verify that with increase of cell’s working temperature, the current output of PV module increases, whereas the maximum power output reduces. Since the increase in the output current is much less than the decrease in the voltage, the total power decreases at high temperatures.
150
T=55degC
T=60degC
T=70degC
T=80degC
T=90degC
100
50
0
0 50 100 150 200
Voltage (Volt)
250 300 350 400
Fig 5.1. IV output characteristics of PV array for different temperatures
46
30
25
20
15
10
5
T=55 degC
T=60 degC
T=70 degC
T=80 degC
T=90 degC
0
0 50 100 150 200
Voltage (Volt)
250 300 350 400
Fig 5.2. PV output characteristics of PV array for different temperatures
30
20
T=55 degC
T=60 degC
T=70 degC
T=80 degC
T=90 degC
10
0
0 20 40 60 80 100
Current (Amp)
120 140 160 180
Fig 5.3. PI output characteristics of PV array for different temperatures
200
150
S=1200 mW/sq.cm
S=1100 mW/sq.cm
S=1000 mW/sq.cm
S=900 mW/sq.cm
S=800 mW/sq.cm
100
50
0
0 50 100 150 200
Voltage (Volt)
250 300 350 400
Fig 5.4. IV output characteristics of PV array for different irradiance levels
47
30
25
20
30
20
10
S=1200 mW/sq.cm.
S=1100 mW/sq.cm.
S=1000 mW/sq.cm.
S=900 mW/sq.cm.
S=800 mW/sq.cm.
0
0 50 100 150 200 250
Voltage (Volt)
300 350 400
Fig 5.5. PV characteristics of PV array for different irradiance levels
35
30
25
S=1200 mW/sq.cm.
S=1100 mW/sq.cm.
S=1000 mW/sq.cm.
S=900 mW/sq.cm.
S=800 mW/sq.cm.
20
15
10
5
0
0 50 100
Current (Amp)
150 200
Fig 5.6. PI characteristics of PV array for different irradiance levels
Figures (5.4) and (5.5) show that with increase of solar irradiation, the current output of
PV module increases and also the maximum output power. The reason behind it is the opencircuit voltage is logarithmically dependent on the solar irradiance, however the shortcircuit current is directly proportional to the radiant intensity.
5.2. Simulation of doubly fed induction generator
The response of wind speed, three phase stator voltage and three phase rotor voltage are shown in the figures (5.7)  (5.9). Here the value of wind speed varies between 1.0 to 1.05 pu which is necessary for the study of the performance of doubly fed induction generator. The phase to phase stator voltage is set to 300V whereas the rotor voltage value is 150V.
48
1.1
1.05
1
0.95
0.9
0.85
0 0.1
0.2
0.3
0.4
0.5
Time (sec)
0.6
0.7
Fig 5.7. Response of wind speed
0.8
0.9
1
500
250
Va
Vb
Vc
0
250
75
150
225
300
300
225
150
75
0
0.85
500
0.9
0.91
0.92
0.93
0.94
0.95
0.96
0.97
0.98
Time (sec)
Fig 5.8. Three phase stator voltage of DFIG
0.99
1 va vb vc
1 0.9
0.95
Time (sec)
Fig 5.9. Three phase rotor voltage of DFIG
49
5.3. Simulation results of hybrid grid
The various characteristics of the hybrid microgrid are represented by the figures (5.10) –
(5.25). Here the microgrid operates in the grid tied mode. In this mode, the main converter operates in the PQ mode and power is balanced by the utility grid. The battery is fully charged. AC bus voltage is maintained by the utility grid and DC bus voltage is maintained by the main converter.
Figure (5.10) shows the curve of solar irradiation level which value is set as 950 W/sq.m from 0.0s to 0.1s, increases linearly to 1300 W/sq.m from 0.1s to 0.2s, remains constant from 0.3s to 0.4s, decreases to 950 W/sq.m and keeps that value until 1s. Figures (5.11) –
(5.13) signify output voltage, current and power with respect to the solar irradiation signal.
The output power of PV panel varies 11.25 kW to 13 kW, which closely follows the solar irradiation when the ambient temperature is fixed.
1.3
1.2
1.1
1
0.9
0 0.1
0.2
0.3
0.4
0.5
Time (sec)
0.6
0.7
Fig 5.10. Irradiation signal of the PV array
0.8
0.9
1
420
410
450
440
430
420
410
0 0.1
0.2
0.3
0.4
0.5
Time (sec)
0.6
0.7
Fig 5.11. Output voltage of PV array
0.8
0.9
1
50
30
29
28
27
0 0.1
0.2
0.3
0.4
0.5
Time (sec)
0.6
0.7
Fig 5.12. Output current of PV array
0.8
0.9
13
1
12.5
12
11.5
11
0 0.1
0.2
0.3
0.4
0.5
Time (sec)
0.6
0.7
Fig 5.13. Output power of PV array
0.8
0.9
1
1.5
1
0.5
0
0 0.5
1 1.5
2 2.5
Time (sec)
3 3.5
4 4.5
x 10
3
5
Fig 5.14. Generated PWM signal for the boost converter
51
1400
1200
1000
800
600
400
200
0
0.8
0.82
0.84
0.86
0.88
0.9
Time (sec)
0.92
0.94
0.96
0.98
Fig 5.15. Output voltage across DC load
1
Figure (5.14) shows the gate pulse signal which is fed to the switch of boost converter.
The output voltage across DC load is represented by figure (5.15) which is settled to around
820V.
85.05
85
84.95
84.9
84.85
0.7
0.75
0.8
0.85
Time (sec)
0.9
Fig 5.16. State of charge of battery
0.95
165.5
165
164.5
164
163.5
163
162.5
162
161.5
0.8
0.82
0.84
0.86
0.88
0.9
Time (sec)
0.92
0.94
0.96
0.98
Fig 5.17. Voltage of battery
1
1
52
100
50
0
50
100
150
200
0.8
0.82
0.84
0.86
0.88
0.9
Time (sec)
0.92
0.94
0.96
0.98
Fig 5.18. Current of battery
500
1
Va
Vb
Vc
250
0
250
500
0.8
0.82
0.84
0.86
0.88
0.9
Time (sec)
0.92
0.94
0.96
0.98
Fig 5.19. Output voltage across AC load
150
100
50
0
50
100
150
0.8
0.82
0.84
0.86
0.88
0.9
Time (sec)
0.92
0.94
0.96
0.98
Fig 5.20. Output current across AC load ia ib ic
1
1
53
300
Va
Vb
Vc
0
300
0.90
0.92
0.94
Time (sec)
0.96
0.98
Fig 5.21. AC side voltage of the main converter
1
40
30
20
10
0
10
20
30
40
0.80
0.82
0.84
0.86
0.88
0.90
0.92
0.94
0.96
0.98
Time (sec) ia ib ic
1
Fig 5.22. AC side current of the main converter
The battery characteristics are shown in the figures (5.16)  (5.18). The state of charge of battery is set at 85% whereas the battery current varies between 50 to 50A and the value of battery voltage is nearly 163.5. The output characteristics of AC load voltage and current are represented by the figures (5.19) and (5.20). Phase to phase voltage value of AC load is 300V and current value is 50A.Figure (5.21) and (5.22) shows the voltage and current responses at the AC side of the main converter when the solar radiation value varies between 9501300
W/sq.m with a fixed DC load of 25 kW.
54
35
30
25
20
15
10
0.65
0.7
0.75
0.8
0.85
Time (sec)
0.9
Fig 5.23. Output power of DFIG
500
0.95
Va
Vb
Vc
1
0
500
0.980
0.982
0.984
0.986
0.988
0.990
0.992
0.994
0.996
0.998
Time (sec)
Fig 5.24. Three phase supply voltage of utility grid
500
1
Va
Vb
Vc
0
500
0.98
0.982
0.984
0.986
0.988
0.99
0.992
0.994
0.996
0.998
Time (sec)
1
Fig 5.25. Three phase PWM inverter voltage
55
Figure (5.23) shows the response of the DFIG power output which becomes a stable value
32kW due to mechanical inertia. Figure (5.24) and (5.25) represents the three phase supply voltage to the utility grid and three phase PWM inverter output voltage respectively.
5.4. Summary
In this chapter simulation results are discussed briefly. Also various characteristics of PV array, doubly fed induction generator, battery and converters are studied in this chapter and the waveforms are traced.
56
CHAPTER 6
CONCLUSION AND SCOPE OF FUTURE WORK
6.1. Conclusion
The modeling of hybrid microgrid for power system configuration is done in
MATLAB/SIMULINK environment. The present work mainly includes the grid tied mode of operation of hybrid grid. The models are developed for all the converters to maintain stable system under various loads and resource conditions and also the control mechanism are studied. MPPT algorithm is used to harness maximum power from DC sources and to coordinate the power exchange between DC and AC grid. Although the hybrid grid can diminish the processes of DC/AC and AC/DC conversions in an individual AC or DC grid, there are many practical problems for the implementation of the hybrid grid based on the current AC dominated infrastructure. The efficiency of the total system depends on the diminution of conversion losses and the increase for an extra DC link. The hybrid grid can provide a reliable, high quality and more efficient power to consumer. The hybrid grid may be feasible for small isolated industrial plants with both PV systems and wind turbine generator as the major power supply.
6.2. Scope of future work
a.
The modeling and control can be done for the islanded mode of operation. b.
The control mechanism can be developed for a microgrid containing unbalanced and nonlinear loads.
57
References
[1] S. Bose, Y. Liu, K. BaheiEldin, J.de Bedout, and M. Adamiak, “Tie line Controls in
Microgrid Applications,” in iREP Symposium Bulk Power System Dynamics and Control
VII, Revitalizing Operational Reliability, pp. 19, Aug. 2007.
[2] R. H. Lasseter, “MicroGrids,” in Proc. IEEEPES’02, pp. 305308, 2002.
[3] Michael Angelo Pedrasa and Ted Spooner, “A Survey of Techniques Used to Control
Microgrid Generation and Storage during Island Operation,” in AUPEC, 2006.
[4] F. D. Kanellos, A. I. Tsouchnikas, and N. D. Hatziargyriou, “Microgrid Simulation during GridConnected and Islanded Mode of Operation,” in Int. Conf. Power Systems
Transients (IPST’05), June. 2005.
[5] Y. W. Li, D. M. Vilathgamuwa, and P. C. Loh, Design, analysis, and realtime testing of a controller for multi bus microgrid system, IEEE Trans. Power Electron., vol. 19, pp.
11951204, Sep. 2004.
[6] R. H. Lasseter and P. Paigi, “Microgrid: A conceptual solution,” in Proc. IEEE
PESC’04, pp. 42854290, 2004.
[7] F. Katiraei and M. R. Iravani, “Power Management Strategies for a Microgrid with
Multiple Distributed Generation Units,” IEEE trans. Power System, vol. 21, no. 4, Nov.
2006.
[8] P. Piagi and R. H. Lasseter, “Autonomous control of microgrids,” in Proc. IEEEPES’06,
2006, IEEE, 2006.
[9] M. Barnes, J. Kondoh, H. Asano, and J. Oyarzabal, “RealWorld MicroGrids an
Overview,” in IEEE Int. Conf. Systems of Systems Engineering, pp.18, 2007.
[10] Chi Jin, Poh Chiang Loh, Peng Wang, Yang Mi, and Frede Blaabjerg, “Autonomous
Operation of Hybrid ACDC Microgrids,” in IEEE Int. Conf. Sustainable Energy
Technologies, pp. 17, 2010.
[11] Y. Zoka, H. Sasaki, N.Yomo, K. Kawahara, C. C. Liu, “An Interaction Problem of
Distributed Generators Installed in a MicroGrid,” in Proc. IEEE Elect. Utility
Deregulation, Restructuring and Power Technologies, pp. 795799, Apr. 2004.
[12] H. Nikkhajoei, R. H. Lasseter, “Microgrid Protection,” in IEEE Power Engineering
Society General Meeting, pp. 16, 2007.
[13] Zhenhua Jiang, and Xunwei Yu, “Hybrid DC and ACLinked Microgrids: Towards
Integration of Distributed Energy Resources,” in IEEE Energy2030 Conf., pp.18, 2008.
58
[14] Bo Dong, Yongdong Li, ZhixueZheng, Lie Xu “Control Strategies of Microgrid with
Hybrid DC and AC Buses,” in Power Electronics and Applications, EPE'11,
14 th
European Conf., pp. 18, 2011.
[15] Dong Bo, Yongdong Li , and Zedong Zheng, “Energy Management of Hybrid DC and
AC Bus Linked Microgrid,” in IEEE Int. Symposium Power Electronics for Distributed
Generation System, pp. 713716, 2010.
[16] Xiong Liu, Peng Wang, and Poh Chiang Loh, “A Hybrid AC/DC Microgrid and Its
Coordination Control,” IEEE Trans. Smart Grid, vol. 2, no. 2, pp. 278286 June. 2011.
[17] Mesut E. Baran, and Nikhil R. Mahajan, “DC Distribution for Industrial Systems:
Opportunities and Challenges,” IEEE Trans. Industry Applications, vol. 39, no. 6, pp.
15961601, Nov/Dec. 2003.
[18] Y. Ito, Z. Yang, and H. Akagi, “DC Microgrid Based Distribution Power Generation
System,” in Proc. IEEE Int. Power Electron. Motion Control Conf., vol. 3, pp. 1740
1745, Aug. 2004.
[19] A. Arulampalam, N. Mithulananthan, R.C. Bansal, and T.K. Saba, “Microgrid Control of
PV WindDiesel Hybrid System with Islanded and Grid Connected Operations,” in
Proc. IEEE Int. Conf. Sustainable Energy Technologies, pp. 15, 2010.
[20] Poh Chiang Loh, Ding Li, and FredeBlaabjerg, “Autonomous Control of Interlinking
Converters in Hybrid ACDC Microgrid with Energy Storages,” in IEEE Energy
Conversion Congress and Exposition (ECCE), pp. 652658, 2011.
[21] M. E. Ropp and S. Gonzalez, “Development of a MATLAB/Simulink model of a single phase grid connected photovoltaic system,” IEEE Trans. Energy Conv., vol. 24, no. 1, pp. 195202, Mar 2009.
[22] Mei Shan Ngan, Chee Wei Tan, “A Study of Maximum Power Point Tracking
Algorithms for Standalone Photovoltaic Systems,” in IEEE Applied Power electronics
Colloquium (IAPEC), pp. 2227, 2011.
[23] K. H. Hussein, I. Muta, T.Hoshino, and M. Osakada, “Maximum Photovoltaic Power
Tracking: An Algorithm for rapidly changing atmospheric conditions,” in Proc. Inst.
Elect. Engg. Gener. Transm. Distrib., vol. 142, pp. 59–64, Jan.1995.
[24] D. P. Hohm, M. E. Ropp, “Comparative Study of Maximum Power Point Tracking
Algorithms Using an Experimental, Programmable, Maximum Power Point Tracking
Test Bed”, in IEEE,pp.16991702, 2000.
59
[25] Marcello Gradella Villalva, Jones Rafael Gazoli, and Ernesto Ruppert Filho, “Analysis and Simulation of the P&O MPPT Algorithm using a linearized PV Array model,” in
Industrial Electronics, IECON’09, 35 th
Annual Conf., pp. 189195, 2009.
[26] Mohammad A. S. Masoum, Hooman Dehbonei, and Ewald F. Fuchs, “Theoretical and
Experimental Analyses of Photovoltaic Systems with Voltage and CurrentBased
Maximum PowerPoint Tracking”, in IEEE Trans. Energy Conversion, vol. 17, no. 4, pp.
514522, Dec. 2002.
[27] O. Tremblay, L. A. Dessaint, and A. I. Dekkiche, “A generic battery model for the dynamic simulation of hybrid electric vehicles,” in Proc. IEEE Veh. Power propulsion
Conf., pp. 284289, 2007.
[28] S. N. Bhadra, D. Kastha, S. Banerjee, “Wind Electrical Systems,” Oxford University
Press, New Delhi, 2009.
[29] B. K. Bose, “Modern Power Electronics and AC Drives,” PrenticeHall, Inc., New Delhi,
2002.
[30] A. Girgis and S. Brahma, "Effect of Distributed Generation on Protective Device
Coordination in Distribution System," in Large Engineering Systems Conf. Power
Engineering, pp. 115119, 2001.
[31] N. Kroutikova, C. A. HernandezAramburo, and T. C. Green, “Statespace model of grid connected inverters under current mode control,” IET Elect. Power Appl., vol. 1, no. 3, pp. 329338, 2007.
[32] D. Sera, R. Teodorescu, J. Hantschel, and M. Knoll, “Optimized maximum power point tracker for fastchanging environmental conditions,” IEEE Trans. Ind. Electron., vol.55, no. 7, pp. 26292637, Jul. 2008.
[33] B. Bryant and M. K. Kazimierczuk, “Voltage loop of boost PWM DCDC converters with peak current mode control,” IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 53, no.
1, pp. 99105, Jan. 2006.
[34] S. Arnalte, J. C. Burgos, and J. L. Rodriguezamenedo, “Direct torque control of a doubly fed induction generator for variable speed wind turbines,” Elect. Power Compon.
Syst., vol. 30, no. 2, pp. 199216, Feb. 2002.
[35] R. Pena, J. C. Clare, G. M. Asher, “Doubly fed induction generator using back to back
PWM converters and its application to variable speed wind energy generation,” in Proc.
IEE Electr. Power Appl., vol. 143, no. 3, pp. 231241, may 1996.
60
* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project
advertisement