REPORT 10-22
Scalable Newton-Krylov Solver for Very Large Power Flow
R. Idema, D.J.P. Lahaye, C. Vuik, and L .van der Sluis
ISSN 1389-6520
Reports of the Department of Applied Mathematical Analysis
Delft 2010
Copyright 
2010 by Department of Applied Mathematical Analysis, Delft, The Netherlands.
No part of the Journal may be reproduced, stored in a retrieval system, or transmitted, in any
form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the
prior written permission from Department of Applied Mathematical Analysis, Delft University of
Technology, The Netherlands.
Scalable Newton-Krylov Solver for Very Large Power Flow
R. Idema, D.J.P. Lahaye, C. Vuik∗ and L. van der Sluis†
The power flow problem is generally solved by the Newton-Raphson method with a sparse
direct solver for the linear system of equations in each iteration. While this works fine for
small power flow problems, we will show that for very large problems the direct solver is very
slow and we present alternatives that scale much better in the problem size. For the largest
test problem, with around one million busses, the presented alternatives are over 120 times
faster than using a direct solver.
Electrical power systems can be regarded to be among the most complex systems designed, constructed, and operated by humans. The consumers are supplied with the requested amount of
active and reactive power at a constant frequency and with a constant voltage. Loads are switched
on and off continuously, and because electricity cannot be efficiently stored in large quantities, the
balance between the amount of generated and consumed electricity has to be maintained by control
actions. This has brought forward the basic design of these power systems: a solid, centralized,
vertical structure. The production of electrical energy or, more correctly, the conversion of the
primary energy source into electricity takes place in a central core consisting of a number of large
power plants, completely controlled in terms of their output. Tree-structured distribution networks
transport this energy towards the distributed consumers.
The sophisticated control of the large power plants, combined with the efficient design of the
networks, has created the present power systems, based on the principles of robustness, reliability,
security, stability, and quality of energy supply. These basic principles have always been the system
design goals and were traditionally interwoven with the centralized, vertical structure. Aberration
from this structure has often been treated with skepticism, implying violation of these principles.
But in the recent years two socio-economic factors, namely the stimulation of the use of renewable
energy as source of primary energy and the liberalization of the energy markets, created a boost
towards a new concept. The systems of the future should have a non-centralized, distributed
In such distributed systems, the generation of electrical energy takes place in a large number of
small- to medium-scale geographically distributed power plants. But this horizontal operation is
associated with a basic restricting feature: the output of these power plants cannot be regulated,
either due to their operation in a liberalized market environment, or due to unavailability of the
primary energy mover when renewable generation is concerned. Moreover, these generators are
∗ Delft University of Technology, Delft Institute of Applied Mathematics, Mekelweg 4, 2628 CD Delft, The
Netherlands. E-mail: [email protected], [email protected], [email protected]
† Delft University of Technology, Power Systems Department, Mekelweg 4, 2628 CD Delft, The Netherlands.
E-mail: [email protected]
connected at the lower voltage levels (in contrast to the current layout of the system where most of
the generation is connected at transmission level), they are connected by means of power electronic
interfaces (instead of the classic synchronous generator paradigm), and their output presents an
intermittent character (leading to strong fluctuations in the power injected in the grid).
Resulting from these radical structural changes, increased uncertainty in electrical energy generation penetrates system operation. The system design engineers are therefore forced to deal with
new problems, such as finding out under which conditions the new structure does not violate the
operational principles of the power system, and what the design methodologies for the transition
from the present to the future structure are.
A possible consequence of the transition from the current vertically-operated power system into a
future horizontally-operated power system for the power system is that the transmission network
becomes less active, while the distribution network becomes a lot more active. The power is not
only consumed but also generated in the distribution network. The direction of power flows in the
network become less predictable, and one-way traffic becomes two-way traffic. Voltage stability
becomes an issue in this case, as the voltage is no longer controlled by the generators in the large
centralized power stations and tap-changing transformers alone. The decentralized generation has
to play a role in controlling the voltage.
The power flow, or load flow, computation is the most important network computation in power
systems. It calculates the voltage magnitude and angle in each bus of a power system, under
specified system operation conditions. Other quantities, such as current values, power values, and
power losses, can be calculated easily when the bus voltages are known.
Power flow computations bring insight in the steady-state behavior of a power system. This is
needed in many control and planning applications. For example, whenever power system components have to be taken out of service, say for maintenance purposes, it is crucial to know if
the power system will still function within system limits without these particular components, or
whether additional measures have to be taken. Moreover, a power system has to be at least n?1
secure, meaning that if any one component fails the power system still functions properly. These
are typical problems that can be solved by doing load flow computations on the power system
More and more nation wide power systems are being connected to be able to exchange cheap excess
power. This results in huge connected power systems with many times the busses and transmission
lines of the classical systems. A small set of simultaneous failures could propagate through the
entire system, causing a massive blackout. Therefore providing security against overloading is
more important than ever. Furthermore, in a larger power system it is much more likely to have
multiple failures at the same time. Therefore n?2 security analysis is already being done regularly,
taking huge amounts of computational effort.
Currently, power flow calculations are done on the transmission network while the distribution
network is aggregated at busses in the power system model. However, with the transition to
horizontally-operated power systems with distributed generation it will no longer be possible to
simply work on the transmission network alone. The power flow computations will have to be
extended to the distribution network, possibly down to as low as the 10kV systems. This would
result in power flow problems of sheer massive size. Motivated by these developments, we explore
mathematical techniques for power flow problems that are particularly well equipped to deal with
very large problem sizes.
Traditionally the power flow problem is solved by use of the Newton-Raphson method, with a
direct solver for the linear systems [23, 24]. It has been recognized that iterative linear solvers can
offer advantages over sparse direct solvers for large power systems [10, 5, 3, 12, 29]. The question
arises when iterative methods are better than direct methods for power flow problems. The key
to answering this question is in the scaling of the computational time of the power flow algorithm
in the problem size.
In this paper we will answer that question for a test set of power flow problems, ranging up to
one million busses. We will show how the Newton-Raphson method with a direct solver scales,
and compare it with the scaling behavior of the Newton-Raphson method with an iterative linear
solver with a selection of preconditioners.
We will see that direct solvers, or any other method using a complete LU factorization, scale very
badly in the problem size. The alternatives that we propose in this paper show near linear scaling,
and are therefore much faster for large power flow problems. Using a direct solver the largest
problem takes over an hour to solve, while our solver can solve it in less than 30 seconds, that is
120 times faster.
The implementation of our solver is done in C++ using PETSc (Portable, Extensible Toolkit for
Scientific Computation) [2], a state of the art C library for scientific computing. The NewtonRaphson method is part of the SNES framework within PETSc. PETSc can be used to produce
both sequential programs, and programs running in parallel on multiple processors. The results
that we present in this paper were all executed on a single processor core.
The test set of power flow problems used is based on the UCTE1 winter 2008 study model, which
consists of 4253 busses and 7191 lines. The smallest problem is the UCTE winter 2008 study
model itself, while the larger problems are all constructed by copying the model multiple times,
and interconnecting the copies with new transmission lines.
Power Flow Problem
The power flow problem is the problem to determine the voltage at each bus of a power system,
given the supply at each generator and the demand at each load in the network.
Let Y = G + jB denote the network admittance matrix of the power system. Then the power
flow problem can be formulated as the nonlinear system of equations
k=1 |Vi | |Vk | (Gik cos δik + Bik sin δik ) = Pi ,
k=1 |Vi | |Vk | (Gik sin δik − Bik cos δik ) = Qi ,
where |Vi | is the voltage magnitude, δi is the voltage angle, with δij = δi − δj , Pi is the active
power, and Qi is the reactive power at bus i. For details see for example [19, 11].
Define the power mismatch function as
Pi − k=1 |Vi | |Vk | (Gik cos δik + Bik sin δik )
F (x) =
Qi − k=1 |Vi | |Vk | (Gik sin δik − Bik cos δik )
where x is the vector of voltage angles and magnitudes. Then we can reformulate the power flow
problem (1), (2), to finding a solution vector x such that
F (x) = 0.
This is the system of non-linear equations that we will solve to find the solution of the power flow
1 UCTE is a former association of transmission system operators in Europe. As of July 2009, the European
Network of Transmission System Operators for Electricity (ENTSO-E), a newly formed association of 42 TSOs
from 34 countries in Europe, has taken over all operational tasks of the existing European TSO associations,
including UCTE. See http://www.entsoe.eu/
Power Flow Solution
Our solver is based on the Newton-Raphson method for systems of nonlinear equations like (4).
This iterative method updates the approximate solution xi in each iteration with a Newton step
si , calculated from the linear system of equations
Ji si = −F i ,
where Ji is the Jacobian of the power mismatch F . For details on the Newton-Raphson method
see for example [7].
In power flow analysis, the classical way to solve the linear system (5) is with a direct solver.
Effectively, this means that Ji is factorized into a lower triangular factor Li , and an upper triangular factor Ui , such that Li Ui = Ji . Then the linear problem Li Ui si = −F i can be solved using
forward and backward substitution, which are both very fast operations.
The LU factorization was originally designed for full matrices, and has complexity n3 in the
problem size n. It was adapted very efficiently for sparse matrices, like the Jacobian matrix Ji
in the power flow problem. Although the scaling of the LU factorization’s computational time
in the problem size is much more complex for sparse matrices, it is well-known that in general it
does not scale linearly for large problem sizes. This is something that is also clearly illustrated by
our numerical experiments in Section 6. For more information on sparse direct methods see for
example [8, 4].
To get a power flow solver that scales better for large problems we will look at alternative linear
solvers for problem (5) in the form of iterative methods, in particular the Generalized Minimal
Residual method (GMRES) [18]. The reason to use GMRES is that it, being a minimal residual
method, solves the problem in the least number of matrix-vector multiplications. From the performance of GMRES we can then judge whether we should try other iterative linear solvers, like
Bi-CGSTAB [27, 20] or IDR(s) [21].
With the Newton-Raphson method there is only a certain accuracy that we can expect to reach
in each iteration. Solving the linear system to an accuracy higher than that needed to reach
the best Newton-Raphson accuracy in the current step would be a waste of computational time.
Therefore, when using an iterative linear solver we should not solve each linear system to full
precision. Instead we should determine forcing terms ηi and use the iterative linear solver to solve
up to
kJi si + F i k ≤ ηi kF i k.
We then speak of an inexact Newton-Krylov method [6].
In [12] we discussed three methods of choosing the forcing terms ηi . Here we will use the method
proposed by Eisenstat and Walker [9]. This method has been successfully used in practice on
many different problems, and also provided very good results for us.
Essential to the performance of a Krylov method like GMRES is a good preconditioner. See
for example [17] for information on preconditioning. In our solver we use right preconditioning,
meaning that we iteratively solve the linear system
Ji Pi−1 z i = −F i ,
and then get the Newton step si by solving Pi si = z i . For fast convergence the preconditioner
matrix Pi should resemble the coefficient matrix Ji . At the same time we need a fast way to solve
linear systems of the form Pi ui = v i , as such a system has to be solved in each iteration of the
linear solver.
In this paper we will construct preconditioners in the form of a product of a lower and upper
triangular matrix Pi = Li Ui . Any linear system with coefficient matrix Pi can then simply be
solved using forward and backward substitution. To come to such a preconditioner we choose
a target matrix Qi and then construct either the LU factorization Li Ui = Qi or an incomplete
LU (ILU) factorization [13, 14] Li Ui of Qi . In the case of the full LU factorization we get a
preconditioner Pi = Li Ui = Qi , whereas with the ILU factorization the preconditioner Pi = Li Ui
only resembles the target matrix Qi . The trade-off here is that an ILU factorization is cheaper to
build than an LU factorization, whereas the full LU factorization will generally results in a better
There are three choices that we will consider for the target matrix Qi . These are the Jacobian
matrix Qi = Ji , the initial Jacobian matrix Qi = J0 , and Qi = Φ, where
0 B ′′
with B ′ and B ′′ as in the BX scheme of the Fast Decoupled Load Flow (FDLF) [22] method as
proposed by van Amerongen [26].
The FDLF matrix Φ can be seen as an approximate Schur complement of the initial Jacobian
matrix [15]. In previous studies Φ has already proven to be a good preconditioner [10], [12], while
containing only half the non-zeros of the Jacobian matrix, thus providing benefits in computing
time and memory.
Other preconditioners that are known to often work well for large problems are preconditioners
based on iterative methods. Only stationary iterative methods can be used as a preconditioner for
standard implementations of GMRES, Bi-CGSTAB, and IDR(s). Non-stationary iterative methods, like GMRES itself, can only be used with special flexible iterative methods, like FGMRES [16].
The use of FGMRES with a GMRES based preconditioner has been explored in [29].
Algebraic Multigrid (AMG) methods can also be used as a preconditioner. Running one cycle of
AMG, with a stationary solve on the coarsest grid, leads to a stationary preconditioner. Such a
preconditioner is very well suited for extremely large problems. For more information on AMG
see [25, App. A].
As mentioned in the previous section, we use complete and incomplete LU factorizations of the
target matrix Qi to construct our preconditioners. In this section we consider the quality and fill,
i.e., the number of non-zeros, of the preconditioners and how to control these properties.
When using a full LU factorization the quality of the preconditioner is predetermined by the
choice of the target matrix, as Pi = Qi . However, when doing an LU decomposition on a sparse
matrix we can expect the factors Li and Ui to have more non-zeros than the original matrix
Qi . This is referred to as fill-in. The number of non-zeros in the factors divided by the number
of non-zeros in the original matrix is called the fill-in ratio. The higher the fill-in ratio is, the
more memory is needed, and the more computational time is needed for the factorization and the
forward and backward substitutions. It is therefore paramount to try to minimize the fill-in of the
LU factorization.
The fill-in can be controlled by reordering the rows and columns of the matrix that is to be
factored. However, finding the ordering that minimizes fill-in has been proven to be NP-hard [28].
Many methods have been developed to quickly find a good reordering, see for example [8, 4].
The method of incomplete LU factorization that we use is ILU(k), where k is the number of
levels of fill-in. This method determines the allowed non-zero positions in the factors based on the
k-level, and subsequently calculates the best values for these non-zero positions.
As the fill-in is predetermined one might think that reordering is not important. However, even
if the reordering does not influence the fill-in for an ILU(k) factorization, it does influence the
quality with which Li Ui approximates the target matrix Qi .
In our experiments we have used the AMD reordering [1], which yielded very good results for both
the LU and ILU(k) factorizations. To illustrate the impact of a good reordering, Table 1 shows
the results of a single LU factorization of the initial Jacobian matrix J0 for the uctew032 problem
(see Table 2). The factorization time is measured in seconds.
factorization time
fill-in ratio
Table 1: LU factorization of J0 for uctew032
The influence of reordering on the ILU(k) factorization is less drastic, but still very useful. For the
uctew032 case, when solving the initial Jacobian system J0 s0 = −F 0 with an ILU(k) factorization
of J0 as preconditioner, with different choices of k, we observed GMRES needing around 25% less
iterations to converge when using the AMD reordering, compared to using no reordering.
Numerical Experiments
In this section we present the results of our numerical experiments. The test cases used are created
using the UCTE winter 2008 model, as described in Section 1. Table 2 shows the number of busses
and lines in the test problems, as well as the number of non-zeros in the Jacobian matrix nnz(J).
The naming convention used is uctewXXX, where XXX is the number of times the model is copied
and interconnected.
Table 2: Power flow test problems
All tests are run on a single core of a machine with Intel Core2 Duo 3GHz CPU and 16Gb memory.
The problems are solved from a flat start, up to an accuracy of 10−6 .
First we consider the results using preconditioners based on the Jacobian matrix, i.e., Qi = Ji .
Fig. 1 shows the solve time in seconds versus the number of busses. Note that using a full LU
factorization in this case is effectively the same as using a direct solver.
It is clear that the direct solver does not scale well in the problem size. For our test cases it is
competitive up to about 150k busses, but deteriorates rapidly for larger problems. The largest
problem took over an hour to solve with a direct linear solver, whereas the ILU(4), ILU(8), or
ILU(12) factorization of Ji as preconditioner all solved the problem in less than a minute.
Figure 1: Solve time in seconds with Jacobian preconditioner
I: LU of Ji , II: ILU(4) of Ji , III: ILU(8) of Ji , IV: ILU(12) of Ji
We also tested k-levels higher than 12, but found that the factorization then started taking too
much time, and the solver became slower. For our test data ILU(12) provided the optimum in
terms of solve time on the largest cases.
Next we test preconditioners based on the initial Jacobian matrix, i.e., Qi = J0 . Fig. 2 shows the
solve times for these preconditioners.
Figure 2: Solve time in seconds with initial Jacobian preconditioner
I: LU of J0 , II: ILU(4) of J0 , III: ILU(8) of J0 , IV: ILU(12) of J0
Since the preconditioner is the same in each iteration, we only have to make a factorization once
at the start. Therefore using the LU factorization, where the factorization is by far the most
expensive computation in the entire solve, is much faster than for the direct solver, where we had
to make a new factorization in each iteration.
After the first iteration, J0 will not be as good a preconditioner as Ji , thus more GMRES iterations
will be needed. We see that using ILU(4) of J0 is slower than using ILU(4) of Ji . This is because
the ILU(4) factorization is very fast, and most of the time is spend on GMRES iterations. With
J0 as preconditioner the number of GMRES iteration goes up by more than is saved by having to
do only one factorization. However, for higher k-levels like ILU(12), the extra GMRES iterations
needed when using J0 cost less time than the extra factorizations needed for Ji .
Finally we experiment with the FDLF matrix as basis for our preconditioners, i.e., Qi = Φ. Fig. 3
shows the results.
Figure 3: Solve time in seconds with FDLF preconditioner
I: LU of Φ, II: ILU(4) of Φ, III: ILU(8) of Φ, IV: ILU(12) of Φ
Again we only have to make a single factorization, and the target matrix has about half the
non-zeros of the Jacobian. Because of this, using the LU factorization can stay competitive
with the ILU(k) alternatives longer, up to problem sizes of around 300k busses. However, for
larger problems the LU factorization again starts to show bad scaling behavior, and the ILU(12)
factorization is a clear winner.
Inspection of the solve times showed that for our test problems using preconditioners based on Ji ,
while competitive for smaller problems, was never better than using Qi = J0 or Qi = Φ. In Fig. 4
we therefore compare the LU and ILU(12) factorizations of J0 and Φ.
Obviously, for the largest problems the ILU(12) factorizations perform the best. The variant using
ILU(12) of Φ is slightly faster, but close inspection of the solver output shows this is actually due
to the inexact Newton-Krylov solver needing 8 Newton-Raphson iterations when using J0 , against
6 iterations for Φ. This has to do with being a bit lucky, or unlucky, at how the Newton step
exactly turns out for a certain problem under the forcing term condition (6), much more than it
has to do with the quality of the preconditioner.
Figure 4: Solve time for all test problems with the best performing methods
I: LU of J0 , II: LU of Φ, III: ILU(12) of J0 , IV: ILU(12) of Φ
Note that under 150k busses it seems there is not much between these methods. Closer inspection
reveals that this is indeed true, see Fig. 5. Differences here are again more due to the vagaries of
the inexact Newton-Krylov method, than to the quality of the preconditioners.
Figure 5: Solve time for the smaller problems with the best performing methods
I: LU of J0 , II: LU of Φ, III: ILU(12) of J0 , IV: ILU(12) of Φ
To validate our choice for GMRES as iterative solver, we have also tested Bi-CGSTAB with the
same preconditioners. Bi-CGSTAB should be expected to outperform GMRES when a lot of
GMRES iterations are needed, meaning that Bi-CGSTAB will become a better alternative when
the preconditioner becomes worse.
Our tests showed that for the largest test problem Bi-CGSTAB was faster than GMRES when
using ILU(4) preconditioners, and slightly slower than GMRES with ILU(12). Using ILU(4) with
Bi-CGSTAB was still significantly slower than using ILU(12) with either iterative linear solver.
The results for ILU(8) were hard to compare because an extra Newton iteration was needed when
using Bi-CGSTAB.
The experiments of the previous section clearly illustrates the bad scaling of the LU factorization
in the problem size. Because of that bad scaling direct sparse solvers, but also Krylov methods
with preconditioners based on the LU factorization, are not viable options for the solution of the
linear systems that arise when solving power flow problems with the Newton-Raphson method.
For the same reason the classical implementation of the Fast Decoupled Load Flow method is also
not viable for very large problems.
To solve very large power flow problems, we need a scalable solver to solve the linear systems.
In this paper we have proposed to use an iterative linear solver, in particular GMRES, with an
ILU(k) factorization of the Jacobian matrix Ji , the initial Jacobian matrix J0 , or the FDLF matrix
Φ, as preconditioner.
We have shown that a good reordering strategy is essential to reduce the fill-in of the LU factorization, but also to improve the quality of ILU factorizations. In [12] we already showed the
importance of choosing good forcing terms for the inexact Newton-Krylov method. Using the
AMD reordering, and the Eisenstat and Walker forcing terms, we have run experiments on power
flow problems up to a million busses.
The experiments show that the ILU(k) preconditioners scale very well for the tested problem sizes.
Factorizing a preconditioner once at the start, i.e., using J0 or Φ, generally performed better than
refactorizing in each iteration, i.e., using Ji . The best results were attained with an ILU(12)
factorization of either J0 or Φ as a preconditioner, solving the largest problem with over a million
busses in around 30 seconds.
Experiments with Bi-CGSTAB showed to be very competitive. When using lower quality preconditioners, i.e., ILU factorization with lower k-levels, Bi-CGSTAB led to a faster power flow solver
than GMRES. However, for the fastest ILU(12) based preconditioners GMRES was slightly faster
than Bi-CGSTAB.
The authors would like to thank Robert van Amerongen for his many contributions, providing
hands-on experience, and invaluable insight on the subject of power flow analysis. Further thanks
are due to Barry Smith for his help with the PETSc package, and UCTE/ENTSO-E for providing
the UCTE study model, and their assistance on using it.
[1] P. R. Amestoy, T. A. Davis, and I. S. Duff. An approximate minimum degree ordering
algorithm. SIAM J. Matrix Anal. Appl., 17(4):886–905, October 1996.
[2] S. Balay, K. Buschelman, V. Eijkhout, W. D. Gropp, D. Kaushik, M. G. Knepley, L. Curfman McInnes, B. F. Smith, and H. Zhang. PETSc users manual. Technical Report ANL-95/11
- Revision 3.1, Argonne National Laboratory, 2010. http://www.mcs.anl.gov/petsc/.
[3] D. Chaniotis and M. A. Pai. A new preconditioning technique for the GMRES algorithm in
power flow and P − V curve calculations. Electrical Power and Energy Systems, 25:239–245,
[4] T. A. Davis. Direct Methods for Sparse Linear Systems. SIAM, Philadelphia, 2006.
[5] F. de León and A. Semlyen. Iterative solvers in the Newton power flow problem: preconditioners, inexact solutions and partial Jacobian updates. IEE Proc. Gener. Transm. Distrib,
149(4):479–484, 2002.
[6] R. S. Dembo, S. C. Eisenstat, and T. Steihaug. Inexact Newton methods. SIAM J. Numer.
Anal., 19(2):400–408, 1982.
[7] J. E. Dennis, Jr. and R. B. Schnabel. Numerical Methods for Unconstrained Optimization
and Nonlinear Equations. Prentice Hall, New Jersey, 1983.
[8] I. S. Duff, A. M. Erisman, and J. K. Reid. Direct Methods for Sparse Matrices. Oxford
University Press, New York, 1986.
[9] S. C. Eisenstat and H. F. Walker. Choosing the forcing terms in an inexact Newton method.
SIAM J. Sci. Comput., 17(1):16–32, 1996.
[10] A. J. Flueck and H. D. Chiang. Solving the nonlinear power flow equations with an inexact
Newton method using GMRES. IEEE Transactions on Power Systems, 13(2):267–273, 1998.
[11] R. Idema, D. J. P. Lahaye, and C. Vuik. Load flow literature survey. Report 09-04, Delft
Institute of Applied Mathematics, Delft University of Technology, 2009.
[12] R. Idema, D. J. P. Lahaye, C. Vuik, and L. van der Sluis. Fast Newton load flow. In
Transmission and Distribution Conference and Exposition, 2010 IEEE PES, pages 1–7, April
[13] J. A. Meijerink and H. A. van der Vorst. An iterative solution method for linear systems
of which the coefficient matrix is a symmetric m-matrix. Mathematics of Computation,
31(137):148–162, January 1977.
[14] J. A. Meijerink and H. A. van der Vorst. Guidelines for the usage of incomplete decompositions in solving sets of linear equations as they occur in practical problems. Journal of
Computational Physics, 44(1):134–155, 1981.
[15] A. J. Monticelli, A. Garcia, and O. R. Saavedra. Fast decoupled load flow: Hypothesis,
derivations, and testing. IEEE Transactions on Power Systems, 5(4):1425–1431, 1990.
[16] Y. Saad. A flexible inner-outer preconditioned GMRES algorithm. SIAM J. Sci. Comput.,
14(2):461–469, March 1993.
[17] Y. Saad. Iterative methods for sparse linear systems. SIAM, second edition, 2000.
[18] Y. Saad and M. H. Schultz. GMRES: A generalized minimal residual algorithm for solving
nonsymmetric linear systems. SIAM J. Sci. Stat. Comput., 7:856–869, 1986.
[19] P. Schavemaker and L. van der Sluis. Electrical Power System Essentials. John Wiley &
Sons, Chichester, 2008.
[20] G. L. G. Sleijpen, H. A. van der Vorst, and D. R. Fokkema. BiCGstab(ℓ) and other hybrid
Bi-CG methods. Numerical Algorithms, 7:75–109, 1994.
[21] P. Sonneveld and M. B. van Gijzen. IDR(s): A family of simple and fast algorithms for
solving large nonsymmetric systems of linear equations. SIAM J. Sci. Comput., 31(2):1035–
1062, 2008.
[22] B. Stott and O. Alsac. Fast decoupled load flow. IEEE Transactions on Power Apparatus
and Systems, PAS-93(3):859–869, 1974.
[23] W. F. Tinney and C. E. Hart. Power flow solution by Newton’s method. IEEE Transactions
on Power Apparatus and Systems, PAS-86(11):1449–1449, 1967.
[24] W. F. Tinney and J. W. Walker. Direct solutions of sparse network equations by optimally
ordered triangular factorization. Proceedings of the IEEE, 55(11):1801–1809, 1967.
[25] U. Trottenberg, C. W. Oosterlee, and A. Schüller. Multigrid. Academic Press, 2001.
[26] R. A. M. van Amerongen. A general-purpose version of the fast decoupled loadflow. IEEE
Transactions on Power Systems, 4(2):760–770, 1989.
[27] H. A. van der Vorst. Bi-CGSTAB: a fast and smoothly converging variant of Bi-CG for
solution of nonsymmetric linear systems. SIAM J. Sci. Stat. Comput., 13:631–644, 1992.
[28] M. Yannakakis. Computing the minimum fill-in is NP-complete. SIAM J. Alg. Disc. Meth.,
2(1):77–79, March 1981.
[29] Y.-S. Zhang and H.-D. Chiang. Fast Newton-FGMRES solver for large-scale power flow study.
IEEE Transactions on Power Systems, 25(2):769–776, May 2010.
Was this manual useful for you? yes no
Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Download PDF