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A novel optimisation method for voltage and reactive power control of electric power systems
Author(s)
Mataifa, Haltor
Date Issued
2023
Type
Thesis
Publisher
Cape Peninsula University of Technology
Abstract
Reliable electrical power supply is one of the most important utilities for modern society. This
can be seen by the fact that any prolonged interruption of electrical power supply usually
leads to enormous disruption of essential services and normal daily activities, and can in fact
threaten to cause a lot of damage or losses if not promptly remedied. Moreover, recent
developments in the power system, such as the deregulation and restructuring of the
electrical power supply industry, the introduction of competitive electricity and power
markets, and the rapid growth and expansion of distributed and decentralized electrical
power generation, have led to a significant increase in the complexity of modern power
systems, adding to the challenge of operating them reliably and efficiently. Thus, the need for
optimal strategies for the secure, economical and efficient operation of the power system is
arguably even greater now than at any other time in the history of the power system. In line
with this identified need, this thesis investigates the theoretical design, development, and
practical implementation of efficient algorithms that contribute to the secure, economical and
reliable operation of electric power transmission systems.
The focus of the research presented in this thesis is on the development of methods and
algorithms for the solution of the Volt/VAR optimization (VVO) problem, which is a very
important sub-problem of the optimal power flow (OPF) problem that is primarily concerned
with determination of the optimal coordinated dispatch of voltage-regulating devices and
reactive power sources, with voltage profile improvement and system power loss
minimization as the main objectives (among others). Volt/VAR optimization is one of the most
actively researched areas of power system operation. While most researchers consider
either classical or heuristic optimization methods in isolation, the research work presented in
this thesis investigates the design of efficient Volt/VAR optimization strategies considering
both classical and heuristic optimization techniques.
Two main optimization algorithms are developed for the solution of the Volt/VAR optimization
problem in this thesis. One is based on the primal-dual interior-point method (PDIPM), which
is one of the most efficient classical methods for large-scale nonlinear optimization. The
other is based on the particle swarm optimization algorithm, one of the most popular heuristic
optimization techniques. To enhance the efficiency of the developed algorithms, the model
development for the Volt/VAR optimization problem considers both the polar and rectangular
coordinate representations of the system voltages. Although most researchers make use of
the polar representation, analysis reveals that the rectangular representation has relatively
more favourable mathematical properties from the computational efficiency perspective,
particularly for the methods and algorithms developed in this thesis. The efficiency of the
developed methods and algorithms is further enhanced by incorporating the Newton-
Raphson load flow computation into the Volt/VAR optimization algorithm, which is moreover also developed using the rectangular model formulation. Five power system case studies, a
3-bus system, a 6-bus system, and the IEEE 14-bus, IEEE 30-bus, and IEEE 118-bus power
systems, are used to analyse the performance of the developed algorithms. The results
obtained from the performance analysis reveal that the developed algorithms exhibit high
computational efficiency and superior convergence characteristics. Moreover, a comparative
performance analysis is also conducted between the PDIPM-based VVO algorithm and the
PSO-based VVO algorithm. The performance analysis reveals that the primal-dual interiorpoint
method outperforms the particle swarm optimization algorithm in terms of
computational efficiency, since on average it requires fewer iterations to converge, and has a
shorter running time. The particle swarm optimization, on the other hand, generally achieves
a higher percentage real power loss reduction than the primal-dual interior-point method.
This suggests that the two classes of methods (i.e. classical and heuristic optimization
methods) have complementary performance characteristics, something which could be
exploited to devise optimization strategies that seek to combine their relative strengths, and
thus have a better prospect of exhibiting performance that is superior to that of the individual
algorithms.
The methods, algorithms and software programs developed and presented in this thesis are
of great relevance both to industry and to academia, and can serve as a good foundation for
further research and development, as suggested in the concluding chapter of the thesis.
can be seen by the fact that any prolonged interruption of electrical power supply usually
leads to enormous disruption of essential services and normal daily activities, and can in fact
threaten to cause a lot of damage or losses if not promptly remedied. Moreover, recent
developments in the power system, such as the deregulation and restructuring of the
electrical power supply industry, the introduction of competitive electricity and power
markets, and the rapid growth and expansion of distributed and decentralized electrical
power generation, have led to a significant increase in the complexity of modern power
systems, adding to the challenge of operating them reliably and efficiently. Thus, the need for
optimal strategies for the secure, economical and efficient operation of the power system is
arguably even greater now than at any other time in the history of the power system. In line
with this identified need, this thesis investigates the theoretical design, development, and
practical implementation of efficient algorithms that contribute to the secure, economical and
reliable operation of electric power transmission systems.
The focus of the research presented in this thesis is on the development of methods and
algorithms for the solution of the Volt/VAR optimization (VVO) problem, which is a very
important sub-problem of the optimal power flow (OPF) problem that is primarily concerned
with determination of the optimal coordinated dispatch of voltage-regulating devices and
reactive power sources, with voltage profile improvement and system power loss
minimization as the main objectives (among others). Volt/VAR optimization is one of the most
actively researched areas of power system operation. While most researchers consider
either classical or heuristic optimization methods in isolation, the research work presented in
this thesis investigates the design of efficient Volt/VAR optimization strategies considering
both classical and heuristic optimization techniques.
Two main optimization algorithms are developed for the solution of the Volt/VAR optimization
problem in this thesis. One is based on the primal-dual interior-point method (PDIPM), which
is one of the most efficient classical methods for large-scale nonlinear optimization. The
other is based on the particle swarm optimization algorithm, one of the most popular heuristic
optimization techniques. To enhance the efficiency of the developed algorithms, the model
development for the Volt/VAR optimization problem considers both the polar and rectangular
coordinate representations of the system voltages. Although most researchers make use of
the polar representation, analysis reveals that the rectangular representation has relatively
more favourable mathematical properties from the computational efficiency perspective,
particularly for the methods and algorithms developed in this thesis. The efficiency of the
developed methods and algorithms is further enhanced by incorporating the Newton-
Raphson load flow computation into the Volt/VAR optimization algorithm, which is moreover also developed using the rectangular model formulation. Five power system case studies, a
3-bus system, a 6-bus system, and the IEEE 14-bus, IEEE 30-bus, and IEEE 118-bus power
systems, are used to analyse the performance of the developed algorithms. The results
obtained from the performance analysis reveal that the developed algorithms exhibit high
computational efficiency and superior convergence characteristics. Moreover, a comparative
performance analysis is also conducted between the PDIPM-based VVO algorithm and the
PSO-based VVO algorithm. The performance analysis reveals that the primal-dual interiorpoint
method outperforms the particle swarm optimization algorithm in terms of
computational efficiency, since on average it requires fewer iterations to converge, and has a
shorter running time. The particle swarm optimization, on the other hand, generally achieves
a higher percentage real power loss reduction than the primal-dual interior-point method.
This suggests that the two classes of methods (i.e. classical and heuristic optimization
methods) have complementary performance characteristics, something which could be
exploited to devise optimization strategies that seek to combine their relative strengths, and
thus have a better prospect of exhibiting performance that is superior to that of the individual
algorithms.
The methods, algorithms and software programs developed and presented in this thesis are
of great relevance both to industry and to academia, and can serve as a good foundation for
further research and development, as suggested in the concluding chapter of the thesis.
Additional information
Thesis (DEng (Electrical Engineering))--Cape Peninsula University of Technology, 2023
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