Transient stability-constrained optimal power flow using improved differential evolution and parallel computing

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Transient stability-constrained optimal power flow using improved differential evolution and parallel computing


Author: Cai, Huarong
Title: Transient stability-constrained optimal power flow using improved differential evolution and parallel computing
Degree: Ph.D.
Year: 2008
Subject: Hong Kong Polytechnic University -- Dissertations.
Electric power system stability.
Electric power systems.
Department: Dept. of Electrical Engineering
Pages: x, 148 p. : ill. ; 31 cm.
Language: English
InnoPac Record:
Abstract: Optimal Power Flow (OFF) is one of the most important tools in power system planning, operation and control. Its purpose is to determine the power system controls to find the delicate balance between economy and security. Due to the rapid increase of electricity demand and the deregulation of electricity markets, power systems tend to operate closer to stability boundaries and, as a consequence, resulting in serious damage to national economics and security. Thus, consideration of the transient stability limits in the OFF problem of power systems is becoming more and more imperative. It is, however, an open question as how to include the stability constraints into OFF since transient stability is a dynamic concept and differential equations are involved. Some conventional optimization methods such as Interior Point Method (IPM) have been attempted to incorporate the transient stability constraints into OFF mainly by approximating the differential equations to algebraic equations. However, conventional mathematical optimization methods are sensitive to the starting points and have convergence difficulties in handling nonlinear, non-convex problems. Besides, the discretizing scheme will lead to computational inaccuracy in solving the problem; and discrete control variable handling such as transformer tap settings is another problem for the conventional methods. To address the above mentioned problems, this thesis is devoted to the development of an alternative approach in dealing with the OFF problem based on a new evolutionary algorithm Differential Evolution (DE). Each individual in the DE population is a candidate solution for the OFF problem. In particular, an improved version of DE with population re-initialization scheme called PJDE is reported to ameliorate the premature problem of DE. Simulations on IEEE 14-, 30-, and 118-bus systems show the powerful ability of PJDE in seeking the global optimal solution. As for transient stability constraints, a hybrid method which combines time domain simulation and transient energy function is employed to assess the transient stability of each individual with no limitation in system modeling. Stable individual has more chance to survive in the evolution process in seeking both secure and economic global solution. Since transient stability assessment is the most time-consuming part of the whole method, strategies called "stable-space push" and "fitness sorting" are also developed to reduce the searching space as well as the computation time. Besides the transient stability constraints, other non-convex and discontinuous practical constraints like generator valve-point effects, prohibited operation zones constraints that are difficult to handle by conventional methods are also considered into the OFF problem. The performance of the proposed algorithm has been tested on the WSCC 9-bus and New England 39-bus systems and compared with the reported results by conventional methods. The results show that the method developed in this thesis is very powerful in solving nonlinear, non-convex, discontinuous complex optimization problem with both continuous and discrete control variables. A parallel computation platform is also built in this thesis to speed up the proposed method. The parallel computation is implemented on a Beowulf PC-cluster using Message-Passing Interface (MPI) technology. Topologies like Master-Slave, Dual-Ring, and Hybrid Structure of the parallel computation are developed to optimize the computation. Case studies shows that parallelization does significantly improve the speed of DE; it is possible to realize online TSCOPF with moderate scale PC clusters and meet the real-world online application requirement. The method developed in this thesis is found to be effective and powerful in finding the global solution which is economic and secure for the power system.

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