Applications of Differential Evolution in Power System Optimization

Modern power systems are very large, complex and widely distributed. Scarcity in energy resources, increasing power generation cost and ever-growing demand for electric energy necessitates optimal operation of power systems. Even a small reduction in production cost may lead to a large savings. Hence efficient algorithms for solving the power system scheduling are needed. New optimization methods based on evolutionary computation that abstract the principle of natural selection and genetics are employed for scheduling problems. They are easy to implement and have the capability to converge to global optimum at a relatively lesser computational effort. Differential Evolution (DE), a numerical optimization approach is simple, easy to implement, significantly faster and robust. It has been verified as a promising candidate for solving real-valued engineering optimization problems. This chapter is concerned with the applications of differential evolution and its variants for various power system scheduling problems like economic dispatch, dynamic economic dispatch and unit commitment. Different case studies have been conducted including nonlinearities such as the valve-point effects, prohibited operating zones and transmission losses. This chapter enumerates the advantages of differential evolution to determine the most economic conditions of the electric power system.

[1]  Eiichi Tanaka,et al.  An Evolutionary Programming Solution to the Unit Commitment Problem , 1997 .

[2]  L. Lakshminarasimman,et al.  Short-term scheduling of hydrothermal power system with cascaded reservoirs by using modified differential evolution , 2006 .

[3]  Ching-Tzong Su,et al.  Network reconfiguration of distribution systems using improved mixed-integer hybrid differential evolution , 2002 .

[4]  Malabika Basu,et al.  Simulated Annealing Technique for Dynamic Economic Dispatch , 2006 .

[5]  A. Bakirtzis,et al.  A solution to the unit-commitment problem using integer-coded genetic algorithm , 2004, IEEE Transactions on Power Systems.

[6]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[7]  L. Lakshminarasimman,et al.  Hydrothermal coordination using modified mixed integer hybrid differential evolution , 2007 .

[8]  Ji-Pyng Chiou,et al.  Ant direction hybrid differential evolution for solving large capacitor placement problems , 2004 .

[9]  Anastasios G. Bakirtzis,et al.  A genetic algorithm solution to the unit commitment problem , 1996 .

[10]  Allen J. Wood,et al.  Power Generation, Operation, and Control , 1984 .

[11]  Hoay Beng Gooi,et al.  Dynamic Economic Dispatch: Feasible and Optimal Solutions , 2001 .

[12]  Zwe-Lee Gaing,et al.  Particle swarm optimization to solving the economic dispatch considering the generator constraints , 2003 .

[13]  Chuanwen Jiang,et al.  A matrix real-coded genetic algorithm to the unit commitment problem , 2006 .

[14]  S. Kannan,et al.  Application and comparison of metaheuristic techniques to generation expansion planning problem , 2005, IEEE Transactions on Power Systems.