A hybrid differential evolution algorithm based on particle swarm optimization for nonconvex economic dispatch problems

This paper presents the design and application of an efficient hybrid heuristic search method to solve the practical economic dispatch problem considering many nonlinear characteristics of power generators, and their operational constraints, such as transmission losses, valve-point effects, multi-fuel options, prohibited operating zones, ramp rate limits and spinning reserve. These practical operation constraints which can usually be found at the same time in realistic power system operations make the economic load dispatch problem a nonsmooth optimization problem having complex and nonconvex features with heavy equality and inequality constraints.The proposed approach combines in the most effective way the properties of two of the most popular evolutionary optimization techniques now in use for power system optimization, the Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms. To improve the global optimization property of DE, the PSO procedure is integrated as additional mutation operator.The effectiveness of the proposed algorithm (termed DEPSO) is demonstrated by solving four kinds of ELD problems with nonsmooth and nonconvex solution spaces. The comparative results with some of the most recently published methods confirm the effectiveness of the proposed strategy to find accurate and feasible optimal solutions for practical ELD problems.

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► We propose a more realistic model of the economic load dispatch problem to obtain an accurate and practical solution.
► We have considered: valve-point effects, multi-fuel options, prohibited zones, ramp rate limits and spinning reserve.
► A new efficient hybrid DE-PSO method is proposed to solve the resulting complex optimization problem.
► The basic idea is to incorporate the PSO as a supplementary mutation operator into the conventional DE algorithm.
► Simulation results confirm the effectiveness of the proposed strategy to find accurate and feasible optimal solutions.