JOURNAL ARTICLE

Hybrid Mean-Variance Mapping Optimization for Non-Convex Economic Dispatch Problems

Khoa Hoang TruongPandian VasantBalbir Singh Mahinder SinghDieu Ngoc Vo

Year: 2017 Journal:   International Journal of Swarm Intelligence Research Vol: 8 (4)Pages: 34-59   Publisher: IGI Global

Abstract

The economic dispatch (ED) is one of the important optimization problems in power system generation for fuel cost saving. This paper proposes a hybrid variant of mean-variance mapping optimization (MVMO-SH) for solving such problem considering the non-convex objective functions. The new proposed method is a hybrid variant of the original mean-variance mapping optimization algorithm (MVMO) with the embedded local search and multi-parent crossover to enhance its global search ability and improve solution quality for optimization problems. The proposed MVMO-SH is tested on different non-convex ED problem including valve point effects, multiple fuels and prohibited operating zones characteristics. The result comparisons from the proposed method with other methods in the literature have indicated that the proposed method is more robust and provides better solution quality than the others. Therefore, the proposed MVMO-SH is a promising method for solving the complex ED problems in power systems.

Keywords:
Mathematical optimization Crossover Variance (accounting) Computer science Economic dispatch Optimization problem Regular polygon Power (physics) Electric power system Mathematics Artificial intelligence

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Topics

Electric Power System Optimization
Physical Sciences →  Engineering →  Electrical and Electronic Engineering
Optimal Power Flow Distribution
Physical Sciences →  Engineering →  Electrical and Electronic Engineering
Energy Load and Power Forecasting
Physical Sciences →  Engineering →  Electrical and Electronic Engineering
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