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

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

Truong H. Khoa, Pandian M. Vasant, Balbir Singh Mahinder Singh, V. N. Dieu
Copyright: © 2017 |Pages: 26
DOI: 10.4018/IJSIR.2017100103
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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.
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Introduction

The thermal generating units of an electric power system utilize coal, oil and natural gas to produce energy supply to required system load demand. The fossil fuel is facing depletion and conservation is used as a way to increase energy efficiency. Hence, the generation of the power plants needs to be optimized at lowest possible fuel cost via economic dispatch (ED). The objective of ED is to determine the optimal power output of generation facilities results in minimum fuel generation cost while satisfying all units, as well as system constraints (Dieu, Schegner, & Ongsakul, 2013).

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