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

Truong H. Khoa, Pandian M. Vasant, Balbir Singh Mahinder Singh and V. N. Dieu
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Truong H. Khoa: Department of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia
Pandian M. Vasant: Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam & Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS 32610 Seri Iskandar, Perak, Malaysia
Balbir Singh Mahinder Singh: Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia
V. N. Dieu: Department of Power Systems, HCMC University of Technology, Ho Chi Minh City, Vietnam

International Journal of Swarm Intelligence Research (IJSIR), 2017, vol. 8, issue 4, 34-59

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.

Date: 2017
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