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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Truong H. Khoa & Pandian M. Vasant & Balbir Singh Mahinder Singh & V. N. Dieu, 2017. "Hybrid Mean-Variance Mapping Optimization for Non-Convex Economic Dispatch Problems," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 8(4), pages 34-59, October.
    • Handle: RePEc:igg:jsir00:v:8:y:2017:i:4:p:34-59
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    Cited by:

    1. Shahenda Sarhan & Abdullah M. Shaheen & Ragab A. El-Sehiemy & Mona Gafar, 2022. "Enhanced Teaching Learning-Based Algorithm for Fuel Costs and Losses Minimization in AC-DC Systems," Mathematics, MDPI, vol. 10(13), pages 1-22, July.
    2. Guojiang Xiong & Jing Zhang & Xufeng Yuan & Dongyuan Shi & Yu He & Yao Yao & Gonggui Chen, 2018. "A Novel Method for Economic Dispatch with Across Neighborhood Search: A Case Study in a Provincial Power Grid, China," Complexity, Hindawi, vol. 2018, pages 1-18, November.
    3. Li Yan & Zhengyu Zhu & Xiaopeng Kang & Boyang Qu & Baihao Qiao & Jiajia Huan & Xuzhao Chai, 2022. "Multi-Objective Dynamic Economic Emission Dispatch with Electric Vehicle–Wind Power Interaction Based on a Self-Adaptive Multiple-Learning Harmony-Search Algorithm," Energies, MDPI, vol. 15(14), pages 1-22, July.
    4. Boyang Qu & Baihao Qiao & Yongsheng Zhu & Jingjing Liang & Ling Wang, 2017. "Dynamic Power Dispatch Considering Electric Vehicles and Wind Power Using Decomposition Based Multi-Objective Evolutionary Algorithm," Energies, MDPI, vol. 10(12), pages 1-28, December.

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