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Solving Optimal Power Flow Problem via Improved Constrained Adaptive Differential Evolution

Author

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  • Wenchao Yi

    (College of Energy Engineering, Zhejiang University, Hangzhou 310027, China
    College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Zhilei Lin

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Youbin Lin

    (Zhejiang Chuangxin Automative Air Conditioning Company, Lishui 323799, China)

  • Shusheng Xiong

    (College of Energy Engineering, Zhejiang University, Hangzhou 310027, China
    Longquan Industrial Innovation Research Institute, Longquan 323700, China)

  • Zitao Yu

    (College of Energy Engineering, Zhejiang University, Hangzhou 310027, China)

  • Yong Chen

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

Abstract

The optimal power flow problem is one of the most widely used problems in power system optimizations, which are multi-modal, non-linear, and constrained optimization problems. Effective constrained optimization methods can be considered in tackling the optimal power flow problems. In this paper, an ϵ -constrained method-based adaptive differential evolution is proposed to solve the optimal power flow problems. The ϵ -constrained method is improved to tackle the constraints, and a p -best selection method based on the constraint violation is implemented in the adaptive differential evolution. The single and multi-objective optimal power flow problems on the IEEE 30-bus test system are used to verify the effectiveness of the proposed and improved ε adaptive differential evolution algorithm. The comparison between state-of-the-art algorithms illustrate the effectiveness of the proposed and improved ε adaptive differential evolution algorithm. The proposed algorithm demonstrates improvements in nine out of ten cases.

Suggested Citation

  • Wenchao Yi & Zhilei Lin & Youbin Lin & Shusheng Xiong & Zitao Yu & Yong Chen, 2023. "Solving Optimal Power Flow Problem via Improved Constrained Adaptive Differential Evolution," Mathematics, MDPI, vol. 11(5), pages 1-13, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:5:p:1250-:d:1087900
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    References listed on IDEAS

    as
    1. Thang Trung Nguyen & Fazel Mohammadi, 2020. "Optimal Placement of TCSC for Congestion Management and Power Loss Reduction Using Multi-Objective Genetic Algorithm," Sustainability, MDPI, vol. 12(7), pages 1-15, April.
    2. Wenchao Yi & Liang Gao & Zhi Pei & Jiansha Lu & Yong Chen, 2021. "ε Constrained differential evolution using halfspace partition for optimization problems," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 157-178, January.
    3. Li, Shuijia & Gong, Wenyin & Hu, Chengyu & Yan, Xuesong & Wang, Ling & Gu, Qiong, 2021. "Adaptive constraint differential evolution for optimal power flow," Energy, Elsevier, vol. 235(C).
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    Cited by:

    1. Umair Hussan & Huaizhi Wang & Muhammad Ahsan Ayub & Hamna Rasheed & Muhammad Asghar Majeed & Jianchun Peng & Hui Jiang, 2024. "Decentralized Stochastic Recursive Gradient Method for Fully Decentralized OPF in Multi-Area Power Systems," Mathematics, MDPI, vol. 12(19), pages 1-16, September.

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