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Optimal Power Flow with Stochastic Solar Power Using Clustering-Based Multi-Objective Differential Evolution

Author

Listed:
  • Derong Lv

    (College of Electrical Engineering, Guizhou University, Guiyang 550025, China)

  • Guojiang Xiong

    (College of Electrical Engineering, Guizhou University, Guiyang 550025, China
    Guizhou University Institute of Engineering Investigation & Design Co., Ltd., Guiyang 550025, China)

  • Xiaofan Fu

    (College of Electrical Engineering, Guizhou University, Guiyang 550025, China)

  • Yang Wu

    (Guizhou Electric Power Grid Dispatching and Control Center, Guiyang 550002, China)

  • Sheng Xu

    (Guizhou Electric Power Grid Dispatching and Control Center, Guiyang 550002, China)

  • Hao Chen

    (Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System, Quanzhou 362216, China)

Abstract

Optimal power flow is one of the fundamental optimal operation problems for power systems. With the increasing scale of solar energy integrated into power systems, the uncertainty of solar power brings intractable challenges to the power system operation. The multi-objective optimal power flow (MOOPF) considering the solar energy becomes a hotspot issue. In this study, a MOOPF model considering the uncertainty of solar power is proposed. Both scenarios of overestimation and underestimation of solar power are modeled and penalized in the form of operating cost. In order to solve this multi-objective optimization model effectively, this study proposes a clustering-based multi-objective differential evolution (CMODE) which is based on the main features: (1) extending DE into multi-objective algorithm, (2) introducing the feasible solution priority technique to deal with different constraints, and (3) combining the feasible solution priority technique and the merged hierarchical clustering method to determine the optimal Pareto frontier. The simulation outcomes on two cases based on the IEEE 57-bus system verify the reliability and superiority of CMODE over other peer methods in addressing the MOOPF.

Suggested Citation

  • Derong Lv & Guojiang Xiong & Xiaofan Fu & Yang Wu & Sheng Xu & Hao Chen, 2022. "Optimal Power Flow with Stochastic Solar Power Using Clustering-Based Multi-Objective Differential Evolution," Energies, MDPI, vol. 15(24), pages 1-21, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9489-:d:1003365
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    References listed on IDEAS

    as
    1. Shi, Bin & Yan, Lie-Xiang & Wu, Wei, 2013. "Multi-objective optimization for combined heat and power economic dispatch with power transmission loss and emission reduction," Energy, Elsevier, vol. 56(C), pages 135-143.
    2. Xiaobing Yu & Xianrui Yu & Yiqun Lu & Jichuan Sheng, 2018. "Economic and Emission Dispatch Using Ensemble Multi-Objective Differential Evolution Algorithm," Sustainability, MDPI, vol. 10(2), pages 1-17, February.
    3. Theocharides, Spyros & Makrides, George & Livera, Andreas & Theristis, Marios & Kaimakis, Paris & Georghiou, George E., 2020. "Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing," Applied Energy, Elsevier, vol. 268(C).
    4. Xiong, Guojiang & Shuai, Maohang & Hu, Xiao, 2022. "Combined heat and power economic emission dispatch using improved bare-bone multi-objective particle swarm optimization," Energy, Elsevier, vol. 244(PB).
    5. Spyros Theocharides & Marios Theristis & George Makrides & Marios Kynigos & Chrysovalantis Spanias & George E. Georghiou, 2021. "Comparative Analysis of Machine Learning Models for Day-Ahead Photovoltaic Power Production Forecasting," Energies, MDPI, vol. 14(4), pages 1-22, February.
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