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Multiple Power Supply Capacity Planning Research for New Power System Based on Situation Awareness

Author

Listed:
  • Dahu Li

    (Hubei Collaborative Innovation Center for High-Efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan 430068, China
    State Grid Hubei Electric Power Co., Ltd., Wuhan 430077, China)

  • Xiaoda Cheng

    (Hubei Collaborative Innovation Center for High-Efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan 430068, China)

  • Leijiao Ge

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China)

  • Wentao Huang

    (Hubei Collaborative Innovation Center for High-Efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan 430068, China)

  • Jun He

    (Hubei Collaborative Innovation Center for High-Efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan 430068, China)

  • Zhongwei He

    (Enshi Power Supply Company, State Grid Hubei Electric Power Co., Ltd., Enshi 445699, China)

Abstract

In the context of new power systems, reasonable capacity optimization of multiple power systems can not only reduce carbon emissions, but also improve system safety and stability. This paper proposes a situation awareness-based capacity optimization strategy for wind-photovoltaic-thermal power systems and establishes a bi-level model for system capacity optimization. The upper-level model considers environmental protection and economy, and carries out multi-objective optimization of the system capacity planning solution with the objectives of minimizing carbon emissions and total system cost over the whole life cycle of the system, further obtaining a set of capacity planning solutions based on the Pareto frontier. A Pareto optimal solution set decision method based on grey relativity analysis is proposed to quantitatively assess the comprehensive economic–environmental properties of the system. The capacity planning solutions obtained from the upper model are used as the input to the lower model. The lower model integrates system stability, environmental protection, and economy and further optimizes the set of capacity planning solutions obtained from the upper model with the objective of maximizing the inertia security region and the best comprehensive economic–environmental properties to obtain the optimal capacity planning scheme. The NSGA-II modified algorithm (improved NSGA-II algorithm based on dominant strength, INSGA2-DS) is used to solve the upper model, and the Cplex solver is called on to solve the lower model. Finally, the modified IEEE-39 node algorithm is used to verify that the optimized capacity planning scheme can effectively improve the system security and stability and reduce the carbon emissions and total system cost throughout the system life cycle.

Suggested Citation

  • Dahu Li & Xiaoda Cheng & Leijiao Ge & Wentao Huang & Jun He & Zhongwei He, 2022. "Multiple Power Supply Capacity Planning Research for New Power System Based on Situation Awareness," Energies, MDPI, vol. 15(9), pages 1-24, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3298-:d:806746
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    References listed on IDEAS

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    Cited by:

    1. Leijiao Ge & Jun Yan & Yonghui Sun & Zhongguan Wang, 2022. "Situational Awareness for Smart Distribution Systems," Energies, MDPI, vol. 15(11), pages 1-3, June.
    2. Weiqiang Qiu & Sheng Zhou & Yang Yang & Xiaoying Lv & Ting Lv & Yuge Chen & Ying Huang & Kunming Zhang & Hongfei Yu & Yunchu Wang & Yuanqian Ma & Zhenzhi Lin, 2023. "Application Prospect, Development Status and Key Technologies of Shared Energy Storage toward Renewable Energy Accommodation Scenario in the Context of China," Energies, MDPI, vol. 16(2), pages 1-21, January.
    3. Xuejun Li & Jiaxin Qian & Changhai Yang & Boyang Chen & Xiang Wang & Zongnan Jiang, 2024. "New Power System Planning and Evolution Path with Multi-Flexibility Resource Coordination," Energies, MDPI, vol. 17(1), pages 1-20, January.

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