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Optimal Configuration of Wind–Solar–Thermal-Storage Power Energy Based on Dynamic Inertia Weight Chaotic Particle Swarm

Author

Listed:
  • Sile Hu

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
    Inner Mongolia Power (Group) Co., Ltd., Hohhot 010020, China)

  • Yuan Gao

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

  • Yuan Wang

    (Inner Mongolia Electric Power Economic and Technical Research Institute Branch, Inner Mongolia Electric Power (Group) Co., Ltd., Hohhot 010020, China)

  • Yuan Yu

    (Inner Mongolia Power (Group) Co., Ltd., Hohhot 010020, China)

  • Yue Bi

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

  • Linfeng Cao

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

  • Muhammad Farhan Khan

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

  • Jiaqiang Yang

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

Abstract

The proposed approach involves a method of joint optimization configuration for wind–solar–thermal-storage (WSTS) power energy bases utilizing a dynamic inertia weight chaotic particle swarm optimization (DIWCPSO) algorithm. The power generated from the combination of wind and solar energy is analyzed quantitatively by using the average complementarity index (ACI) to determine the optimal ratio of wind and solar installations. We constructed a multi-objective optimization configuration model for the WSTS power generation systems, considering the equivalent annual income and the optimal energy consumption level as objective functions of the system. We solved the model using the chaotic particle swarm optimization algorithm with linearly decreasing dynamic inertia weight. To validate the effectiveness of the proposed approach, we conducted a simulation using the 2030 power energy base planning data of a particular region in Inner Mongolia. The results demonstrate that the proposed method significantly improves the annual income, enhances the consumption of wind–solar energy, and boosts the power transmission capacity of the system.

Suggested Citation

  • Sile Hu & Yuan Gao & Yuan Wang & Yuan Yu & Yue Bi & Linfeng Cao & Muhammad Farhan Khan & Jiaqiang Yang, 2024. "Optimal Configuration of Wind–Solar–Thermal-Storage Power Energy Based on Dynamic Inertia Weight Chaotic Particle Swarm," Energies, MDPI, vol. 17(5), pages 1-14, February.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:5:p:989-:d:1342247
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    References listed on IDEAS

    as
    1. Jiawei Wu & Jinyu Xiao & Jinming Hou & Xunyan Lyu, 2023. "Development Potential Assessment for Wind and Photovoltaic Power Energy Resources in the Main Desert–Gobi–Wilderness Areas of China," Energies, MDPI, vol. 16(12), pages 1-22, June.
    2. Abdulaziz Almalaq & Tawfik Guesmi & Saleh Albadran, 2023. "A Hybrid Chaotic-Based Multiobjective Differential Evolution Technique for Economic Emission Dispatch Problem," Energies, MDPI, vol. 16(12), pages 1-34, June.
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