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

Sile Hu, Yuan Gao, Yuan Wang, Yuan Yu, Yue Bi, Linfeng Cao, Muhammad Farhan Khan and Jiaqiang Yang ()
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Sile Hu: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, 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

Energies, 2024, vol. 17, issue 5, 1-14

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.

Keywords: wind–solar–thermal storage; power energy base; average complementarity index; dynamic inertia weight chaotic particle swarm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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