Optimization study of wind, solar, hydro and hydrogen storage based on improved multi-objective particle swarm optimization

Accelerating the construction of a new energy system, vigorously advancing the development of renewable energy, and establishing a new complementary electricity system is one of the important measures for green transformation, playing a crucial role in the future energy development. Consequently, th...

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Bibliographic Details
Published in:Journal of energy storage Vol. 93; p. 112298
Main Authors: Wang, Jixuan, Wen, Yujing, Wu, Kegui, Ding, Shuai, Liu, Yang, Tian, Hao, Zhang, Jihua, Wang, Liying, Cao, Qingjiao, Zhang, Yunxin
Format: Journal Article
Language:English
Published: Elsevier Ltd 15.07.2024
Subjects:
Carbon footprint
IMOPSO algorithm
Optimization study
Wind-solar-hydrogen storage
Wind-solar-hydrogen storage
IMOPSO algorithm
Optimization study
Carbon footprint
ISSN:2352-152X, 2352-1538
Online Access:Get full text
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Summary:Accelerating the construction of a new energy system, vigorously advancing the development of renewable energy, and establishing a new complementary electricity system is one of the important measures for green transformation, playing a crucial role in the future energy development. Consequently, this article, targeting the current status of multi-energy complementarity, establishes a complementary system of pumped hydro storage, battery storage, and hydrogen storage, and formulates an optimization model for a wind-solar‑hydrogen storage system to facilitate the integration of wind and solar power. The article improves the multi-objective particle swarm optimization by incorporating learning factors with asynchronous changes and an exponential nonlinear decreasing inertia weight. It aims to minimize the system's total cost and environmental protection cost, using the IMOPSO algorithm to optimize the model. The results indicate that the total cost is 8262 CNY/(kW·h), and the environmental protection cost is 541.9 CNY/(kW·h). The integration rates of wind and solar power are 64.37 % and 77.25 %, respectively, which represent an increase of 30.71 % and 25.98 % over the MOPSO algorithm. The system's total clean energy supply reaches 94.1 %, offering a novel approach for the storage and utilization of clean energy. •Improved multi-objective particle swarm algorithm with varying learning factors and nonlinear decreasing inertia weight;•An optimization model for a wind-solar-hydrogen storage system is constructed;•The model is refined using the IMOPSO algorithm to minimize both the overall system costs and the costs associated with environmental protection.
ISSN:2352-152X
2352-1538
DOI:10.1016/j.est.2024.112298