Energy Optimization Strategy for Wind–Solar–Storage Systems with a Storage Battery Configuration

Yufeng Wang, Haining Ji (), Runteng Luo, Bin Liu and Yongzi Wu
Additional contact information
Yufeng Wang: School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, China
Haining Ji: School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, China
Runteng Luo: School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, China
Bin Liu: School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, China
Yongzi Wu: School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, China

Mathematics, 2025, vol. 13, issue 11, 1-17

Abstract: With the progressive advancement of the energy transition strategy, wind–solar energy complementary power generation has emerged as a pivotal component in the global transition towards a sustainable, low-carbon energy future. To address the inherent challenges of intermittent renewable energy generation, this paper proposes a comprehensive energy optimization strategy that integrates coordinated wind–solar power dispatch with strategic battery storage capacity allocation. Through the development of a linear programming model for the wind–solar–storage hybrid system, incorporating critical operational constraints including load demand, an optimization solution was implemented using the Artificial Fish Swarm Algorithm (AFSA). This computational approach enabled the determination of an optimal scheme for the coordinated operation of wind, solar, and storage components within the integrated energy system. Based on the case study analysis, the AFSA optimization algorithm achieves a 1.07% reduction in total power generation costs compared to the traditional Simulated Annealing (SA) approach. Comparative analysis reveals that the integrated grid-connected operation mode exhibits superior economic performance over the standalone storage microgrid system. Additionally, we conducted a further analysis of the key factors contributing to the enhancement of economic benefits. The strategy proposed in this paper significantly enhances power supply stability, reduces overall costs and promotes the large-scale application of green energy.

Keywords: wind–solar energy storage microgrid system; energy optimization strategy; artificial fish swarm algorithm; simulated annealing; joint operation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/11/1755/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/11/1755/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:11:p:1755-:d:1664047

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().