Energy Optimization Strategy for Wind–Solar–Storage Systems with a Storage Battery Configuration
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 genera...
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MDPI AG
2025-05-01
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| Series: | Mathematics |
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| author | Yufeng Wang Haining Ji Runteng Luo Bin Liu Yongzi Wu |
| author_facet | Yufeng Wang Haining Ji Runteng Luo Bin Liu Yongzi Wu |
| author_sort | Yufeng Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-33982bbc99df4b8890c1f03948d96c1e |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-33982bbc99df4b8890c1f03948d96c1e2025-08-20T02:23:00ZengMDPI AGMathematics2227-73902025-05-011311175510.3390/math13111755Energy Optimization Strategy for Wind–Solar–Storage Systems with a Storage Battery ConfigurationYufeng Wang0Haining Ji1Runteng Luo2Bin Liu3Yongzi Wu4School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, ChinaSchool of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, ChinaSchool of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, ChinaSchool of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, ChinaSchool of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, ChinaWith 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.https://www.mdpi.com/2227-7390/13/11/1755wind–solar energy storage microgrid systemenergy optimization strategyartificial fish swarm algorithmsimulated annealingjoint operation |
| spellingShingle | Yufeng Wang Haining Ji Runteng Luo Bin Liu Yongzi Wu Energy Optimization Strategy for Wind–Solar–Storage Systems with a Storage Battery Configuration Mathematics wind–solar energy storage microgrid system energy optimization strategy artificial fish swarm algorithm simulated annealing joint operation |
| title | Energy Optimization Strategy for Wind–Solar–Storage Systems with a Storage Battery Configuration |
| title_full | Energy Optimization Strategy for Wind–Solar–Storage Systems with a Storage Battery Configuration |
| title_fullStr | Energy Optimization Strategy for Wind–Solar–Storage Systems with a Storage Battery Configuration |
| title_full_unstemmed | Energy Optimization Strategy for Wind–Solar–Storage Systems with a Storage Battery Configuration |
| title_short | Energy Optimization Strategy for Wind–Solar–Storage Systems with a Storage Battery Configuration |
| title_sort | energy optimization strategy for wind solar storage systems with a storage battery configuration |
| topic | wind–solar energy storage microgrid system energy optimization strategy artificial fish swarm algorithm simulated annealing joint operation |
| url | https://www.mdpi.com/2227-7390/13/11/1755 |
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