Economic Optimization of Hybrid Energy Storage Capacity for Wind Power Based on Coordinated SGMD and PSO
Under the dual carbon objectives, wind power penetration has accelerated markedly. However, the inherent volatility and insufficient peak regulation capability in energy storage allocation hamper efficient grid integration. To address these challenges, this paper presents a hybrid storage capacity c...
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| Language: | English |
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2025-05-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/10/2417 |
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| author | Kai Qi Keqilao Meng Xiangdong Meng Fengwei Zhao Yuefei Lü |
| author_facet | Kai Qi Keqilao Meng Xiangdong Meng Fengwei Zhao Yuefei Lü |
| author_sort | Kai Qi |
| collection | DOAJ |
| description | Under the dual carbon objectives, wind power penetration has accelerated markedly. However, the inherent volatility and insufficient peak regulation capability in energy storage allocation hamper efficient grid integration. To address these challenges, this paper presents a hybrid storage capacity configuration method that combines Symplectic Geometry Mode Decomposition (SGMD) with Particle Swarm Optimization (PSO). SGMD provides fine-grained, multi-scale decomposition of load–power curves to reduce modal aliasing, while PSO determines globally optimal ESS capacities under peak-shaving constraints. Case-study simulations showed a 25.86% reduction in the storage investment cost compared to EMD-based baselines, maintenance of the state of charge (SOC) within 0.3–0.6, and significantly enhanced overall energy management efficiency. The proposed framework thus offers a cost-effective and robust solution for energy storage at renewable energy plants. |
| format | Article |
| id | doaj-art-cdae627dc8a1471d86bdf96458da5ffb |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Energies |
| spelling | doaj-art-cdae627dc8a1471d86bdf96458da5ffb2025-08-20T02:33:52ZengMDPI AGEnergies1996-10732025-05-011810241710.3390/en18102417Economic Optimization of Hybrid Energy Storage Capacity for Wind Power Based on Coordinated SGMD and PSOKai Qi0Keqilao Meng1Xiangdong Meng2Fengwei Zhao3Yuefei Lü4School of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot 010051, ChinaSchool of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot 010051, ChinaHuaneng Wulatezhongqi New Energy Power Generation Co., Ltd., Bayannur 015200, ChinaHuaneng Wulatezhongqi New Energy Power Generation Co., Ltd., Bayannur 015200, ChinaHuaneng Wulatezhongqi New Energy Power Generation Co., Ltd., Bayannur 015200, ChinaUnder the dual carbon objectives, wind power penetration has accelerated markedly. However, the inherent volatility and insufficient peak regulation capability in energy storage allocation hamper efficient grid integration. To address these challenges, this paper presents a hybrid storage capacity configuration method that combines Symplectic Geometry Mode Decomposition (SGMD) with Particle Swarm Optimization (PSO). SGMD provides fine-grained, multi-scale decomposition of load–power curves to reduce modal aliasing, while PSO determines globally optimal ESS capacities under peak-shaving constraints. Case-study simulations showed a 25.86% reduction in the storage investment cost compared to EMD-based baselines, maintenance of the state of charge (SOC) within 0.3–0.6, and significantly enhanced overall energy management efficiency. The proposed framework thus offers a cost-effective and robust solution for energy storage at renewable energy plants.https://www.mdpi.com/1996-1073/18/10/2417peak regulation capabilitysymplectic geometric mode decompositionhybrid energy storage capacity configurationparticle swarm optimization |
| spellingShingle | Kai Qi Keqilao Meng Xiangdong Meng Fengwei Zhao Yuefei Lü Economic Optimization of Hybrid Energy Storage Capacity for Wind Power Based on Coordinated SGMD and PSO Energies peak regulation capability symplectic geometric mode decomposition hybrid energy storage capacity configuration particle swarm optimization |
| title | Economic Optimization of Hybrid Energy Storage Capacity for Wind Power Based on Coordinated SGMD and PSO |
| title_full | Economic Optimization of Hybrid Energy Storage Capacity for Wind Power Based on Coordinated SGMD and PSO |
| title_fullStr | Economic Optimization of Hybrid Energy Storage Capacity for Wind Power Based on Coordinated SGMD and PSO |
| title_full_unstemmed | Economic Optimization of Hybrid Energy Storage Capacity for Wind Power Based on Coordinated SGMD and PSO |
| title_short | Economic Optimization of Hybrid Energy Storage Capacity for Wind Power Based on Coordinated SGMD and PSO |
| title_sort | economic optimization of hybrid energy storage capacity for wind power based on coordinated sgmd and pso |
| topic | peak regulation capability symplectic geometric mode decomposition hybrid energy storage capacity configuration particle swarm optimization |
| url | https://www.mdpi.com/1996-1073/18/10/2417 |
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