Optimal Allocation of Energy Storage System Considering Multi-Correlated Wind Farms
With the increasing penetration of wind power, not only the uncertainties but also the correlation among the wind farms should be considered in the power system analysis. In this paper, Clayton-Copula method is developed to model the multiple correlated wind distribution and a new point estimation m...
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| Format: | Article |
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MDPI AG
2017-05-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/10/5/625 |
| _version_ | 1798040613301518336 |
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| author | Shuli Wen Hai Lan Qiang Fu David C. Yu Ying-Yi Hong Peng Cheng |
| author_facet | Shuli Wen Hai Lan Qiang Fu David C. Yu Ying-Yi Hong Peng Cheng |
| author_sort | Shuli Wen |
| collection | DOAJ |
| description | With the increasing penetration of wind power, not only the uncertainties but also the correlation among the wind farms should be considered in the power system analysis. In this paper, Clayton-Copula method is developed to model the multiple correlated wind distribution and a new point estimation method (PEM) is proposed to discretize the multi-correlated wind distribution. Furthermore, combining the proposed modeling and discretizing method with Hybrid Multi-Objective Particle Swarm Optimization (HMOPSO), a comprehensive algorithm is explored to minimize the power system cost and the emissions by searching the best placements and sizes of energy storage system (ESS) considering wind power uncertainties in multi-correlated wind farms. In addition, the variations of load are also taken into account. The IEEE 57-bus system is adopted to perform case studies using the proposed approach. The results clearly demonstrate the effectiveness of the proposed algorithm in determining the optimal storage allocations considering multi-correlated wind farms. |
| first_indexed | 2024-04-11T22:10:05Z |
| format | Article |
| id | doaj.art-70b9b931d0f7498c8fb20e15207d1f03 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T22:10:05Z |
| publishDate | 2017-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-70b9b931d0f7498c8fb20e15207d1f032022-12-22T04:00:35ZengMDPI AGEnergies1996-10732017-05-0110562510.3390/en10050625en10050625Optimal Allocation of Energy Storage System Considering Multi-Correlated Wind FarmsShuli Wen0Hai Lan1Qiang Fu2David C. Yu3Ying-Yi Hong4Peng Cheng5College of Automation, Harbin Engineering University, Harbin 150001, ChinaCollege of Automation, Harbin Engineering University, Harbin 150001, ChinaDepartment of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USADepartment of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USADepartment of Electrical Engineering, Chung Yuan Christian University, Chung Li District, Taoyuan City 32023, TaiwanCollege of Automation, Harbin Engineering University, Harbin 150001, ChinaWith the increasing penetration of wind power, not only the uncertainties but also the correlation among the wind farms should be considered in the power system analysis. In this paper, Clayton-Copula method is developed to model the multiple correlated wind distribution and a new point estimation method (PEM) is proposed to discretize the multi-correlated wind distribution. Furthermore, combining the proposed modeling and discretizing method with Hybrid Multi-Objective Particle Swarm Optimization (HMOPSO), a comprehensive algorithm is explored to minimize the power system cost and the emissions by searching the best placements and sizes of energy storage system (ESS) considering wind power uncertainties in multi-correlated wind farms. In addition, the variations of load are also taken into account. The IEEE 57-bus system is adopted to perform case studies using the proposed approach. The results clearly demonstrate the effectiveness of the proposed algorithm in determining the optimal storage allocations considering multi-correlated wind farms.http://www.mdpi.com/1996-1073/10/5/625multi-correlated wind distributionClayton-Copula methodpoint estimation method (PEM)energy storage system (ESS)multi-objective particle swarm optimization (MOPSO) |
| spellingShingle | Shuli Wen Hai Lan Qiang Fu David C. Yu Ying-Yi Hong Peng Cheng Optimal Allocation of Energy Storage System Considering Multi-Correlated Wind Farms Energies multi-correlated wind distribution Clayton-Copula method point estimation method (PEM) energy storage system (ESS) multi-objective particle swarm optimization (MOPSO) |
| title | Optimal Allocation of Energy Storage System Considering Multi-Correlated Wind Farms |
| title_full | Optimal Allocation of Energy Storage System Considering Multi-Correlated Wind Farms |
| title_fullStr | Optimal Allocation of Energy Storage System Considering Multi-Correlated Wind Farms |
| title_full_unstemmed | Optimal Allocation of Energy Storage System Considering Multi-Correlated Wind Farms |
| title_short | Optimal Allocation of Energy Storage System Considering Multi-Correlated Wind Farms |
| title_sort | optimal allocation of energy storage system considering multi correlated wind farms |
| topic | multi-correlated wind distribution Clayton-Copula method point estimation method (PEM) energy storage system (ESS) multi-objective particle swarm optimization (MOPSO) |
| url | http://www.mdpi.com/1996-1073/10/5/625 |
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