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Optimal Allocation of Energy Storage System Considering Multi-Correlated Wind Farms

Author

Listed:
  • Shuli Wen

    (College of Automation, Harbin Engineering University, Harbin 150001, China)

  • Hai Lan

    (College of Automation, Harbin Engineering University, Harbin 150001, China)

  • Qiang Fu

    (Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA)

  • David C. Yu

    (Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA)

  • Ying-Yi Hong

    (Department of Electrical Engineering, Chung Yuan Christian University, Chung Li District, Taoyuan City 32023, Taiwan)

  • Peng Cheng

    (College of Automation, Harbin Engineering University, Harbin 150001, China)

Abstract

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.

Suggested Citation

  • Shuli Wen & Hai Lan & Qiang Fu & David C. Yu & Ying-Yi Hong & Peng Cheng, 2017. "Optimal Allocation of Energy Storage System Considering Multi-Correlated Wind Farms," Energies, MDPI, vol. 10(5), pages 1-16, May.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:5:p:625-:d:97551
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    References listed on IDEAS

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    1. Segers, Johan, 2015. "Hybrid copula estimators," LIDAM Reprints ISBA 2015005, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Jae-Kun Lyu & Jae-Haeng Heo & Jong-Keun Park & Yong-Cheol Kang, 2013. "Probabilistic Approach to Optimizing Active and Reactive Power Flow in Wind Farms Considering Wake Effects," Energies, MDPI, vol. 6(11), pages 1-21, October.
    3. Koirala, Krishna H. & Mishra, Ashok K. & D'Antoni, Jeremy M. & Mehlhorn, Joey E., 2015. "Energy prices and agricultural commodity prices: Testing correlation using copulas method," Energy, Elsevier, vol. 81(C), pages 430-436.
    4. Arjmand, Reza & Rahimiyan, Morteza, 2016. "Statistical analysis of a competitive day-ahead market coupled with correlated wind production and electric load," Applied Energy, Elsevier, vol. 161(C), pages 153-167.
    5. Moghaddam, Amjad Anvari & Seifi, Alireza & Niknam, Taher, 2012. "Multi-operation management of a typical micro-grids using Particle Swarm Optimization: A comparative study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1268-1281.
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    Cited by:

    1. Ahmed Alzahrani & Hussain Alharthi & Muhammad Khalid, 2019. "Minimization of Power Losses through Optimal Battery Placement in a Distributed Network with High Penetration of Photovoltaics," Energies, MDPI, vol. 13(1), pages 1-16, December.

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