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Enhanced method for considering energy storage systems as ancillary service resources in stochastic unit commitment

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  • Kwon, Kyung-bin
  • Kim, Dam

Abstract

As renewable energy has become more viable, interest in how uncertainties in power systems are handled has increased. To handle increased uncertainties, system operators utilize the spinning reserve. Here, we present a method for using energy storage systems (ESS) as ancillary service resources, especially as a spinning reserve. The dominant feature of ESS is energy-constrained; thus, the method applied to general generators is necessary to be enhanced in order to utilize ESS as a spinning reserve resource. We propose a method for redefining the ESS reserve margin as the discrepancy between the charging and discharging capacities considering a reference scenario and other scenarios in the stochastic optimization of unit commitment. The various scenarios that are compared to the reference scenario are generated by considering forecast uncertainties of load and renewable output; then, the appropriate number of scenarios are selected by applying the scenario reduction technique. To validate the proposed method, we performed case studies on the IEEE 24-bus and 118-bus test systems. The proposed method will help reduce the operation costs by 4.13% in a case of the IEEE 24-bus; furthermore, improvements in load shedding and renewable curtailment have been identified through diverse cases.

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  • Kwon, Kyung-bin & Kim, Dam, 2020. "Enhanced method for considering energy storage systems as ancillary service resources in stochastic unit commitment," Energy, Elsevier, vol. 213(C).
    • Handle: RePEc:eee:energy:v:213:y:2020:i:c:s0360544220317837
    DOI: 10.1016/j.energy.2020.118675
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    References listed on IDEAS

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    Cited by:

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    2. Mehrdad Tahmasebi & Jagadeesh Pasupuleti & Fatemeh Mohamadian & Mohammad Shakeri & Josep M. Guerrero & M. Reyasudin Basir Khan & Muhammad Shahzad Nazir & Amir Safari & Najmeh Bazmohammadi, 2021. "Optimal Operation of Stand-Alone Microgrid Considering Emission Issues and Demand Response Program Using Whale Optimization Algorithm," Sustainability, MDPI, vol. 13(14), pages 1-22, July.
    3. Nikpour, Ahmad & Nateghi, Abolfazl & Shafie-khah, Miadreza & Catalão, João P.S., 2021. "Day-ahead optimal bidding of microgrids considering uncertainties of price and renewable energy resources," Energy, Elsevier, vol. 227(C).
    4. Dong, Jizhe & Han, Shunjie & Shao, Xiangxin & Tang, Like & Chen, Renhui & Wu, Longfei & Zheng, Cunlong & Li, Zonghao & Li, Haolin, 2021. "Day-ahead wind-thermal unit commitment considering historical virtual wind power data," Energy, Elsevier, vol. 235(C).
    5. Zhang, Mingze & Li, Weidong & Yu, Samson Shenglong & Wen, Kerui & Muyeen, S.M., 2023. "Day-ahead optimization dispatch strategy for large-scale battery energy storage considering multiple regulation and prediction failures," Energy, Elsevier, vol. 270(C).
    6. Koltsaklis, Nikolaos E. & Knápek, Jaroslav, 2023. "Assessing flexibility options in electricity market clearing," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    7. Kuttner, Leopold, 2022. "Integrated scheduling and bidding of power and reserve of energy resource aggregators with storage plants," Applied Energy, Elsevier, vol. 321(C).

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