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Location and Capacity Optimization of Distributed Energy Storage System in Peak-Shaving

Author

Listed:
  • Ruiyang Jin

    (College of Engineering, Peking University, Beijing 100871, China)

  • Jie Song

    (College of Engineering, Peking University, Beijing 100871, China)

  • Jie Liu

    (Department of Electrical Engineering, Tsinghua University, Beijing 100084, China)

  • Wei Li

    (Inner Mongolia Power (Group) Co., Ltd., Hohhot 010020, China)

  • Chao Lu

    (Department of Electrical Engineering, Tsinghua University, Beijing 100084, China)

Abstract

The peak-valley characteristic of electrical load brings high cost in power supply coming from the adjustment of generation to maintain the balance between production and demand. Distributed energy storage system (DESS) technology can deal with the challenge very well. However, the number of devices for DESS is much larger than central energy storage system (CESS), which brings challenges for solving the problem of location selection and capacity allocation with large scale. We formulate the charging/discharging model of DESS and economic analysis. Then, we propose a simulation optimization method to determine the locations to equip with DESSs and the storage capacity of each location. The greedy algorithm with Monte Carlo simulation is applied to solve the location and capacity optimization problem of DESS over a large scale. Compared with the global optimal genetic algorithm, the case study conducted on the load data of a district in Beijing validates the efficiency and superiority of our method.

Suggested Citation

  • Ruiyang Jin & Jie Song & Jie Liu & Wei Li & Chao Lu, 2020. "Location and Capacity Optimization of Distributed Energy Storage System in Peak-Shaving," Energies, MDPI, vol. 13(3), pages 1-15, January.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:3:p:513-:d:311312
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    References listed on IDEAS

    as
    1. Li, Zhengshuo & Guo, Qinglai & Sun, Hongbin & Wang, Jianhui, 2015. "Storage-like devices in load leveling: Complementarity constraints and a new and exact relaxation method," Applied Energy, Elsevier, vol. 151(C), pages 13-22.
    2. Wilson Cesar Sant’Ana & Robson Bauwelz Gonzatti & Germano Lambert-Torres & Erik Leandro Bonaldi & Bruno Silva Torres & Pedro Andrade de Oliveira & Rondineli Rodrigues Pereira & Luiz Eduardo Borges-da-, 2019. "Development and 24 Hour Behavior Analysis of a Peak-Shaving Equipment with Battery Storage," Energies, MDPI, vol. 12(11), pages 1-22, May.
    3. Uddin, Moslem & Romlie, Mohd Fakhizan & Abdullah, Mohd Faris & Abd Halim, Syahirah & Abu Bakar, Ab Halim & Chia Kwang, Tan, 2018. "A review on peak load shaving strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3323-3332.
    4. Parra, David & Norman, Stuart A. & Walker, Gavin S. & Gillott, Mark, 2016. "Optimum community energy storage system for demand load shifting," Applied Energy, Elsevier, vol. 174(C), pages 130-143.
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    Cited by:

    1. Yuriy Bilan & Marcin Rabe & Katarzyna Widera, 2022. "Distributed Energy Resources: Operational Benefits," Energies, MDPI, vol. 15(23), pages 1-7, November.
    2. Lange, Christopher & Rueß, Alexandra & Nuß, Andreas & Öchsner, Richard & März, Martin, 2020. "Dimensioning battery energy storage systems for peak shaving based on a real-time control algorithm," Applied Energy, Elsevier, vol. 280(C).
    3. Yanfeng Liu & Yaxing Wang & Xi Luo, 2020. "Design and Operation Optimization of Distributed Solar Energy System Based on Dynamic Operation Strategy," Energies, MDPI, vol. 14(1), pages 1-26, December.
    4. Xiangming Wu & Chenguang Yang & Guang Han & Zisong Ye & Yinlong Hu, 2022. "Energy Loss Reduction for Distribution Networks with Energy Storage Systems via Loss Sensitive Factor Method," Energies, MDPI, vol. 15(15), pages 1-15, July.
    5. Tsao, Yu-Chung & Vu, Thuy-Linh, 2023. "Distributed energy storage system planning in relation to renewable energy investment," Renewable Energy, Elsevier, vol. 218(C).

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