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Distribution system planning considering peak shaving of energy station

Author

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  • He, Shuaijia
  • Gao, Hongjun
  • Liu, Junyong
  • Zhang, Xi
  • Chen, Zhe

Abstract

Energy stations (ESs) connected in a distribution system (DS) may lead to great impacts on the planning scheme of DS. In this context, this paper carries out a long-term DS planning model considering the peak shaving of ES, which is achieved by scheduling the input energy of ES. By regarding DS and ES as different stakeholders, a decentralized framework is devised to shave the electric peak loads in the DS planning, where the coupling relationship between the TOU price and exchanged power (e.g., the input power of ES) is clearly expressed. Specifically, an explicit adjustment formula is developed to represent the coupling relationship based on the concept of elasticity. In addition, an easily reformulated solution method is developed to address the probability distribution (PD) uncertainty of electric and cooling loads in the uncertainty-moment-based distributionally robust optimization (DRO) planning model. The chance-constrained power balance is expressed in a second order conic (SOC) format based on the conditional value at risk (CVaR) method and duality theory. Then, the SOC constraints are linearized according to the polyhedral linearization method. Furthermore, the bilinear terms of the planning model are respectively linearized by the McCormick and big-M methods. Finally, the proposed planning model is tested on a modified IEEE 33-node DS with an ES and a practical 99-node DS with an ES. Numerical results show that the proposed planning model is effective in managing PD uncertainties of loads as well as reducing costs of DS.

Suggested Citation

  • He, Shuaijia & Gao, Hongjun & Liu, Junyong & Zhang, Xi & Chen, Zhe, 2022. "Distribution system planning considering peak shaving of energy station," Applied Energy, Elsevier, vol. 312(C).
    • Handle: RePEc:eee:appene:v:312:y:2022:i:c:s030626192200157x
    DOI: 10.1016/j.apenergy.2022.118692
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    References listed on IDEAS

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    Cited by:

    1. Xiang, Yue & Dai, Jiakun & Xue, Ping & Liu, Junyong, 2023. "Autonomous topology planning for distribution network expansion: A learning-based decoupled optimization method," Applied Energy, Elsevier, vol. 348(C).
    2. Wu, Huayi & Xu, Zhao, 2024. "Multi-energy flow calculation in integrated energy system via topological graph attention convolutional network with transfer learning," Energy, Elsevier, vol. 303(C).
    3. Zhichun Yang & Gang Han & Fan Yang & Yu Shen & Yu Liu & Huaidong Min & Zhiqiang Zhou & Bin Zhou & Wei Hu & Yang Lei, 2023. "A Distribution Network Planning Method Considering the Distributed Energy Resource Flexibility of Virtual Power Plants," Sustainability, MDPI, vol. 15(19), pages 1-17, September.
    4. Henni, Sarah & Becker, Jonas & Staudt, Philipp & vom Scheidt, Frederik & Weinhardt, Christof, 2022. "Industrial peak shaving with battery storage using a probabilistic forecasting approach: Economic evaluation of risk attitude," Applied Energy, Elsevier, vol. 327(C).
    5. Cerna, Fernando V. & Dantas, Jamile T. & Naderi, Ehsan & Contreras, Javier, 2024. "Optimal strategy to reduce energy waste in an electricity distribution network through direct/indirect bulk load control," Energy, Elsevier, vol. 294(C).

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