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A multi-objective stochastic-information gap decision model for soft open points planning considering power fluctuation and growth uncertainty

Author

Listed:
  • Li, Junkai
  • Ge, Shaoyun
  • Zhang, Shida
  • Xu, Zhengyang
  • Wang, Liyong
  • Wang, Chengshan
  • Liu, Hong

Abstract

Recently, soft open points (SOPs) attract much attention due to providing power flow control and voltage regulation to the distribution system (DS). However, multiple uncertainties and expensive capital cost are referred to as thorny issues in the SOPs planning. In this paper, a multi-objective stochastic-information gap decision (MS-IGD) model is presented to alleviate multiple uncertainties with the limited budget. First, a deterministic SOP planning model is built to minimize the comprehensive cost of distributed system operator (DSO). Then, a MS-IGD model is formulated considering power fluctuation and growth uncertainty in distributed generators (DGs) and loads. To solve this model effectively, it is transformed to a mixed integer linear programming (MILP) by the polyhedron linearization technology, linear programming theory and ε constraint algorithm. In case studies, based on a prospective DS from northern China, the SOPs planning scheme with corresponding accommodation range of multiple uncertainties is presented. Numerical results not only authenticate that the worst-case envelope bound should not be decided in advance because of the complicated relationship between multiple uncertainties and investments, but also demonstrate the negative correlation between the deviation of power fluctuation and growth uncertainty under the same budget.

Suggested Citation

  • Li, Junkai & Ge, Shaoyun & Zhang, Shida & Xu, Zhengyang & Wang, Liyong & Wang, Chengshan & Liu, Hong, 2022. "A multi-objective stochastic-information gap decision model for soft open points planning considering power fluctuation and growth uncertainty," Applied Energy, Elsevier, vol. 317(C).
    • Handle: RePEc:eee:appene:v:317:y:2022:i:c:s0306261922005177
    DOI: 10.1016/j.apenergy.2022.119141
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    References listed on IDEAS

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    Cited by:

    1. Hongwei Li & Xingmin Li & Siyu Chen & Shuaibing Li & Yongqiang Kang & Xiping Ma, 2024. "Low-Carbon Optimal Scheduling of Integrated Energy System Considering Multiple Uncertainties and Electricity–Heat Integrated Demand Response," Energies, MDPI, vol. 17(1), pages 1-20, January.
    2. Deakin, Matthew & Sarantakos, Ilias & Greenwood, David & Bialek, Janusz & Taylor, Phil C. & Walker, Sara, 2023. "Comparative analysis of services from soft open points using cost–benefit analysis," Applied Energy, Elsevier, vol. 333(C).
    3. Zhu, Junpeng & Huang, Yong & Lu, Shuai & Shen, Mengya & Yuan, Yue, 2024. "Incorporating local uncertainty management into distribution system planning: An adaptive robust optimization approach," Applied Energy, Elsevier, vol. 363(C).

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