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ORIGINAL RESEARCH article

Front. Energy Res.
Sec. Sustainable Energy Systems
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1440192
This article is part of the Research Topic Urban Energy System Planning, Operation, and Control with High Efficiency and Low Carbon Goals View all 21 articles

Distributionally Robust Chance-Constrained Operation of Distribution Grids Considering Voltage Constraints

Provisionally accepted
Chao Wang Chao Wang 1Junjie Sun Junjie Sun 1Xinwei Li Xinwei Li 1Tiankai Yang Tiankai Yang 2*Wangsong Liu Wangsong Liu 1
  • 1 State Grid Liaoning Electric Power Co., Ltd, Shenyang, Liaoning Province, China
  • 2 Dalian Maritime University, Dalian, China

The final, formatted version of the article will be published soon.

    The distribution grid experiences node voltage fluctuations due to the growing uncertainty of largescale renewable energy sources (RESs). A practical solution is establishing a chance-constrained optimal model to deal with the uncertainties. However, using this method needs to know the accurate probability distribution of node power injections, which has limitations in application. Therefore, this paper proposes a distributionally robust chance-constrained optimization method for power grid operation based on the ambiguity set of probability distributions. Firstly, considering voltage security constraints, this paper establishes a chance-constrained model to minimize the cost of active power regulation. Besides, based on the Wasserstein ambiguity set, a linearized method is proposed to convexify the objective function. Moreover, the conditional risk value (CVaR) is applied to convert the uncertain model into a deterministic model. The effectiveness of the proposed method is validated through optimization results obtained for the modified PG&E69-bus distribution grid.

    Keywords: uncertainty of renewable energy sources, voltage security constraints, Distributionally robust chance constraints, Wasserstein distance, Conditional risk value, Linearized method

    Received: 29 May 2024; Accepted: 19 Jun 2024.

    Copyright: © 2024 Wang, Sun, Li, Yang and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Tiankai Yang, Dalian Maritime University, Dalian, China

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