Modeling, Prediction and Risk Management of Distribution System Voltages with Non-Gaussian Probability Distributions

作     者：Gao, Yuanhai Xu, Xiaoyuan Yan, Zheng Shahidehpour, Mohammad Yang, Bo Guan, Xinping 

作者机构：The Ministry of Education Key Laboratory of Control of Power Transmission and Conversion Shanghai Jiao Tong University Shanghai200240 China The Shanghai Non-carbon Energy Conversion Utilization Institute Shanghai200240 China The Galvin Center for Electricity Innovation Illinois Institute of Technology ChicagoIL60616 United States The Department of Automation The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai200240 China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Prediction models 

摘      要：High renewable energy penetration into power distribution systems causes a substantial risk of exceeding voltage security limits, which needs to be accurately assessed and properly managed. However, the existing methods usually rely on the joint probability models of power generation and loads provided by probabilistic prediction to quantify the voltage risks, where inaccurate prediction results could lead to over or under estimated risks. This paper proposes an uncertain voltage component (UVC) prediction method for assessing and managing voltage risks. First, we define the UVC to evaluate voltage variations caused by the uncertainties associated with power generation and loads. Second, we propose a Gaussian mixture model-based probabilistic UVC prediction method to depict the non-Gaussian distribution of voltage variations. Then, we derive the voltage risk indices, including value-at-risk (VaR) and conditional value-at-risk (CVaR), based on the probabilistic UVC prediction model. Third, we investigate the mechanism of UVC-based voltage risk management and establish the voltage risk management problems, which are reformulated into linear programming or mixed-integer linear programming for convenient solutions. The proposed method is tested on power distribution systems with actual photovoltaic power and load data and compared with those considering probabilistic prediction of nodal power injections. Numerical results show that the proposed method is computationally efficient in assessing voltage risks and outperforms existing methods in managing voltage risks. The deviation of voltage risks obtained by the proposed method is only 15% of that by the methods based on probabilistic prediction of nodal power injections. Copyright © 2024, The Authors. All rights reserved.