A surrogate-assisted point estimate method for hybrid probabilistic and interval power flow in distribution networks

作     者：Wang, Chenxu Peng, Yan Zhou, Yixi Ma, Junchao Lu, Chengyu Yang, Xiyun 

作者机构：State Grid Zhejiang Elect Power Corp Elect Power Res Inst Hangzhou 310014 Peoples R China State Grid Hangzhou Elect Power Supply Co Hangzhou 310020 Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China 

出 版 物：《ENERGY REPORTS》 (Energy Rep.)

年 卷 期：2022年第8卷

页      面：713-721页

核心收录：

基　　金：State Grid Corporation Science and Technology Project [5211DS220009] 

主　　题：Uncertain power flow Hybrid uncertainties Distributed generators Point estimate method Surrogate model 

摘      要：The uncertain power flow analysis is essential to assessing the operating states of distribution networks under various uncertainties. The power flow calculations considering the single uncertainty have been well developed. However, different types of uncertainties may coexist in power systems. In order to comprehensively assess the impacts of mixed uncertainties, this paper proposes a surrogate-assisted point estimate method to solve the hybrid probabilistic and interval power flow. By taking advantage of the point estimate method and high-fidelity surrogate model, the proposed method can accurately obtain the relevant results of power flow calculation with high efficiency. The numerical studies in IEEE 33-bus and 141-bus systems verify the accuracy and efficiency of the proposed method by comparing it with other well-known methods. (C) 2022 The Author(s). Published by Elsevier Ltd.