Distributionally robust optimization model of active distribution network considering uncertainties of source and load

作     者：Lei DONG Jia LI Tianjiao PU Naishi CHEN 

作者机构：Power System Research InstituteNorth China Electric Power UniversityBeijing 102206China China Electric Power Research InstituteBeijing 100192China 

出 版 物：《Journal of Modern Power Systems and Clean Energy》 (现代电力系统与清洁能源学报（英文）)

年 卷 期：2019年第7卷第6期

页      面：1585-1595页

核心收录：

学科分类：080802[工学-电力系统及其自动化] 0808[工学-电气工程] 08[工学] 

基　　金：supported by Natural Science Foundation of Beijing Municipality(No.3161002) National Key R&D Program(No.2017YFB0903300) 

主　　题：Active distribution network(ADN) Source-load uncertainty Two-stage distributionally robust optimization Ambiguity set Generalized linear decision rule 

摘      要：To ensure the safety and reliability of the distribution network and adapt to the uncertain development of renewable energy sources and loads,a two-stage distributionally robust optimization model is proposed for the active distribution network(ADN)optimization problem considering the uncertainties of the source and load in this *** establishing an ambiguity set to capture the uncertainties of the photovoltaic(PV)power,wind power and load,the piecewise-linear function and auxiliary parameters are introduced to help characterize the probability distribution of uncertain *** optimization goal of the model is to minimize the total expected cost under the worst-case distribution in the ambiguity *** first-stage expected cost is obtained based on the predicted value of the uncertainty *** second-stage expected cost is based on the actual value of the uncertainty variable to solve the first-stage *** generalized linear decision rule approximates the two-stage optimization model,and the affine function is introduced to provide a closer approximation to the second-stage optimization ***,the improved IEEE 33-node and IEEE 118-node systems are simulated and analyzed with deterministic methods,stochastic programming,and robust optimization methods to verify the feasibility and superiority of the proposed model and algorithm.