报告题目：Feasible Region Analysis and Hierarchical Optimization Algorithm for AC Power Flow
报告人：Prof. Changhong Zhao
会议时间：3月15日（周三）10:00
会议地点：腾讯会议634-327-850
主办单位：重庆大学、输配电装备及系统安全与新技术国家重点实验室、重庆大学溧阳智慧城市研究院
协办单位：四川大学、电子科技大学、西南交通大学、成都理工大学、成都中医药大学、四川师范大学、西华大学、西南科技大学、西南大学、重庆邮电大学、重庆科技学院
Personal Profile:
Dr. Changhong Zhao is an Assistant Professor with the Department. Of Information Engineering at the Chinese University of Hong Kong (CUHK). He received BE in Automation from Tsinghua University and PhD in Electrical Engineering from Caltech. He spent years on research and development at the US National Renewable Energy Laboratory (NREL). He received the Demetriades Prize for best thesis in renewable energy, the Charles Wilts Prize for outstanding research in EE at Caltech, the Early Career Award from Hong Kong Research Grants Council, and the IEEE Power and Energy Society Prize Paper Award.
Abstract:
The growth of distributed and renewable energy sources in power system is calling for more scalable and responsive operation schemes than before, particularly new methods to analyze and optimize power flows in distribution networks. I will introduce two pieces of work on this topic. In the first work, we develop an optimization framework to approximate the feasible region of renewable power injections under a nonlinear AC power flow model. We first formulate a power injection feasibility problem, which is then relaxed to a convex second-order cone program (SOCP). We utilize the strong duality of SOCP to characterize the SOCP-relaxed feasible region as a convex polytope. Finally, we apply a heuristic method to remove power injections that make the SOCP relaxation inexact and obtain a more accurate approximation of the feasible renewable power injection region. The second work is a hierarchical distributed algorithm to solve a large-scale optimal power flow (OPF) problem. The proposed algorithm exploits the tree structure of a distribution network to significantly reduce the computational burden of the primal-dual gradient method to solve OPF. To reduce the risk of voltage violation caused by the modeling inaccuracy due to power flow linearization, we propose an improved gradient evaluation method that is more accurate with mild impact on computational efficiency.
