2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)最新文献_第10页

Multi-Level Optimal Power Flow Solver in Large Distribution Networks 大型配电网多级最优潮流求解器

2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) Pub Date : 2020-02-29 DOI: 10.1109/SmartGridComm47815.2020.9303000

Xinyang Zhou, Yue-Chun Chen, Zhiyuan Liu, Changhong Zhao, Lijun Chen

引用次数: 2

Matrix Completion Using Alternating Minimization for Distribution System State Estimation 基于交变最小矩阵补全的配电系统状态估计

2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) Pub Date : 2019-09-27 DOI: 10.1109/SmartGridComm47815.2020.9302962

Yajing Liu, April Sagan, A. Bernstein, Rui Yang, Xinyang Zhou, Y. Zhang

{"title":"Matrix Completion Using Alternating Minimization for Distribution System State Estimation","authors":"Yajing Liu, April Sagan, A. Bernstein, Rui Yang, Xinyang Zhou, Y. Zhang","doi":"10.1109/SmartGridComm47815.2020.9302962","DOIUrl":"https://doi.org/10.1109/SmartGridComm47815.2020.9302962","url":null,"abstract":"This paper examines the problem of state estimation in power distribution systems under low-observability conditions. The recently proposed constrained matrix completion method which combines the standard matrix completion method and power flow constraints has been shown to be effective in estimating voltage phasors under low-observability conditions using single-snapshot information. However, the method requires solving a semidefinite programming (SDP) problem, which becomes computationally infeasible for large systems and if multiple-snapshot (time-series) information is used. This paper proposes an efficient algorithm to solve the constrained matrix completion problem with time-series data. This algorithm is based on reformulating the matrix completion problem as a bilinear (non-convex) optimization problem, and applying the alternating minimization algorithm to solve this problem. This paper proves the summable convergence of the proposed algorithm, and demonstrates its efficacy and scalability via IEEE 123-bus system and a real utility feeder system. This paper also explores the value of adding more data from the history in terms of computation time and estimation accuracy.","PeriodicalId":428461,"journal":{"name":"2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131738565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Learning Optimal Solutions for Extremely Fast AC Optimal Power Flow 学习极快交流最优潮流的最优解决方案

2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) Pub Date : 2019-09-27 DOI: 10.1109/SmartGridComm47815.2020.9303008

Ahmed S. Zamzam, K. Baker

{"title":"Learning Optimal Solutions for Extremely Fast AC Optimal Power Flow","authors":"Ahmed S. Zamzam, K. Baker","doi":"10.1109/SmartGridComm47815.2020.9303008","DOIUrl":"https://doi.org/10.1109/SmartGridComm47815.2020.9303008","url":null,"abstract":"We develop, in this paper, a machine learning approach to optimize the real-time operation of electric power grids. In particular, we learn feasible solutions to the AC optimal power flow (OPF) problem with negligible optimality gaps. The AC OPF problem aims at identifying optimal operational conditions of the power grids that minimize power losses and/or generation costs. Due to the computational challenges with solving this nonconvex problem, many efforts have focused on linearizing or approximating the problem in order to solve the AC OPF on faster timescales. However, many of these approximations can be fairly poor representations of the actual system state and still require solving an optimization problem, which can be time consuming for large networks. In this work, we learn a mapping between the system loading and optimal generation values, enabling us to find near-optimal and feasible AC OPF solutions. This allows us to bypass solving the traditionally nonconvex AC OPF problem, resulting in a significant decrease in computational burden for grid operators.","PeriodicalId":428461,"journal":{"name":"2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130187544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 114

Vulnerability Assessment of Large-scale Power Systems to False Data Injection Attacks 大型电力系统对虚假数据注入攻击的脆弱性评估

2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) Pub Date : 2017-05-10 DOI: 10.1109/SmartGridComm47815.2020.9302956

Jiazi Zhang, Zhigang Chu, L. Sankar, O. Kosut

引用次数: 43