Alternating Direction Method of Multipliers for Solving Joint Chance Constrained Optimal Power Flow Under Uncertainties

被引:0
|
作者
Qin, James Ciyu [1 ]
Yan, Yifan [2 ]
Jiang, Rujun [2 ]
Mo, Huadong [1 ]
Dong, Daoyi [1 ]
机构
[1] Univ New South Wales, Sch Engn & Informat Technol, Canberra, ACT 2610, Australia
[2] Fudan Univ, Sch Data Sci, Shanghai 200433, Peoples R China
来源
IFAC PAPERSONLINE | 2022年 / 55卷 / 16期
基金
中国国家自然科学基金; 澳大利亚研究理事会;
关键词
Alternating direction method of multipliers; joint chance-constraint; optimal power flow; optimization; uncertainty;
D O I
10.1016/j.ifacol.2022.09.010
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The increasing penetration of renewable energy sources in power systems introduces additional load fluctuations. Lack of awareness of them may lead to a high risk of system failure. Many existing approaches mitigate this issue by considering individual chance constraints (CCs) for the physical limitation of the system. To guarantee that all the operational constraints are satisfied with a predetermined probability, this paper uses joint chance constraints to formulate the optimal power flow (OPF) problem. Additionally, to ensure the scalability, this paper presents an alternating direction method of multipliers (ADMM) with convex optimization subproblems to solve the joint chance-constrained (JCC) OPF, where the computational burden is reduced. At last, to avoid making assumptions about the uncertainties, the CCs are approximated with a sample-based approach. Copyright (C) 2022 The Authors.
引用
收藏
页码:116 / 121
页数:6
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