首页|Online Optimization in Power Systems With High Penetration of Renewable Generation:Advances and Prospects

Online Optimization in Power Systems With High Penetration of Renewable Generation:Advances and Prospects

扫码查看
原文链接
  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
  • 维普
Traditionally, offline optimization of power systems is acceptable due to the largely predictable loads and reliable gen-eration. The increasing penetration of fluctuating renewable gen-eration and internet-of-things devices allowing for fine-grained controllability of loads have led to the diminishing applicability of offline optimization in the power systems domain, and have redi-rected attention to online optimization methods. However, online optimization is a broad topic that can be applied in and moti-vated by different settings, operated on different time scales, and built on different theoretical foundations. This paper reviews the various types of online optimization techniques used in the power systems domain and aims to make clear the distinction between the most common techniques used. In particular, we introduce and compare four distinct techniques used covering the breadth of online optimization techniques used in the power systems domain, i.e., optimization-guided dynamic control, feedback optimization for single-period problems, Lyapunov-based opti-mization, and online convex optimization techniques for multi-period problems. Lastly, we recommend some potential future directions for online optimization in the power systems domain.

Feedback optimizationLyapunov optimizationonline convex optimizationonline optimizationoptimization-guided control

Zhaojian Wang、Wei Wei、John Zhen Fu Pang、Feng Liu、Bo Yang、Xinping Guan、Shengwei Mei

展开 >

Key Laboratory of System Control and Information Processing,Ministry of Education of China,Department of Automation,Shanghai Jiao Tong University,Shanghai 200240,China

State Key Laboratory of Power System and the Department of Electrical Engineering,Tsinghua University,Beijing 100084,China

Institute of High Performance Computing(IHPC),Agency for Science Technology and Research(A*STAR),Singapore 138632,Singapore

National Natural Science Foundation of Chinathe"ChenGuang Program"Supported by the Shanghai Education Development Foundation and Shanghai Municipal Education Commission of Young Elite Scientists Sponsorship Program by Cast of China Association for Science and Technology

6210326520CG11

2023

10.1109/JAS.2023.123126

自动化学报(英文版)
中国自动化学会,中国科学院自动化研究所,中国科技出版传媒股份有限公司

自动化学报(英文版)

CSTPCDCSCD北大核心EI
ISSN：2329-9266
年,卷(期)：2023.10(4)
  • 150