.png){kind=icon}
返回
Learning optimization proxies for large-scale Security-Constrained Economic
.png){kind=icon}
OA
AI
DOI：10.1016/j.epsr.2022.108566.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The Security-Constrained Economic Dispatch (SCED) is a fundamental optimization model for Transmission System Operators (TSO) to clear real-time energy markets while ensuring reliable operations of power grids. In a context of growing operational uncertainty, due to increased penetration of renewable generators and distributed energy resources, operators must continuously monitor risk in real-time, i.e., they must quickly assess the system's behavior under various changes in load and renewable production. Unfortunately, systematically solving an optimization problem for each such scenario is not practical given the tight constraints of real-time operations. To overcome this limitation, this paper proposes to learn an optimization proxy for SCED, i.e., a Machine Learning (ML) model that can predict an optimal solution for SCED in milliseconds. Motivated by a principled analysis of the market-clearing optimizations of MISO, the paper proposes a novel just-in-time ML pipeline that addresses the main challenges of learning SCED solutions, i.e., the variability in load, renewable output and production costs, as well as the combinatorial structure of commitment decisions. A novel combined classification-plus-regression architecture is also proposed, to further capture the behavior of SCED solutions. Numerical experiments are reported on the French transmission system, and demonstrate the approach's ability to produce, within a time frame that is compatible with real-time operations, accurate optimization proxies that produce relative errors below 1%.
Keyword:
Security-constrained economic dispatch
Optimization proxy
Deep learning
Neural network
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.2
论文数:
1.2W
被引数:
2.2W
机构
引用论文
.png){kind=icon}
Combining Deep Learning and Optimization for Preventive Security-Constrained DC Optimal Power Flow深度学习与优化相结合的预防性安全约束直流最优潮流OAAI
OAAI分享收藏Fast Economic Dispatch in Smart Grids Using Deep Learning: An Active Constraint Screening Approach
PREAI
分享收藏
查看更多内容