Modeling the impact of extreme summer drought on conventional and renewable generation capacity: methods and a case study on the Eastern U.S. power system

no code implementations • 13 Nov 2023 • Hang Shuai, Fangxing Li, Jinxiang Zhu, William Jerome Tingen II, Srijib Mukherjee

This paper proposes a systematic, analytical approach for assessing the impacts of extreme summer drought events on the available capacity of hydro, thermal, and renewable energy generators.

Deep Reinforcement Learning based Model-free On-line Dynamic Multi-Microgrid Formation to Enhance Resilience

no code implementations • 6 Mar 2022 • Jin Zhao, Member, Fangxing Li, Fellow, Srijib Mukherjee, Senior Member, Christopher Sticht

The proposed deep RL method provides real-time computing to support on-line dynamic MMGF scheme, and the scheme handles a long-term resilience enhancement problem using adaptive on-line MMGF to defend changeable conditions.

Reinforcement Learning (RL)