Deep Reinforcement Learning for Energy Microgrids Management Considering Flexible Energy Sources

Melentiev Energy Systems Institute SB RAS, Irkutsk, Russia

Published online: 15 October 2019

Abstract

The problem of optimally activating the flexible energy sources (short- and long-term storage capacities) of electricity microgrid is formulated as a sequential decision making problem under uncertainty where, at every time-step, the uncertainty comes from the lack of knowledge about future electricity consumption and weather dependent PV production. This paper proposes to address this problem using deep reinforcement learning. To this purpose, a specific deep learning architecture has been used in order to extract knowledge from past consumption and production time series as well as any available forecasts. The approach is empirically illustrated in the case of off-grid microgrids located in Belgium and Russia.