EPJ Web Conf.
Volume 217, 2019International Workshop on Flexibility and Resiliency Problems of Electric Power Systems (FREPS 2019)
|Number of page(s)||9|
|Published online||15 October 2019|
Deep Reinforcement Learning for Energy Microgrids Management Considering Flexible Energy Sources
Melentiev Energy Systems Institute SB RAS, Irkutsk, Russia
Published online: 15 October 2019
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.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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