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Communication Dans Un Congrès Année : 2019

Energy Management for Microgrids: a Reinforcement Learning Approach

Résumé

This paper presents a framework based on reinforcement learning for energy management and economic dispatch of an islanded microgrid without any forecasting module. The architecture of the algorithm is divided in two parts: a learning phase trained by a reinforcement learning (RL) algorithm on a small dataset and the testing phase based on a decision tree induced from the trained RL. An advantage of this approach is to create an autonomous agent, able to react in real-time, considering only the past. This framework was tested on real data acquired at Ecole Polytechnique in France over a long period of time, with a large diversity in the type of days considered. It showed near optimal, efficient and stable results in each situation.
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Dates et versions

hal-02382232 , version 1 (12-12-2019)

Identifiants

Citer

Tanguy Levent, Philippe Preux, Erwan Le Pennec, Jordi Badosa, Gonzague Henri, et al.. Energy Management for Microgrids: a Reinforcement Learning Approach. ISGT-Europe 2019 - IEEE PES Innovative Smart Grid Technologies Europe, Sep 2019, Bucharest, France. pp.1-5, ⟨10.1109/ISGTEurope.2019.8905538⟩. ⟨hal-02382232⟩
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