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Energies 2017, 10(11), 1846;

Battery Energy Management in a Microgrid Using Batch Reinforcement Learning

ESAT/Electa, KU Leuven, Kasteelpark Arenberg 10 bus 2445, BE-3001 Leuven, Belgium
Energy Department, EnergyVille, Thor Park, Poort Genk 8130, 3600 Genk, Belgium
Energy Department, Vlaamse Instelling voor Technologisch Onderzoek (VITO), Boeretang 200, B-2400 Mol, Belgium
This paper is an extended version of our paper published in International workshop of Energy-Open 2017.
Author to whom correspondence should be addressed.
Received: 15 October 2017 / Revised: 5 November 2017 / Accepted: 7 November 2017 / Published: 12 November 2017
(This article belongs to the Special Issue Selected Papers from International Workshop of Energy-Open)
Full-Text   |   PDF [1076 KB, uploaded 12 November 2017]   |  


Motivated by recent developments in batch Reinforcement Learning (RL), this paper contributes to the application of batch RL in energy management in microgrids. We tackle the challenge of finding a closed-loop control policy to optimally schedule the operation of a storage device, in order to maximize self-consumption of local photovoltaic production in a microgrid. In this work, the fitted Q-iteration algorithm, a standard batch RL technique, is used by an RL agent to construct a control policy. The proposed method is data-driven and uses a state-action value function to find an optimal scheduling plan for a battery. The battery’s charge and discharge efficiencies, and the nonlinearity in the microgrid due to the inverter’s efficiency are taken into account. The proposed approach has been tested by simulation in a residential setting using data from Belgian residential consumers. The developed framework is benchmarked with a model-based technique, and the simulation results show a performance gap of 19%. The simulation results provide insight for developing optimal policies in more realistically-scaled and interconnected microgrids and for including uncertainties in generation and consumption for which white-box models become inaccurate and/or infeasible. View Full-Text
Keywords: control policy; fitted-Q iteration; microgrids; reinforcement learning control policy; fitted-Q iteration; microgrids; reinforcement learning

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Mbuwir, B.V.; Ruelens, F.; Spiessens, F.; Deconinck, G. Battery Energy Management in a Microgrid Using Batch Reinforcement Learning. Energies 2017, 10, 1846.

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