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Energy management in solar microgrid via reinforcement learning using fuzzy reward

Energy management in solar microgrid via reinforcement learning using fuzzy reward This paper proposes a single-agent system towards solving energy management issues in solar microgrids. The proposed system consists of a photovoltaic (PV) source, a battery bank, a desalination unit (responsible for providing the demanded water) and a local consumer. The trade-offs and complexities involved in the operation of the different units, and the quality of services’ demanded from energy consumer units (e.g. the desalination unit), makes the energy management a challenging task. The goal of the agent is to satisfy the energy demand in the solar microgrid, optimizing the battery usage, in conjunction to satisfying the quality of services provided. It is assumed that the solar microgrid operates in island-mode. Thus, no connection to the electrical grid is considered. The agent collects data from the elements of the system and learns the suitable policy towards optimizing system performance by using the Q-Learning algorithm. The reward function is implemented by fuzzy system Sugeno type for improving the learning efficiency. Simulation results provided show the performance of the system. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Advances in Building Energy Research Taylor & Francis

Energy management in solar microgrid via reinforcement learning using fuzzy reward

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Energy management in solar microgrid via reinforcement learning using fuzzy reward

Abstract

This paper proposes a single-agent system towards solving energy management issues in solar microgrids. The proposed system consists of a photovoltaic (PV) source, a battery bank, a desalination unit (responsible for providing the demanded water) and a local consumer. The trade-offs and complexities involved in the operation of the different units, and the quality of services’ demanded from energy consumer units (e.g. the desalination unit), makes the energy management a challenging...
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Publisher
Taylor & Francis
Copyright
© 2017 Informa UK Limited, trading as Taylor & Francis Group
ISSN
1756-2201
eISSN
1751-2549
DOI
10.1080/17512549.2017.1314832
Publisher site
See Article on Publisher Site

Abstract

This paper proposes a single-agent system towards solving energy management issues in solar microgrids. The proposed system consists of a photovoltaic (PV) source, a battery bank, a desalination unit (responsible for providing the demanded water) and a local consumer. The trade-offs and complexities involved in the operation of the different units, and the quality of services’ demanded from energy consumer units (e.g. the desalination unit), makes the energy management a challenging task. The goal of the agent is to satisfy the energy demand in the solar microgrid, optimizing the battery usage, in conjunction to satisfying the quality of services provided. It is assumed that the solar microgrid operates in island-mode. Thus, no connection to the electrical grid is considered. The agent collects data from the elements of the system and learns the suitable policy towards optimizing system performance by using the Q-Learning algorithm. The reward function is implemented by fuzzy system Sugeno type for improving the learning efficiency. Simulation results provided show the performance of the system.

Journal

Advances in Building Energy Research – Taylor & Francis

Published: Jan 2, 2018

Keywords: Reinforcement learning; Q-learning; microgrid; energy management; fuzzy reward

References

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