ABSTRACTThis 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.
Solar Microgrid Desalination Unit Fuzzy Reward Quality Of Services Energy Management Q-Learning Algorithm Local Consumer Battery Bank Energy Management In Microgrid Reinforcement Learning
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Round-ups are the summaries of handpicked papers around trending topics published every week. These would enable you to scan through a collection of papers and decide if the paper is relevant to you before actually investing time into reading it.
Climate change Research Articles published between Sep 19, 2022 to Sep 25, 2022
Sep 26, 2022
Articles Included: 5
Disaster Prevention and Management ISSN: 0965-3562 Article publication date: 20 September 2022 This paper applies the theory of cascading, interconnec...Read More
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