Optimal Energy Management of a Grid-Tied Solar PV-Battery Microgrid: A Reinforcement Learning Approach
نویسندگان
چکیده
In the near future, microgrids will become more prevalent as they play a critical role in integrating distributed renewable energy resources into main grid. Nevertheless, sources, such solar and wind can be extremely volatile are weather dependent. These coupled with demand lead to random variations on both generation load sides, thus complicating optimal management. this article, reinforcement learning approach has been proposed deal non-stationary scenario, which management system (EMS) is modelled Markov decision process (MDP). A novel modification of control problem presented that improves use stored battery dynamic not subjected future high grid tariffs. comprehensive reward function also developed decreases infeasible action explorations improving performance data-driven technique. Q-learning algorithm then minimize operational cost microgrid under unknown information. To assess EMS, comparison study between trading EMS model non-trading case performed using typical commercial curve PV profile over 24-h horizon. Numerical simulation results indicate agent learns select an optimized schedule minimizes (cost power purchased from utility wear cost) all studied cases. However, comparing costs, latter one was found decrease costs by 4.033% summer season 2.199% winter season.
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© 2013 Sarasua et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Dynamic Modelling of Advanced Battery Energy Storage System for Grid-Tied AC Microgr...
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ژورنال
عنوان ژورنال: Energies
سال: 2021
ISSN: ['1996-1073']
DOI: https://doi.org/10.3390/en14092700