This paper investigates adaptive optimal control of a grid-independent photovoltaic system consisting of a collector, storage, and a load. The algorithm is based on Q-Learning, a model-free reinforcement learning algorithm, which optimizes control performance through exploration. Q-Learning is used in a simulation study to find a policy which performs better than a conventional control strategy with respect to a cost function which places more weight on meeting a critical base load than on those non-critical loads exceeding the base load.
Adaptive Optimal Control of a Grid-Independent Photovoltaic System
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Henze, GP, & Dodier, RH. "Adaptive Optimal Control of a Grid-Independent Photovoltaic System." Proceedings of the ASME Solar 2002: International Solar Energy Conference. Solar Energy. Reno, Nevada, USA. June 15–20, 2002. pp. 139-148. ASME. https://doi.org/10.1115/SED2002-1045
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