This paper investigates adaptive optimal control of a grid-independent photovoltaic system consisting of a collector, storage, and a load. The control 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
Contributed by the Solar Energy Division of THE AMERICAN SOCIETY OF MECHANICAL ENGINEERS for publication in the ASME JOURNAL OF SOLAR ENERGY ENGINEERING. Manuscript received by the ASME Solar Energy Division, April 2002; final revision, August 2002. Associate Editor: A. Reddy.
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Henze, G. P., and Dodier, R. H. (January 27, 2003). "Adaptive Optimal Control of a Grid-Independent Photovoltaic System ." ASME. J. Sol. Energy Eng. February 2003; 125(1): 34–42. https://doi.org/10.1115/1.1532005
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