Adaptive Optimal Control of a Grid-Independent Photovoltaic System-Reference-Cited by-同舟云学术

Adaptive Optimal Control of a Grid-Independent Photovoltaic System

Published:2003-01-27 Issue:1 Volume:125 Page:34-42
ISSN:0199-6231
Container-title:Journal of Solar Energy Engineering
language:en
Short-container-title:

Author:

Henze Gregor P.¹,Dodier Robert H.²

Affiliation:

1. Architectural Engineering, University of Nebraska–Lincoln, 1110 South 67th St., Peter Kiewit Institute 203D, Omaha, Nebraska 68182-0681

2. Civil, Environmental, and Architecural Engineering, University of Colorado at Boulder, Campus Box 428, Boulder, CO 80309-428

Abstract

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.

Publisher

ASME International

Subject

Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment

Link

http://asmedigitalcollection.asme.org/solarenergyengineering/article-pdf/125/1/34/5713358/34_1.pdf

Reference8 articles.

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2. Gullapalli, V. , 1990, “A Stochastic Reinforcement Learning Algorithm for Learning Real-Valued Functions,” Neural Networks, 3, pp. 671–692.

3. Sutton, R. S., and Barto, A. G., 1998, Reinforcement Learning: An Introduction. MIT Press, Cambridge, MA.

4. Bellman, Richard E., 1957, Dynamic Programming, Princeton Univ. Press, Princeton, NJ.

5. Bertsekas, D. P., and Tsitsiklis, J. N., 1996, Neuro-Dynamic Programming, Athena Scientific, Belmont, MA.

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