maximize producer rewards in distributed windmill environments: a q-learning approach

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ID: 177773
2015
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Abstract
In Smart Grid environments, homes equipped with windmills are encouraged to generate energy and sell it back to utilities. Time of Use pricing and the introduction of storage devices would greatly influence a user in deciding when to sell back energy and how much to sell. Therefore, a study of sequential decision making algorithms that can optimize the total pay off for the user is necessary. In this paper, reinforcement learning is used to tackle this optimization problem. The problem of determining when to sell back energy is formulated as a Markov decision process and the model is learned adaptively using Q-learning. Experiments are done with varying sizes of storage capacities and under periodic energy generation rates of different levels of fluctuations. The results show a notable increase in discounted total rewards from selling back energy with the proposed approach.
Reference Key
li2015aimsmaximize Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Bei Li;Siddharth Gangadhar;Pramode Verma;Samuel Cheng
Journal Biology
Year 2015
DOI
10.3934/energy.2015.1.162
URL
http://www.aimspress.com/energy/article/37/fulltext.html
Keywords
reinforcement learning smart grid q-learning central stations powerplants producer payoff optimizationproduction of electric energy or power

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