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Reinforcement Learning Algorithm for Solving Load Commitment problem Considering a General Load Model

Journal of Energy and Power Engineering 2013年第6期7卷 1150-1162页

作者： Thythodath Parambath Imthias Ahamed Sayed Danish Maqbool Nazar Hussain Malik Department of Electrical Engineering King Saud University Riyadh 11421 Saudi Arabia

Under the smart grid paradigm, in the near future all consumers will be exposed to variable pricing schemes introduced by utilities. Hence, there is a need to develop algorithms which could be used by the consumers to schedule their loads. In this paper, load scheduling problem is formulated as a LCP （load commitment problem）. The load model is general and can model atomic and non-atomic loads. Furthermore, it can also take into consideration the relative discomfort caused by delay in scheduling any load. For this purpose, a single parameter ＂uric＂ is introduced in the load model which captures the relative discomfort caused by delay in scheduling a particular load. Guidelines for choosing this parameter are given. All the other parameters of the proposed load model can be easily specified by the consumer. The paper shows that the general LCP can be viewed as multi-stage decision making problem or a MDP （markov decision problem）. RL （reinforcement learning） based algorithm is developed to solve this problem. The efficacy of the algorithm is investigated when the price of electricity is available in advance as well as for the case when it is random. The scalability of the approach is also investigated.

关键词： Reinforcement learning markov decision problem demand response load scheduling problem load model.

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