Title of article :
Bidding Strategy on Demand Side Using Eligibility Traces Algorithm
Author/Authors :
Naseri Gavareshk, Mohammad Ali Department of Electrical and Biomedical Engineering - Sadjad University of Technology, Mashhad , Hasanpour Darban, Somayeh Department of Electrical and Biomedical Engineering - Sadjad University of Technology, Mashhad , Noori, Amin Department of Electrical and Biomedical Engineering - Sadjad University of Technology, Mashhad , Besharatifar, Mahdi Department of Electrical and Biomedical Engineering - Sadjad University of Technology, Mashhad
Pages :
7
From page :
163
To page :
169
Abstract :
Restructuring in the power industry is followed by splitting different parts and creating a competition between purchasing and selling sections. As a consequence, through an active participation in the energy market, the service provider companies and large consumers create a context for overcoming the problems resulted from lack of demand side participation in the market. The most prominent challenge for customers on demand side, is bidding strategy selection manner for attending in the competitive market. In this regard, they attempt to pay the least expense for purchasing the energy, while tolerating the least risk. In this paper, bidding strategy of service provider companies and the large consumers in the power market is proposed under the eligibility traces algorithm. In this algorithm, the demand side customers are considered as agents of Reinforcement Learning (RL). These agents learn through interaction with environment to bid such that earn the highest benefit.
Keywords :
Power market , bidding strategy , demand side , service provider companies , reinforcement learning , elgibility traces
Journal title :
Astroparticle Physics
Serial Year :
2017
Record number :
2491158
Link To Document :
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