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• W2059958950 abstract "Smart grid technologies have given rise to a liberalised and decentralised electricity market, enabling energy providers and retailers to have a better understanding of the demand side and its response to pricing signals. This paper puts forward a reinforcement-learning-powered tool aiding an electricity retailer to define the tariff prices it offers, in a bid to optimise its retail strategy. In a competitive market, an energy retailer aims to simultaneously increase the number of contracted customers and its profit margin. We have abstracted the problem of deciding on a tariff price as faced by a retailer, as a semi-Markov decision problem (SMDP). A hierarchical reinforcement learning approach, MaxQ value function decomposition, is applied to solve the SMDP through interactions with the market. To evaluate our trading strategy, we developed a retailer agent (termed AstonTAC) that uses the proposed SMDP framework to act in an open multi-agent simulation environment, the Power Trading Agent Competition (Power TAC). An evaluation and analysis of the 2013 Power TAC finals show that AstonTAC successfully selects sell prices that attract as many customers as necessary to maximise the profit margin. Moreover, during the competition, AstonTAC was the only retailer agent performing well across all retail market settings." @default.
• W2059958950 created "2016-06-24" @default.
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• W2059958950 date "2014-10-01" @default.
• W2059958950 modified "2023-09-26" @default.
• W2059958950 title "Optimising market share and profit margin: SMDP-based tariff pricing under the smart grid paradigm" @default.
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• W2059958950 doi "https://doi.org/10.1109/isgteurope.2014.7028942" @default.
• W2059958950 hasPublicationYear "2014" @default.
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