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• W2989646871 abstract "In an independent self-sustained micro grid (MG) with limited energy resources, plugged-in electric vehicles (EV) must compete for available excess power supply or demand, modeled as a random variable. This paper proposes a distributed machine learning algorithm based on a Markov decision process (MDP) and non-cooperative game theory, that maximizes the EV’s profit under uncertainty of future MG supply/demand states, while satisfying specific battery constraints imposed by the EV owner. Performance evaluation of the proposed algorithm shows that even with no a priori knowledge of future MG supply/demand states, it achieves average profits of only 43% less than the global optimal profit. Results also show that using a cooperative version of the algorithm leads to a 12% increase in average profits." @default.
• W2989646871 created "2019-12-05" @default.
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• W2989646871 date "2019-10-01" @default.
• W2989646871 modified "2023-09-27" @default.
• W2989646871 title "A Market Oriented, Reinforcement Learning Based Approach for Electric Vehicles Integration in Smart Micro Grids" @default.
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• W2989646871 doi "https://doi.org/10.1109/smartgridcomm.2019.8909698" @default.
• W2989646871 hasPublicationYear "2019" @default.
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