Optimization of plug-in electric vehicle charging with forecasted price
作者： Adriana Chis , Jarmo Lunden , Visa Koivunen
DOI: 10.1109/ICASSP.2015.7178338
关键词: Computer science 、 Plug-in 、 Markov decision process 、 Time horizon 、 Electricity 、 Electric vehicle 、 Scheduling (computing) 、 Battery (electricity) 、 Smart grid 、 Mathematical optimization 、 Approximation algorithm
摘要: This paper proposes a new method for scheduling the charging of plug-in electric vehicle's (PEV) battery. The is employed in demand side management smart grids and has goal reducing cost over long time horizon. problem PEV battery modeled as Markov decision process with unknown transition probabilities. A fitted Qiteration batch reinforcement learning algorithm kernel-based approximation value iteration proposed dynamics solving problem. solution obtained based on knowledge true day-ahead electricity prices predicted second day ahead. Simulation results using pricing data demonstrate savings 8%-40% consumer.
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