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- W2389758211 abstract "We consider the sequential decision problem faced by the manager of an electric vehicle (EV) charging station, who aims to satisfy the charging demand of the customer while minimizing cost. Since the total time needed to charge the EV up to capacity is typically less than the amount of time that the customer is away, there are opportunities to exploit electricity spot price variations within some time window. However, it is also true that the return time of the customer is uncertain, so there exists the risk of an insufficient charge. We formulate the problem as a finite horizon Markov decision process (MDP) and consider a risk-averse objective function by optimizing under a dynamic risk measure constructed using a convex combination of expected value and conditional value at risk (CVaR). For the first time in the literature, we provide an analysis of the effect that risk parameters, e.g., the risk-level $alpha$ used in CVaR, have on the structure of the optimal policy. We show that becoming more risk-averse in the dynamic risk measure sense corresponds to the intuitively appealing notion of becoming more risk-averse in the order thresholds of the optimal policy. This result allows us to develop computational techniques for approximating a spectrum of risk-averse generated by varying the parameters of the risk measure. Finally, numerical results for a case study using spot price data from California ISO (CAISO) are shown, where the Pareto optimality of our policies when measured against practical metrics of risk and reward is examined." @default.
- W2389758211 created "2016-06-24" @default.
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- W2389758211 date "2016-05-10" @default.
- W2389758211 modified "2023-09-27" @default.
- W2389758211 title "Optimal Policies for Risk-Averse Electric Vehicle Charging with Spot Purchases" @default.
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