The idea of using wind power to charge electric vehicles (EVs) has attracted more and more attention nowadays due to the potential in significantly reducing air pollution. However, this problem is challenging on account of the uncertainty in the wind power generation and the charging demand from the EVs. Simulation-based policy improvement (SBPI) has been an important method for decision-making in stochastic dynamic programming and, in particular, for charging decisions of EVs in microgrids. However, the problem of allocating the limited computing budget for the best decision-making in online applications is less discussed. We consider this important problem in this work and make the following three major contributions. First, we show that the significant uncertainty in wind power generation forecasting could make the policy that is the outcome of an SBPI worse than the base policy. Second, we apply two existing methods to address this issue, namely, the optimal computing budget allocation (OCBA) for maximizing the probability of correct selection (OCBA_PCS) and the OCBA for minimizing the expected opportunity cost (OCBA_EOC). The asymptotic optimality is briefly reviewed. Third, we numerically compare the performance of OCBA_PCS and OCBA_EOC with the equal allocation (EA), a principle-based method, and a stochastic scenario-based method on small-scale and large-scale experiments. This work sheds light on the EV charging decision in general. Note to Practitioners —Together with the growing adoption of EVs in modern societies, there goes the challenge of how to satisfy the charging demand. Given the high uncertainty both in the wind power generation and in the charging demand, it is important to make decisions online using up-to-date estimation on the renewable power generation and the charging demand. Simulation-based policy improvement (SBPI) is shown both theoretically and practically to be useful to improve a given base policy in various applications, including this EV charging problem. However, the high uncertainty in forecasting could sometimes make the output of SBPI worse than that of the base policy. In this work, we first use numerical experiments to demonstrate the risk for such scenarios. Then, we propose to use two computing budget allocation procedures to address this issue. The asymptotic optimality of both algorithms is briefly reviewed. We demonstrate their performance on numerical experiments when there are only several EVs and when there are 100 EVs.

中文翻译：

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不确定风电可将电动汽车充电成本降至最低的计算预算分配方法

由于具有显着减少空气污染的潜力，使用风能为电动汽车（EV）充电的想法如今已引起越来越多的关注。然而，由于风力发电的不确定性和电动汽车的充电需求，该问题具有挑战性。基于仿真的策略改进（SBPI）已成为用于随机动态规划中的决策的重要方法，尤其是对微电网中的EV的决策收取费用的一种重要方法。但是，很少讨论为在线应用程序中的最佳决策分配有限的计算预算的问题。我们在这项工作中考虑了这个重要问题，并做出了以下三个主要贡献。第一的，我们表明，风力发电预测中的显着不确定性可能会使作为SBPI结果的政策比基本政策更糟糕。其次，我们使用两种现有方法来解决此问题，即用于最大化正确选择概率的最佳计算预算分配（OCBA）（OCBA_PCS）和用于将预期机会成本最小化（OCBA_EOC）的OCBA。对渐近最优性进行了简要回顾。第三，在小规模和大规模实验中，我们将OCBA_PCS和OCBA_EOC的性能与均等分配（EA），基于原理的方法和基于随机情景的方法进行了数值比较。这项工作总体上阐明了电动汽车的充电决策。最佳计算预算分配（OCBA）用于最大化正确选择的可能性（OCBA_PCS），而OCBA用于最小化预期机会成本（OCBA_EOC）。对渐近最优性进行了简要回顾。第三，在小规模和大规模实验中，我们将OCBA_PCS和OCBA_EOC的性能与均等分配（EA），基于原理的方法和基于随机情景的方法进行了数值比较。这项工作总体上阐明了电动汽车的充电决策。最佳计算预算分配（OCBA）用于最大化正确选择的可能性（OCBA_PCS），而OCBA用于最小化预期机会成本（OCBA_EOC）。对渐近最优性进行了简要回顾。第三，在小规模和大规模实验中，我们将OCBA_PCS和OCBA_EOC的性能与均等分配（EA），基于原理的方法和基于随机情景的方法进行了数值比较。这项工作总体上阐明了电动汽车的充电决策。以及基于随机情景的小规模和大规模实验方法。这项工作总体上阐明了电动汽车的充电决策。以及基于随机情景的小规模和大规模实验方法。这项工作总体上阐明了电动汽车的充电决策。执业者须知 —随着现代社会对电动汽车的日益普及，如何满足充电需求也面临着挑战。考虑到风力发电和充电需求的高度不确定性，重要的是使用关于可再生能源发电和充电需求的最新估算在线做出决策。理论上和实践上都显示了基于仿真的策略改进（SBPI），可用于改进各种应用程序中的给定基本策略，包括此EV充电问题。但是，预测的高度不确定性有时会使SBPI的输出低于基本政策的输出。在这项工作中，我们首先使用数值实验来证明这种情况下的风险。然后，我们建议使用两种计算预算分配程序来解决此问题。简要回顾了两种算法的渐近最优性。当只有几个电动汽车和有100个电动汽车时，我们将在数值实验中证明其性能。