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Optimization of Battery Charging and Purchasing at Electric Vehicle Battery Swap Stations

机译:电动汽车电池交换站的电池充电和购买优化

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An operator of a network of battery swap stations for electric vehicles must make a long-term investment decision on the number of batteries and charging bays in the system and periodic short-term decisions on when and how many batteries to recharge. Both decisions must be made concurrently, because there exists a trade-off between the long-term investment in batteries and charging bays, and short-term expenses for operating the system. Costs for electric energy as well as demand rates for batteries are stochastic: We consider an infinite time horizon for operation of the system. We derive an optimization problem, which cannot be solved optimally in a reasonable time for real world instances. By optimally solving various small problem instances, we show the mechanics of the model and the influence of its parameters on the optimal cost. We then develop a near-optimal solution heuristic based on Monte Carlo sampling following the ideas of approximate dynamic programming for the infinite horizon dynamic program. We show that operating battery swap stations in a network where lateral transshipments are allowed can substantially decrease expected operating costs.
机译:电动汽车电池交换站网络的运营商必须对系统中电池和充电槽的数量做出长期投资决定,并就何时和多少电池进行充电定期做出短期决定。这两个决定必须同时进行,因为在电池和充电仓的长期投资与操作系统的短期支出之间需要权衡取舍。电能成本和电池需求率是随机的:我们认为系统运行的时间范围是无限的。我们得出一个优化问题,对于现实世界的实例,它不能在合理的时间内最佳地解决。通过最优地解决各种小问题实例,我们展示了模型的机制及其参数对最优成本的影响。然后,我们遵循无限地平线动态程序的近似动态程序设计思想,基于蒙特卡洛采样法开发了一种接近最优的启发式方法。我们表明,在允许横向转运的网络中运行电池交换站可以大大降低预期的运行成本。

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