The demand for vehicle charging will require large investments in power distribution, transmission, and generation. However, this demand is often also flexible in time, and can be actively managed to reduce the needed investments, and to better integrate renewable electricity. Harnessing this flexibility requires forecasting and controlling electric vehicle (EV) charging at thousands of stations. This paper addresses the problem of forecasting and management of the aggregate flexible demand from tens to thousands of EV supply equipment (EVSEs). First, it presents an equivalent time-variant storage model for flexible demand at an aggregation of EVSEs. The proposed model is generalizable to different markets, and also to different flexible loads. Model parameters representing multiple EVSEs can be easily aggregated by summation, and forecasted using autoregressive models. The forecastability of uncontrolled demand and storage parameters is evaluated using data from 1341 nonresidential EVSEs located in Northern California. The median coefficient of variation is as low as 24% for the forecast of uncontrolled demand at the highest aggregation and 10-15% for the storage parameters. The benefits of aggregation and forecastability are demonstrated using an energy arbitrage scenario. Purchasing energy day ahead is less expensive than in the real-time market, but relies on a uncertain forecast of charging availability. The results show that the forecastability significantly improves for larger aggregations. This helps the aggregator make a better forecast, and decreases the cost of charging in comparison to an uncontrolled case by 60% with respect to an oracle scenario.

中文翻译：

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利用电动汽车灵活性的等效时变存储模型：预测和聚合


汽车充电的需求将需要在配电、输电和发电方面进行大量投资。然而，这种需求在时间上通常也是灵活的，并且可以积极管理以减少所需的投资，并更好地整合可再生电力。利用这种灵活性需要预测和控制数千个充电站的电动汽车 (EV) 充电。本文解决了对数十到数千个电动汽车供电设备（EVSE）的总灵活需求进行预测和管理的问题。首先，它提出了一种等效的时变存储模型，用于 EVSE 聚合的灵活需求。所提出的模型可推广到不同的市场以及不同的灵活负载。代表多个 EVSE 的模型参数可以通过求和轻松聚合，并使用自回归模型进行预测。使用来自北加州 1341 个非住宅 EVSE 的数据评估不受控制的需求和存储参数的可预测性。对于最高聚合时不受控制的需求的预测，中位变异系数低至 24%，对于存储参数，中位变异系数低至 10-15%。使用能源套利场景证明了聚合和可预测性的好处。提前一天购买能源比实时市场便宜，但依赖于充电可用性的不确定预测。结果表明，较大聚合的可预测性显着提高。这有助于聚合器做出更好的预测，并且在预言机场景中，与不受控制的情况相比，收费成本降低了 60%。