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首页> 外文期刊>Smart Grid, IEEE Transactions on >Vehicle-to-Grid Regulation Reserves Based on a Dynamic Simulation of Mobility Behavior
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Vehicle-to-Grid Regulation Reserves Based on a Dynamic Simulation of Mobility Behavior

机译：基于机动行为动态仿真的车辆到电网监管储备

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摘要

This study establishes a new approach to analyzing the economic impacts of vehicle-to-grid (V2G) regulation reserves by simulating the restrictions arising from unpredictable mobility requests by vehicle users. A case study for Germany using average daily values (in the following also called the “static” approach) and a dynamic simulation including different mobility use patterns are presented. Comparing the dynamic approach with the static approach reveals a significant difference in the power a vehicle can offer for ancillary services and provides insights into the necessary size of vehicle pools and possible adaptations required in the regulation market to render V2G feasible. In the static approach it is shown that negative secondary control is economically the most beneficial for electric vehicles because it offers the highest potential for charging with “low-priced” energy from negative regulation reserves. A Monte Carlo simulation using stochastic mobility behavior results in a 40% reduction of the power available for regulation compared to the static approach. Because of the high value of power in the regulation market, this finding has a strong impact on the resulting revenues. Further, we demonstrate that, for the data used, a pool size of 10 000 vehicles seems reasonable to balance the variation in each individual''s driving behavior. In the case of the German regulation market, which uses monthly bids, a daily or hourly bid period is recommended. This adaptation would be necessary to provide individual regulation assuming that the vehicles are primarily used for mobility reasons and cannot deliver the same amount of power every hour of the week.
机译：这项研究建立了一种新的方法，通过模拟车辆用户不可预测的出行需求而产生的限制，来分析车辆到电网（V2G）法规储备的经济影响。提出了一个针对德国使用每日平均价值的案例研究（以下也称为“静态”方法），并进行了包括不同出行方式的动态模拟。将动态方法与静态方法进行比较，可以揭示出车辆可提供的辅助服务能力上的显着差异，并且可以洞悉必要的车辆池大小以及监管市场为使V2G可行而可能需要的调整。在静态方法中，表明负次级控制在经济上对电动汽车最为有利，因为它具有最大的潜力，可利用负调节储备中的“低价”能量进行充电。与静态方法相比，使用随机迁移率行为的蒙特卡洛模拟可将可用于调节的功率降低40％。由于监管市场中的权力具有很高的价值，这一发现对由此产生的收入产生了重大影响。此外，我们证明，对于所使用的数据，一个10 000辆汽车的水池大小似乎是合理的，可以平衡每个人驾驶行为的变化。对于使用月度投标的德国监管市场，建议每天或每小时投标。假设车辆主要是出于机动性原因而不能在一周中的每个小时提供相同量的动力，则此调整对于提供单独的法规将是必要的。

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