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首页> 外文会议>ASME international mechanical engineering congress and exposition >QUANTIFYING THE EFFECT OF PLUG-IN ELECTRIC VEHICLES ON FUTURE GRID OPERATIONS AND ANCILLARY SERVICE PROCUREMENT REQUIREMENTS
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QUANTIFYING THE EFFECT OF PLUG-IN ELECTRIC VEHICLES ON FUTURE GRID OPERATIONS AND ANCILLARY SERVICE PROCUREMENT REQUIREMENTS

机译：量化插电式电动汽车对未来电网运行和辅助服务采购要求的影响

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As plug-in electric vehicles (PEVs) grow in popularity, there is increasing research interest in the interaction between PEVs and the electric grid. Much of the previous work in the literature relies on an assumption that PEV charging will be scheduled, and that the duration and magnitude of charging loads can be modulated to suit the needs of the utility and the system operator. While access to the data or owner input necessary for charge scheduling and management might be technically feasible today, it is unclear whether vehicle owners will be amenable to providing these data or accepting utility control of their charging choices. Because of these uncertainties in the future relationship between electric utilities and PEV owners, this study seeks to examine the market effects of vehicles in the absence of the additional data utilities would need to realize these alternate, "optimal" PEV charging scenarios. In particular, this study focuses on quantifying the potential uncertainty in vehicle charging loads on an energy and power basis. Monte Carlo methods were applied to vehicle trip data from the National Household Travel Survey (NHTS) to generate simulated driving profiles for individual vehicles. Using these profiles, six PEV fleet sizes were studied, ranging from 1,000 to 500,000 vehicles, to determine whether fleet size had a linear effect on the stochasticity of vehicle charging loads. Following the Monte Carlo simulations, these loads were examined independent of and compared to net load (load minus wind generation). Results from the Monte Carlo simulations indicate that even for the largest PEV fleet sizes studied, variability in average charging loads is on the order of 10 MW, less than 0.2% of the magnitude of charging load for those fleet sizes. In comparison with electricity demand in the Electric Reliability Council of Texas' (ERCOT) operating area, these charging loads represent a 1% increase above typical summer peak loads. Unfortunately, while the relative increase in demand is small, the timing of peak charging load is nearly coincident with summer peak demand. The simulation approach was validated by comparing the results against empirical vehicle charging data collected by the Pecan Street Research Consortium from households in Austin, Texas. Simulated and empirical vehicle charging trends showed generally good agreement, with similar charging times but slightly different charging durations. The alignment between the two charging profiles indicates that the simulation methodology applied here with NHTS travel data can be used to predict electric load for vehicle charging when empirical historical charging data are not available.
机译：随着插电式电动汽车（PEV）的普及，人们对PEV与电网之间的相互作用的研究兴趣日益增加。文献中的许多以前的工作都基于这样一个假设：将对PEV充电进行计划，并且可以调节充电负载的持续时间和大小，以适应公用事业和系统运营商的需求。虽然在今天在技术上可行的是访问用于充电调度和管理所需的数据或所有者输入，但是尚不清楚车辆所有者是否愿意提供这些数据或接受其充电选择的公用事业控制。由于电力公司和PEV所有者之间的未来关系存在不确定性，因此本研究旨在研究在没有其他数据工具的情况下车辆的市场影响，而公用事业则需要实现这些替代的“最佳” PEV充电方案。尤其是，本研究着重于基于能量和功率来量化车辆充电负载中的潜在不确定性。蒙特卡洛方法已应用于来自全国家庭出行调查（NHTS）的车辆出行数据，以生成单个车辆的模拟行驶曲线。使用这些配置文件，研究了六种PEV车队规模，范围从1,000到500,000辆，以确定车队规模是否对车辆充电负荷的随机性产生线性影响。在进行蒙特卡洛模拟之后，独立于净负荷（负荷减去风力）来检查这些负荷。蒙特卡洛模拟的结果表明，即使对于所研究的最大PEV车队规模，平均充电负荷的变化仍在10 MW的数量级，不到这些车队规模的充电负荷量的0.2％。与德克萨斯州电力可靠性委员会（ERCOT）操作区域中的电力需求相比，这些充电负荷比典型的夏季高峰负荷​​高1％。不幸的是，尽管需求的相对增加很小，但高峰充电负荷的时间几乎与夏季高峰需求一致。通过将结果与美国山核桃街研究协会从得克萨斯州奥斯丁的家庭收集的经验性汽车充电数据进行比较，验证了该模拟方法的有效性。模拟和实证的车辆充电趋势总体上显示出良好的一致性，充电时间相似，但充电时间略有不同。这两个充电曲线之间的一致性表明，当经验历史充电数据不可用时，此处结合NHTS行驶数据应用的模拟方法可用于预测车辆充电的电负荷。

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