预测用于聚合EV电力需求响应计划的共享场所中的车辆停放行为
Predicting vehicles parking behaviour in shared premises for aggregated EV electricity demand response programs
摘要：
2020年全球电动汽车销量继续超出预期，攀升至300多万辆，市场份额超过4%。然而，可再生能源的更高渗透率以及电动汽车（EV）的出现及其额外电力需求所导致的发电不确定性可能会在配电和输电层面对电力系统造成压力。需求响应聚合和负载控制将提高电网稳定性，并使可再生能源更好地渗透到电网中。目前的工作适合这种情况下，在停车场的EV充电优化支持假设在系统中的电动汽车的现任高渗透。我们提出了一种方法来预测估计的停车时间在共享停车场的目标是估计一个特定的停车场的能源需求，评估最佳的电动汽车充电时间表，并集成到一个智能控制器的调度。我们将预测问题形式化为有监督的机器学习任务，以预测汽车离开插槽之前停车事件的持续时间。该预测的持续时间馈送给能量管理系统，该能量管理系统将在该持续时间内分配功率，从而降低总体峰值电力需求。我们设计了两个研究问题的实验，旨在发现所提出的机器学习方法的准确性和预测模型的最相关特征。我们对来自意大利和巴西2个不同校园设施的4个数据集进行了不同的算法和特征组合实验。使用上下文和一天中的时间特征，与基于频率的统计分析相比，模型的总体结果显示出更高的准确性，这表明为共享停车场能源管理系统开发准确预测器的可行路线
英文摘要：
The global electric car sales in 2020 continued to exceed the expectations climbing to over 3 millions and reaching a market share of over 4%. However, uncertainty of generation caused by higher penetration of renewable energies and the advent of Electrical Vehicles (EV) with their additional electricity demand could cause strains to the power system, both at distribution and transmission levels. Demand response aggregation and load control will enable greater grid stability and greater penetration of renewable energies into the grid. The present work fits this context in supporting charging optimization for EV in parking premises assuming a incumbent high penetration of EVs in the system. We propose a methodology to predict an estimation of the parking duration in shared parking premises with the objective of estimating the energy requirement of a specific parking lot, evaluate optimal EVs charging schedule and integrate the scheduling into a smart controller. We formalize the prediction problem as a supervised machine learning task to predict the duration of the parking event before the car leaves the slot. This predicted duration feeds the energy management system that will allocate the power over the duration reducing the overall peak electricity demand. We structure our experiments inspired by two research questions aiming to discover the accuracy of the proposed machine learning approach and the most relevant features for the prediction models. We experiment different algorithms and features combination for 4 datasets from 2 different campus facilities in Italy and Brazil. Using both contextual and time of the day features, the overall results of the models shows an higher accuracy compared to a statistical analysis based on frequency, indicating a viable route for the development of accurate predictors for sharing parking premises energy management systems
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