标题： 电动汽车充电需求的相干分层概率预测 显示英文标题

标题： Coherent Hierarchical Probabilistic Forecasting of Electric Vehicle Charging Demand

摘要： 电动汽车（EV）渗透率的不断提高显著改变了智能电网中的典型负荷曲线。随着快速充电技术的发展，电动汽车充电需求的波动性不断增加，这需要额外的灵活性来实现实时功率平衡。电动汽车充电需求的预测涉及对多个电动汽车充电站（EVCS）中高维时间序列动态的概率建模。 本文以分层概率的方式研究了多个EVCS的预测问题。对于每个充电站，训练了一种基于部分输入凸神经网络（PICNN）的深度学习模型，以预测次日充电需求的条件分布，从而避免了传统分位数回归模型中常见的分位数交叉问题。然后，使用可微凸优化层（DCL）来协调从分布中采样的情景，生成满足分层约束的一致情景。与传统的基于优化的分层协调方法相比，它在机器学习方法中学习了一个更好的权重矩阵，用于调整不同目标的预测结果。基于真实世界电动汽车充电数据的数值实验被用来证明所提出方法的有效性。 显示英文摘要

摘要： The growing penetration of electric vehicles (EVs) significantly changes typical load curves in smart grids. With the development of fast charging technology, the volatility of EV charging demand is increasing, which requires additional flexibility for real-time power balance. The forecasting of EV charging demand involves probabilistic modeling of high dimensional time series dynamics across diverse electric vehicle charging stations (EVCSs). This paper studies the forecasting problem of multiple EVCS in a hierarchical probabilistic manner. For each charging station, a deep learning model based on a partial input convex neural network (PICNN) is trained to predict the day-ahead charging demand's conditional distribution, preventing the common quantile crossing problem in traditional quantile regression models. Then, differentiable convex optimization layers (DCLs) are used to reconcile the scenarios sampled from the distributions to yield coherent scenarios that satisfy the hierarchical constraint. It learns a better weight matrix for adjusting the forecasting results of different targets in a machine-learning approach compared to traditional optimization-based hierarchical reconciling methods. Numerical experiments based on real-world EV charging data are conducted to demonstrate the efficacy of the proposed method.