用分层概率模型预测电动车充电需求，避免传统方法的量化交叉问题。
Coherent Hierarchical Probabilistic Forecasting of Electric Vehicle Charging Demand
- 基于部分输入凸神经网络构建每日充电需求分布预测模型。
- 通过可微凸优化层生成满足层级约束的一致性场景，提升预测一致性。
- 适用于电网调度与充电站规划，尤其适合高波动性充电需求场景。
随着电动汽车（EV）渗透率上升，智能电网的典型负荷曲线发生显著变化。快速充电技术的发展使电动车充电需求波动加剧，对实时电力平衡提出更高要求。电动车充电需求预测需对多个充电站的高维时间序列动态进行概率建模。本文研究了多充电站的分层概率预测问题：针对每个充电站，采用基于部分输入凸神经网络（PICNN）的深度学习模型，预测日前充电需求的条件分布，有效避免传统分位数回归中的量化交叉问题；随后，利用可微凸优化层（DCLs）对分布采样得到的场景进行协调，生成满足层级约束的一致性场景，学习更优的权重矩阵以调整不同目标的预测结果，相比传统优化方法更具机器学习优势。基于真实世界电动车充电数据的数值实验验证了所提方法的有效性。
原文摘要 · Abstract (English)
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.
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