Coherent Hierarchical Probabilistic Forecasting of Electric Vehicle Charging Demand

TL;DR

This paper presents a hierarchical probabilistic forecasting method for EV charging demand using deep learning and convex optimization, improving scenario coherence and accuracy for real-time grid management.

Contribution

It introduces a novel deep learning framework with convex optimization layers for hierarchical EV demand forecasting, addressing quantile crossing and scenario reconciliation issues.

Findings

The proposed method outperforms traditional approaches in accuracy.

It ensures coherent hierarchical scenarios respecting constraints.

Demonstrates effectiveness on real-world EV charging data.

Abstract

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.