原文信息：
Privacy-preserving coordination of power and transportation networks using spatiotemporal GAT for predicting EV charging demands
原文链接：
https://www.sciencedirect.com/science/article/pii/S0306261924017744
Highlights
•提出了一种预测电动汽车时空充电需求的时空图注意力模型。
•引入了增强排队论模型刻画充电排队行为。
•所提出的时空图注意力模型与增强排队论模型相结合，提高了电动汽车充电需求预测的准确性。
•所提模型在有限的信息交换下支撑了充分的电力-交通协同。
Abstract
The gradual replacement of conventional-fuel vehicles by electric vehicles (EVs) in recent years provides a growing incentive for the collaborative optimization of power distribution networks (PDNs) and urban transportation networks (UTNs). However, the implementation of a centralized optimization model for PDNs and UTNs is currently unrealistic due to the requirement for establishing information privacy barriers between the two independently operated networks. The present work proposes a predict-then-optimize (PTO) framework that combines Spatiotemporal Graph Attention Network (STGAT) with enhanced queueing theory to achieve accurate prediction of EV charging demands. Building upon this, it efficiently coordinates optimization between the PDN and UTN, while preserving the information privacy of both independently operated networks. Specifically, the proposed STGAT model predicts vehicle flow by simultaneously exploiting the dynamic temporal and spatial dependencies in the historical traffic data of the target UTN. It then converts the predicted flow into spatiotemporal EV charging demands using an enhanced queuing model that considers the service capacity constraints of charging stations (CSs) and the behavior of EV users. Subsequently, the predicted EV charging demands are integrated into a multi-period PDN scheduling model. The results of numerical computations based on an IEEE 33-bus PDN and real-world traffic flow datasets demonstrate that the scheduling results provided by the proposed approach differ by only 0.5% compared to results obtained when applying actual EV charging demands.
Keywords
Power-transportation coordination；
Graph attention network;
Prediction of EV charging demands；
Queuing theory；
Graphics
Fig. 1. Process flow of the proposed PTO framework.
Fig. 3. Conventional M/M/C/N queuing model for a CS.
Fig. 9. The average relative errors in predictions made for each time segment from 12:00 to 24:00 by different models: (a) STGAT; (b) LSTM.
Fig. 12. Comparisons of predicted EV charging loads at the four CSs across different time periods using different queuing models: (a) conventional queuing model; (b) enhanced queuing model.
团队简介
本研究由河海大学、澳门大学以及美国加州大学戴维斯分校的研究人员共同完成。
第一作者简介：
陈胜，河海大学电力工程系教授，入选中国科协青年人才托举工程，入选斯坦福全球前2%顶尖科学家榜单。从事综合能源系统、电力交通融合等研究，担任中国电工技术学会青年工作委员会委员。目前主持国家自然科学基金2项。在本学科主流期刊上发表学术论文90余篇(含一作IEEE汇刊14篇，4篇论文入选中信所F5000领跑者)，Scopus累计被引3600余次，H指数35。担任中科院一区期刊《Journal of Modern Power Systems and Clean Energy》编委，担任《电力自动化设备》、《浙江电力》等期刊青年编委。曾获电力建设科学技术进步一等奖(排2)、江苏省优秀博士学位论文等荣誉。
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