Data-Driven Approach for Analyzing Spatiotemporal Price Elasticities of EV Public Charging Demands Based on Conditional Random Fields

被引:20
作者
Bao, Zhiyuan [1 ]
Hu, Zechun [1 ]
Kammen, Daniel M. [2 ]
Su, Yifan [1 ]
机构
[1] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China
[2] Univ Calif Berkeley, Energy & Resources Grp, Berkeley, CA 94720 USA
基金
中国国家自然科学基金;
关键词
Elasticity; Charging stations; Pricing; Electric vehicle charging; Correlation; Real-time systems; Vehicles; Electric vehicle; charging demands; price elasticity; charging price; conditional random field; STATIONS; STRATEGY;
D O I
10.1109/TSG.2021.3080460
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
With the increase of electric vehicle (EV) sales, the pricing strategies of public charging stations have significant impacts on their revenues and the spatiotemporal distribution of charging loads. In this paper, we quantify three kinds of price elasticity of charging demands based on the historical charging data of multiple public charging stations with different pricing schemes. The relationship between the volume-weighted average price (VWAP) and the corresponding total charging demand within a zone is studied, which does not require changing the charging prices to estimate elasticity. To evaluate the shifting of charging demands in different periods and zones, a conditional random field (CRF) model is built, which depicts the spatiotemporal correlations of charging demands. In this model, the VWAPs and the total charging demands are taken as observed variables and hidden variables, respectively. The loopy belief propagation algorithm is used to infer the loopy graph approximately, and the learning algorithm with forgetting factors is used to estimate the unknown parameters of the CRF model. The price elasticities are derived from CRF, and the elasticity matrices of charging demands are obtained. Computational results based on historical charging data verify the validity of the proposed model and method.
引用
收藏
页码:4363 / 4376
页数:14
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