Capturing diversity in electric vehicle charging behaviour for network capacity estimation. (April 2021)

• Record Type: Journal Article
• Authors: Crozier, Constance; Morstyn, Thomas; McCulloch, Malcolm
• Is Part Of: Transportation research. Volume 93(2021)
• Publication Date: 2021-04
• Subjects: Clustering -- Demand forecasting -- Electric vehicle charging -- Stochastic modelling; Transportation -- Research -- Periodicals; Transportation -- Environmental aspects -- Periodicals; 354.76
• DOI: 10.1016/j.trd.2021.102762
• Languages: English
• ISSNs: 1361-9209

Abstract

Highlights: Diversity in EV charging affects the scale of required distribution network upgrades. Existing approaches to model charging behaviour may overestimate peak demand by 50 Regional vehicle use has a strong impact on a network's EV charging capacity. Abstract: This paper proposes a stochastic data-driven model for uncontrolled charging that accurately captures diversity in individual consumer behaviour. This is important because understanding the diversity between consumers is necessary to accurately estimate the number of electric vehicles' charging a distribution network could support without reinforcements. The model combines readily available travel survey data with high resolution data from an electric vehicle trial, using clustering and conditional probabilities. We demonstrate through a case study of UK residential charging that existing approaches may overestimate the increase in peak distribution network demand by 50%, which has implications for assessing the cost of network investments required. We also show that the peak charging demand varies regionally from 0.2–1.4 kW per household, demonstrating the importance of using locally representative vehicle usage data.