Quantifying flexibility in EV charging as DR potential: Analysis of two real-world data sets

Abstract

Abstract-The increasing adoption of electric vehicles (EVs) presents both challenges and opportunities for the power grid, especially for distribution system operators (DSOs). The demand represented by EVs can be significant, but on the other hand, sojourn times of EVs could be longer than the time required to charge their batteries to the desired level (e.g., to cover the next trip). The latter observation means that the electrical load from EVs is characterized by a certain level of flexibility, which could be exploited for example in demand response (DR) approaches (e.g., to balance generation from renewable energy sources).This paper analyzes two data sets, one from a charging-athome field trial in Flanders (about 8.5k charging sessions) and another from a large-scale EV public charging pole deployment in The Netherlands (more than 1M sessions). We rigorously analyze the collected data and quantify aforementioned flexibility:(1) we characterize the EV charging behavior by clustering the arrival and departure time combinations, identifying three behaviors (charging near home, charging near work, and park to charge), (2) we fit statistical models for the sojourn time, and flexibility (i.e., non-charging idle time) for each type of observed behavior, and (3) quantify the the potential of DR exploitation as the maximal load that could be achieved by coordinating EV charging for a given time of day t, continuously until t + ∆.

A data-driven statistical approach for extending electric vehicle charging infrastructure

“…We adopt a similar classification in our work, where chargers are classified as near work or near home (see Section 6.1). Develder et al also discovered different charging patterns for charging sessions happening during weekdays and weekends. While some interesting observations were made by Develder et al, the influence of key variables on charging behavior, such as the number of available charging stations in a region or the points of interest around charging stations, was not investigated.…”

“…There are many studies that deal with the problem of detecting EV owners' charging patterns. For example, Develder et al investigated EV owners' charging patterns using 2 different real‐world datasets belonging to different EVCI providers (ElaadNL and iMove). Based on the clustering of arrival and departure times of EVs to/from charging stations, charging sessions were classified into 3 categories: (1) parking to charge; (2) charging near home; and (3) charging near work.…”

Quantitive analysis of electric vehicle flexibility: A data-driven approach

“…This type of behavior is absent in the datasets collected from residential charging (e.g., iMove [28]). The park to charge cluster (62.86% of the total data) is the largest cluster and has arrivals/departures scattered throughout the 240 day with sojourns that last not much longer than the time required to charge the battery.…”

“…Note that this paper is a substantial extension of our work in [28] since we 170 now offer a more extensive analysis of the charging session characteristics and investigate the effect of seasonal changes and weekends on the characteristics of the charging sessions. Additionally, in [28], we quantized flexibility as the maximal load that could be deferred for a specific duration at any time of the day, independent of any DR scheme.…”

“…Additionally, in [28], we quantized flexibility as the maximal load that could be deferred for a specific duration at any time of the day, independent of any DR scheme. In other words, our previous analysis showed 175 the flexibility potential that is available for utilization and not the flexibility that would be utilized to meet various DR objectives.…”