Capturing diversity in electric vehicle charging behaviour for network capacity estimation

Crozier, Constance; Morstyn, Thomas; McCulloch, Malcolm

doi:10.1016/j.trd.2021.102762

Cited by 28 publications

(15 citation statements)

References 39 publications

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“…The article by (Crozier et al, 2021) sought to detect diversity more accurately in individual consumer behavior for a better and more accurate estimates of charging loads. The model combines readily available travel survey data with highresolution data from an EV trial, using clustering and conditional probabilities.…”

Section: Related Work Ev Load Modelingmentioning

confidence: 99%

Optimal Sequential Distribution Planning for Low-Voltage Network With Electric Vehicle Loads

Sangob

Sirisumrannukul

2021

Front. Energy Res.

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There has been a growing presence of electric vehicles in many countries including Thailand, where many forms of incentives have been provided to build integrated infrastructure, and to encourage drivers to switch to electric vehicles (EVs). Because the immediate entry of EVs unavoidably can alter household load profiles, reinforcement on the existing system based on traditional planning may not be sufficient and can introduce over or under capital and operating expenditure over the time horizon. Therefore, if distribution systems are unreadily prepared for such an uptake, three obvious problems can be expected: 1) voltage regulation, 2) overloads of the distribution feeders and the distribution transformers, and 3) high energy loss. In this paper, an activity-based, time-sequential Monte Carlo Simulation algorithm was comprehensively developed for uncontrollable and smart charging, given annually updated information of EV locations and number of EVs, their energy consumption, hourly average vehicle speed, number of daily trips, travel distance per trip, size of EV batteries, time to arrive home and time to leave home. Minimizing the annual sum of investment and operating costs over a planning period could then be sequentially solved by a Particle Swarm Optimization (PSO) algorithm. The results from a practical 122-bus, 24 kV/400 V distribution system with different scenarios of uncontrollable and smart charging show that the sequential optimization embedded with deterministic decision can help improve customer voltage profile, keep feeder and transformer loading within acceptable operating limits and offer significant cost savings from energy loss. As far as a large number of low-voltage networks, and the associated large sum of cost savings are concerned, the proposed planning framework is practical to be applied and expected to be served as a new guideline for future implementation in Thailand.

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Section: Related Work Ev Load Modelingmentioning

confidence: 99%

Optimal Sequential Distribution Planning for Low-Voltage Network With Electric Vehicle Loads

Sangob

Sirisumrannukul

2021

Front. Energy Res.

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“…Rural areas are ideal for home charging due to the number of households with dedicated off-street parking space (driveway or garage), which can be difficult to find in the built up urban areas. Crozier et al (2021) examined various methods for modelling the variability of EV charging and categorised these methods into three groups: (1) bottom-up charging models applied to varied vehicle use, (2) stochastic bottom-up charging models applied to a fixed set of vehicle usage, and (3) top down stochastic charging models.…”

Section: Research Approachmentioning

confidence: 99%

“…Creating these models requires large amounts of data pertaining to the usage and charging of EVs. The variability in charging can be captured through the use of Monte Carlo simulations, but this approach can overestimate the peak aggregated charging demand when considering many agents (vehicles) together (Crozier et al, 2021). The third group directly models charging, rather than the relationship between vehicle use and charging.…”

Section: Research Approachmentioning

confidence: 99%

“…In other words, these are top-down models for EV charging. This approach is most suited to public charging investigations, where questions of charge point numbers and availabilities are the focus (Crozier et al, 2021). The model presented in this paper adopts a bottom-up charging model approach (group one) as the basis for the EV charging, the specifics of the conditions will be discussed in Section 3.…”

Section: Research Approachmentioning

confidence: 99%

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Rural EV charging: The effects of charging behaviour and electricity tariffs

2023

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“…Pevec et al (2018) used business data on charging infrastructure, geographic data, and driving distances to plan charging stations in the Netherlands. Based on travel survey data in combination with electric vehicle data recorded in a UK trial in 2016, Crozier et al (2021) modelled charging behaviour for network capacity estimation.…”

Section: Introductionmentioning

confidence: 99%

Spatial and temporal patterns of electric vehicle charging station utilization: a nationwide case study of Switzerland

Gellrich¹,

Block²,

Leikert-Böhm³

2022

Environ. Res.: Infrastruct. Sustain.

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The expansion of the public charging infrastructure for electric vehicles is seen as central to the development of electric mobility in many countries. Although national studies of charging infrastructure utilization based on real-world data would be a sound basis for demand planning, such studies are scarce. Using Switzerland as an example, this study examines the spatial and temporal patterns of charging infrastructure utilization. To this end, detailed, nationwide, real-time utilization data from 3,086 electric vehicle supply equipment units (EVSEs) at electric vehicle charging stations were collected over a period of several months and analyzed exploratively and statistically. The maximum average utilization rate of the EVSEs surveyed during the study period is between 14% and 16%, depending on the day of the week and time of day. Most charging occurs Monday through Friday during peak working hours and on Saturday during the day. The median utilization time is higher in the largest cities than the statewide average. Charging stations along major transit routes do not have higher utilization rates than in other locations. The results suggest thatpublic charging infrastructure is used primarily in cities and agglomeration during work hours. The findings from this study may help plan and make better use of funding to expand charging infrastructure.

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Capturing diversity in electric vehicle charging behaviour for network capacity estimation

Cited by 28 publications

References 39 publications

Optimal Sequential Distribution Planning for Low-Voltage Network With Electric Vehicle Loads

Optimal Sequential Distribution Planning for Low-Voltage Network With Electric Vehicle Loads

Rural EV charging: The effects of charging behaviour and electricity tariffs

Spatial and temporal patterns of electric vehicle charging station utilization: a nationwide case study of Switzerland

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