Capturing diversity in electric vehicle charging behaviour for network capacity estimation
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 di...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
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Elsevier
2021
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| _version_ | 1811139630825734144 |
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| author | Crozier, C Morstyn, T McCulloch, M |
| author_facet | Crozier, C Morstyn, T McCulloch, M |
| author_sort | Crozier, C |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-09-25T04:09:09Z |
| format | Journal article |
| id | oxford-uuid:91a80f4a-3702-4925-9953-1023202b0b8e |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:09:09Z |
| publishDate | 2021 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:91a80f4a-3702-4925-9953-1023202b0b8e2024-06-12T15:15:13ZCapturing diversity in electric vehicle charging behaviour for network capacity estimationJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:91a80f4a-3702-4925-9953-1023202b0b8eEnglishSymplectic ElementsElsevier2021Crozier, CMorstyn, TMcCulloch, MThis 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. |
| spellingShingle | Crozier, C Morstyn, T McCulloch, M Capturing diversity in electric vehicle charging behaviour for network capacity estimation |
| title | Capturing diversity in electric vehicle charging behaviour for network capacity estimation |
| title_full | Capturing diversity in electric vehicle charging behaviour for network capacity estimation |
| title_fullStr | Capturing diversity in electric vehicle charging behaviour for network capacity estimation |
| title_full_unstemmed | Capturing diversity in electric vehicle charging behaviour for network capacity estimation |
| title_short | Capturing diversity in electric vehicle charging behaviour for network capacity estimation |
| title_sort | capturing diversity in electric vehicle charging behaviour for network capacity estimation |
| work_keys_str_mv | AT crozierc capturingdiversityinelectricvehiclechargingbehaviourfornetworkcapacityestimation AT morstynt capturingdiversityinelectricvehiclechargingbehaviourfornetworkcapacityestimation AT mccullochm capturingdiversityinelectricvehiclechargingbehaviourfornetworkcapacityestimation |