Probabilistic coupled EV‐PV hosting capacity analysis in LV networks with spatio‐temporal modelling and copula theory
Abstract The authors present an innovative approach for probabilistic coupled electric vehicle (EV) and solar photovoltaics (PV) hosting capacity analysis in low‐voltage (LV) distribution networks. The challenges posed by system uncertainties and correlations between different parameters, such as PV...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Smart Grid |
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| Online Access: | https://doi.org/10.1049/stg2.12189 |
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| author | Chathuranga D. W. Wanninayaka Mudiyanselage Kazi N. Hasan Arash Vahidnia Mir Toufikur Rahman |
| author_facet | Chathuranga D. W. Wanninayaka Mudiyanselage Kazi N. Hasan Arash Vahidnia Mir Toufikur Rahman |
| author_sort | Chathuranga D. W. Wanninayaka Mudiyanselage |
| collection | DOAJ |
| description | Abstract The authors present an innovative approach for probabilistic coupled electric vehicle (EV) and solar photovoltaics (PV) hosting capacity analysis in low‐voltage (LV) distribution networks. The challenges posed by system uncertainties and correlations between different parameters, such as PV generation and EV charging demand, are addressed using probabilistic modelling. To appropriately incorporate the geographical distribution and time‐variant patterns of EV charging demand, a comprehensive spatio‐temporal (ST) model is developed to capture the trip distance, EV arrival, and charging time. The correlation between the PV generation and EV charging demand is effectively captured by copula theory. The proposed models have been validated using actual EV charging and PV generation data from 36 Australian EV users over 1 year. Power flow simulation with actual data and modelled data have identified EV‐only and coupled EV‐PV hosting capacities in an Australian LV test network. The coupled EV‐PV model presents a higher level of accuracy, having an average mean absolute percentage error (MAPE) of 5.97% compared to independent EV profiles having a MAPE of 10.12%. A voltage profile analysis with the EV and PV profiles also validates the same trend, having MAPE of 1.5% and 1.95%, respectively, for coupled EV‐PV and independent EV profiles. |
| format | Article |
| id | doaj-art-b03cbec8d66245d692aea370ee93d496 |
| institution | DOAJ |
| issn | 2515-2947 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Smart Grid |
| spelling | doaj-art-b03cbec8d66245d692aea370ee93d4962025-08-20T02:53:16ZengWileyIET Smart Grid2515-29472024-12-017691792810.1049/stg2.12189Probabilistic coupled EV‐PV hosting capacity analysis in LV networks with spatio‐temporal modelling and copula theoryChathuranga D. W. Wanninayaka Mudiyanselage0Kazi N. Hasan1Arash Vahidnia2Mir Toufikur Rahman3School of Engineering RMIT University Melbourne Victoria AustraliaSchool of Engineering RMIT University Melbourne Victoria AustraliaSchool of Engineering RMIT University Melbourne Victoria AustraliaSchool of Engineering RMIT University Melbourne Victoria AustraliaAbstract The authors present an innovative approach for probabilistic coupled electric vehicle (EV) and solar photovoltaics (PV) hosting capacity analysis in low‐voltage (LV) distribution networks. The challenges posed by system uncertainties and correlations between different parameters, such as PV generation and EV charging demand, are addressed using probabilistic modelling. To appropriately incorporate the geographical distribution and time‐variant patterns of EV charging demand, a comprehensive spatio‐temporal (ST) model is developed to capture the trip distance, EV arrival, and charging time. The correlation between the PV generation and EV charging demand is effectively captured by copula theory. The proposed models have been validated using actual EV charging and PV generation data from 36 Australian EV users over 1 year. Power flow simulation with actual data and modelled data have identified EV‐only and coupled EV‐PV hosting capacities in an Australian LV test network. The coupled EV‐PV model presents a higher level of accuracy, having an average mean absolute percentage error (MAPE) of 5.97% compared to independent EV profiles having a MAPE of 10.12%. A voltage profile analysis with the EV and PV profiles also validates the same trend, having MAPE of 1.5% and 1.95%, respectively, for coupled EV‐PV and independent EV profiles.https://doi.org/10.1049/stg2.12189data analysiselectric vehicle energy managementemerging technologies in smart gridsprobabilitystatistical analysis |
| spellingShingle | Chathuranga D. W. Wanninayaka Mudiyanselage Kazi N. Hasan Arash Vahidnia Mir Toufikur Rahman Probabilistic coupled EV‐PV hosting capacity analysis in LV networks with spatio‐temporal modelling and copula theory IET Smart Grid data analysis electric vehicle energy management emerging technologies in smart grids probability statistical analysis |
| title | Probabilistic coupled EV‐PV hosting capacity analysis in LV networks with spatio‐temporal modelling and copula theory |
| title_full | Probabilistic coupled EV‐PV hosting capacity analysis in LV networks with spatio‐temporal modelling and copula theory |
| title_fullStr | Probabilistic coupled EV‐PV hosting capacity analysis in LV networks with spatio‐temporal modelling and copula theory |
| title_full_unstemmed | Probabilistic coupled EV‐PV hosting capacity analysis in LV networks with spatio‐temporal modelling and copula theory |
| title_short | Probabilistic coupled EV‐PV hosting capacity analysis in LV networks with spatio‐temporal modelling and copula theory |
| title_sort | probabilistic coupled ev pv hosting capacity analysis in lv networks with spatio temporal modelling and copula theory |
| topic | data analysis electric vehicle energy management emerging technologies in smart grids probability statistical analysis |
| url | https://doi.org/10.1049/stg2.12189 |
| work_keys_str_mv | AT chathurangadwwanninayakamudiyanselage probabilisticcoupledevpvhostingcapacityanalysisinlvnetworkswithspatiotemporalmodellingandcopulatheory AT kazinhasan probabilisticcoupledevpvhostingcapacityanalysisinlvnetworkswithspatiotemporalmodellingandcopulatheory AT arashvahidnia probabilisticcoupledevpvhostingcapacityanalysisinlvnetworkswithspatiotemporalmodellingandcopulatheory AT mirtoufikurrahman probabilisticcoupledevpvhostingcapacityanalysisinlvnetworkswithspatiotemporalmodellingandcopulatheory |