Charging Load Prediction and Distribution Network Reliability Evaluation Considering Electric Vehicles’ Spatial-Temporal Transfer Randomness

Charging Load Prediction and Distribution Network Reliability Evaluation Considering Electric Vehicles’ Spatial-Temporal Transfer Randomness

Integration of large-scale cluster electric vehicles (EVs) and their spatial-temporal transfer randomness are likely to affect the safety and economic operation of the distribution network. This paper investigates the spatial-temporal distribution prediction of EVs’ charging load and then...

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Autores principales: Shan Cheng, Zhaobin Wei, Dongdong Shang, Zikai Zhao, Huiming Chen
Formato: article
Lenguaje:EN
Publicado: IEEE 2020
Materias:
Electric vehicle
reliability evaluation
trip chain technology
Markov decision process
sequential Monte Carlo method
spatial-temporal transfer randomness
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Acceso en línea:https://doaj.org/article/93f26a9409f5463ab38657c5eec9e453
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spelling oai:doaj.org-article:93f26a9409f5463ab38657c5eec9e4532021-11-19T00:04:12ZCharging Load Prediction and Distribution Network Reliability Evaluation Considering Electric Vehicles’ Spatial-Temporal Transfer Randomness2169-353610.1109/ACCESS.2020.3006093https://doaj.org/article/93f26a9409f5463ab38657c5eec9e4532020-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9129725/https://doaj.org/toc/2169-3536Integration of large-scale cluster electric vehicles (EVs) and their spatial-temporal transfer randomness are likely to affect the safety and economic operation of the distribution network. This paper investigates the spatial-temporal distribution prediction of EVs’ charging load and then evaluates the reliability of the distribution network penetrated with large-scale cluster EVs. To effectively predict the charging load, trip chain technology, Monte Carlo method and Markov decision process (MDP) theory are employed. Moreover, a spatial-temporal transfer model of EVs is established, and based on which, an EV energy consumption model and a charging load prediction model are constructed with consideration of temperature, traffic condition and EV owner’s subjective willingness in different scenarios. With the application of sequential Monte Carlo method, the paper further evaluates distribution network reliability in various charging scenarios. In the evaluation, indices including per unit value (PUV), fast voltage stability index (FVSI), loss of load probability (LOLP), system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), and expected energy not supplied (EENS) are incorporated. To validate the proposed prediction model and evaluation method, a series of numerical simulations are conducted on the basis of taking the traffic-distribution system of a typical city as an example. The result demonstrates that the proposed spatial-temporal transfer model is more practical in charging load prediction than the popularly used Dijkstra’s shortest path algorithm. Moreover, high temperature, congestion and the increment of EV penetration rate will further weaken distribution network reliability.Shan ChengZhaobin WeiDongdong ShangZikai ZhaoHuiming ChenIEEEarticleElectric vehiclereliability evaluationtrip chain technologyMarkov decision processsequential Monte Carlo methodspatial-temporal transfer randomnessElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 8, Pp 124084-124096 (2020)
institution DOAJ
collection DOAJ
language EN
topic Electric vehicle
reliability evaluation
trip chain technology
Markov decision process
sequential Monte Carlo method
spatial-temporal transfer randomness
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
spellingShingle Electric vehicle
reliability evaluation
trip chain technology
Markov decision process
sequential Monte Carlo method
spatial-temporal transfer randomness
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Shan Cheng
Zhaobin Wei
Dongdong Shang
Zikai Zhao
Huiming Chen
Charging Load Prediction and Distribution Network Reliability Evaluation Considering Electric Vehicles’ Spatial-Temporal Transfer Randomness
description Integration of large-scale cluster electric vehicles (EVs) and their spatial-temporal transfer randomness are likely to affect the safety and economic operation of the distribution network. This paper investigates the spatial-temporal distribution prediction of EVs’ charging load and then evaluates the reliability of the distribution network penetrated with large-scale cluster EVs. To effectively predict the charging load, trip chain technology, Monte Carlo method and Markov decision process (MDP) theory are employed. Moreover, a spatial-temporal transfer model of EVs is established, and based on which, an EV energy consumption model and a charging load prediction model are constructed with consideration of temperature, traffic condition and EV owner’s subjective willingness in different scenarios. With the application of sequential Monte Carlo method, the paper further evaluates distribution network reliability in various charging scenarios. In the evaluation, indices including per unit value (PUV), fast voltage stability index (FVSI), loss of load probability (LOLP), system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), and expected energy not supplied (EENS) are incorporated. To validate the proposed prediction model and evaluation method, a series of numerical simulations are conducted on the basis of taking the traffic-distribution system of a typical city as an example. The result demonstrates that the proposed spatial-temporal transfer model is more practical in charging load prediction than the popularly used Dijkstra’s shortest path algorithm. Moreover, high temperature, congestion and the increment of EV penetration rate will further weaken distribution network reliability.
format article
author Shan Cheng
Zhaobin Wei
Dongdong Shang
Zikai Zhao
Huiming Chen
author_facet Shan Cheng
Zhaobin Wei
Dongdong Shang
Zikai Zhao
Huiming Chen
author_sort Shan Cheng
title Charging Load Prediction and Distribution Network Reliability Evaluation Considering Electric Vehicles’ Spatial-Temporal Transfer Randomness
title_short Charging Load Prediction and Distribution Network Reliability Evaluation Considering Electric Vehicles’ Spatial-Temporal Transfer Randomness
title_full Charging Load Prediction and Distribution Network Reliability Evaluation Considering Electric Vehicles’ Spatial-Temporal Transfer Randomness
title_fullStr Charging Load Prediction and Distribution Network Reliability Evaluation Considering Electric Vehicles’ Spatial-Temporal Transfer Randomness
title_full_unstemmed Charging Load Prediction and Distribution Network Reliability Evaluation Considering Electric Vehicles’ Spatial-Temporal Transfer Randomness
title_sort charging load prediction and distribution network reliability evaluation considering electric vehicles’ spatial-temporal transfer randomness
publisher IEEE
publishDate 2020
url https://doaj.org/article/93f26a9409f5463ab38657c5eec9e453
work_keys_str_mv AT shancheng chargingloadpredictionanddistributionnetworkreliabilityevaluationconsideringelectricvehiclesx2019spatialtemporaltransferrandomness
AT zhaobinwei chargingloadpredictionanddistributionnetworkreliabilityevaluationconsideringelectricvehiclesx2019spatialtemporaltransferrandomness
AT dongdongshang chargingloadpredictionanddistributionnetworkreliabilityevaluationconsideringelectricvehiclesx2019spatialtemporaltransferrandomness
AT zikaizhao chargingloadpredictionanddistributionnetworkreliabilityevaluationconsideringelectricvehiclesx2019spatialtemporaltransferrandomness
AT huimingchen chargingloadpredictionanddistributionnetworkreliabilityevaluationconsideringelectricvehiclesx2019spatialtemporaltransferrandomness
_version_ 1718420680111816704

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