Data-driven robust planning of electric vehicle charging infrastructure for urban residential car parks
The number of electric vehicles (EVs) is expected to grow significantly, which calls for effective planning of charging infrastructures. While the planning of the charging infrastructure relies on an accurate charging demands, the behaviours of EVs charging are not always predictable and can be sens...
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sg-ntu-dr.10356-1602042022-07-15T05:41:38Z Data-driven robust planning of electric vehicle charging infrastructure for urban residential car parks Yan, Ziming Zhao, Tianyang Xu, Yan Koh, Leong Hai Go, Jonathan Liaw, Wee Lin School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Stochastic Processes Load Flow The number of electric vehicles (EVs) is expected to grow significantly, which calls for effective planning of charging infrastructures. While the planning of the charging infrastructure relies on an accurate charging demands, the behaviours of EVs charging are not always predictable and can be sensitive to many uncertain future environmental factors. Considering such uncertainties, this study aims to robustly and optimally determine the chargers and main switch board (MSB) capacities without violating queuing time constraints and load flow constraints. The non-parametric estimations of charging demands are derived with data-driven charging behaviour analysis considering diverse social factors, including travelling patterns, queuing, and changes of charging facilities. Then, the impacts of the EV integration are modeled by a stochastic load flow program. The samples of the stochastic load flow stipulate the conditional value-at-risk constraints for the planning of chargers and MSBs, which consider the probabilities and scenarios in a box of ambiguity with bounds. Afterwards, by limiting the frequency and severity of constraints violation, the total investment cost is minimized with a distributionally robust optimisation program. Simulation based on a real-world residential community in Singapore is carried out to testify the effectiveness of the proposed method. National Research Foundation (NRF) The work in this paper was supported by the National Research Foundation of Singapore under the project "Development of a Planning Tool for Electric Vehicle Impact Analysis and Urban Living Electrical Infrastructure Planning". 2022-07-15T05:41:38Z 2022-07-15T05:41:38Z 2020 Journal Article Yan, Z., Zhao, T., Xu, Y., Koh, L. H., Go, J. & Liaw, W. L. (2020). Data-driven robust planning of electric vehicle charging infrastructure for urban residential car parks. IET Generation, Transmission and Distribution, 14(26), 6545-6554. https://dx.doi.org/10.1049/iet-gtd.2020.0835 1751-8687 https://hdl.handle.net/10356/160204 10.1049/iet-gtd.2020.0835 2-s2.0-85102646835 26 14 6545 6554 en IET Generation, Transmission and Distribution © 2020 The Institution of Engineering and Technology. All rights reserved. |
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Engineering::Electrical and electronic engineering Stochastic Processes Load Flow Yan, Ziming Zhao, Tianyang Xu, Yan Koh, Leong Hai Go, Jonathan Liaw, Wee Lin Data-driven robust planning of electric vehicle charging infrastructure for urban residential car parks |
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The number of electric vehicles (EVs) is expected to grow significantly, which calls for effective planning of charging infrastructures. While the planning of the charging infrastructure relies on an accurate charging demands, the behaviours of EVs charging are not always predictable and can be sensitive to many uncertain future environmental factors. Considering such uncertainties, this study aims to robustly and optimally determine the chargers and main switch board (MSB) capacities without violating queuing time constraints and load flow constraints. The non-parametric estimations of charging demands are derived with data-driven charging behaviour analysis considering diverse social factors, including travelling patterns, queuing, and changes of charging facilities. Then, the impacts of the EV integration are modeled by a stochastic load flow program. The samples of the stochastic load flow stipulate the conditional value-at-risk constraints for the planning of chargers and MSBs, which consider the probabilities and scenarios in a box of ambiguity with bounds. Afterwards, by limiting the frequency and severity of constraints violation, the total investment cost is minimized with a distributionally robust optimisation program. Simulation based on a real-world residential community in Singapore is carried out to testify the effectiveness of the proposed method. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Yan, Ziming Zhao, Tianyang Xu, Yan Koh, Leong Hai Go, Jonathan Liaw, Wee Lin |
format |
Article |
author |
Yan, Ziming Zhao, Tianyang Xu, Yan Koh, Leong Hai Go, Jonathan Liaw, Wee Lin |
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Yan, Ziming |
title |
Data-driven robust planning of electric vehicle charging infrastructure for urban residential car parks |
title_short |
Data-driven robust planning of electric vehicle charging infrastructure for urban residential car parks |
title_full |
Data-driven robust planning of electric vehicle charging infrastructure for urban residential car parks |
title_fullStr |
Data-driven robust planning of electric vehicle charging infrastructure for urban residential car parks |
title_full_unstemmed |
Data-driven robust planning of electric vehicle charging infrastructure for urban residential car parks |
title_sort |
data-driven robust planning of electric vehicle charging infrastructure for urban residential car parks |
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2022 |
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https://hdl.handle.net/10356/160204 |
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1738844934338772992 |