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DC Field | Value | Language |
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dc.contributor.author | Wang, Z | - |
dc.contributor.author | Zou, L | - |
dc.contributor.author | Liu, H | - |
dc.contributor.author | Sun, Q | - |
dc.contributor.author | Alsaadi, FE | - |
dc.date.accessioned | 2022-05-10T11:44:27Z | - |
dc.date.available | 2021-11-10 | - |
dc.date.available | 2022-05-10T11:44:27Z | - |
dc.date.issued | 2021-11-10 | - |
dc.identifier.citation | Bai, X., Wang, Z., Zou, L., Liu, H., Sun, Q. and Alsaadi, F.E. (2022) 'Electric vehicle charging station planning with dynamic prediction of elastic charging demand: a hybrid particle swarm optimization algorithm', Complex & Intelligent Systems, 8, pp. 1035 - 1046 (12). doi: 10.1007/s40747-021-00575-8. | en_US |
dc.identifier.issn | 2199-4536 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/24548 | - |
dc.description.abstract | Copyright © The Author(s) 2021. This paper is concerned with the electric vehicle (EV) charging station planning problem based on the dynamic charging demand. Considering the dynamic charging behavior of EV users, a dynamic prediction method of EV charging demand is proposed by analyzing EV users’ travel law via the trip chain approach. In addition, a multi-objective charging station planing problem is formulated to achieve three objectives: (1) maximize the captured charging demands; (2) minimize the total cost of electricity and the time consumed for charging; and (3) minimize the load variance of the power grid. To solve such a problem, a novel method is proposed by combining the hybrid particle swarm optimization (HPSO) algorithm with the entropy-based technique for order preference by similarity to ideal solution (ETOPSIS) method. Specifically, the HPSO algorithm is used to obtain the Pareto solutions, and the ETOPSIS method is employed to determine the optimal scheme. Based on the proposed method, the siting and sizing of the EV charging station can be planned in an optimal way. Finally, the effectiveness of the proposed method is verified via the case study based on a test system composed of an IEEE 33-node distribution system and a 33-node traffic network system. | en_US |
dc.description.sponsorship | National Natural Science Foundation of China under Grants 61703245, 61873148 and 61933007; Natural Science Foundation of Shandong Province of China under Grant ZR2020MF071; AHPU Youth Top-notch Talent Support Program of China under Grant 2018BJRC009; Natural Science Foundation of Anhui Province of China under Grant 2108085MA0y; Royal Society of the UK; Alexander von Humboldt Foundation of Germany. | en_US |
dc.format.extent | 1035 - 1046 (12) | - |
dc.format.medium | Print-Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | Springer Nature | en_US |
dc.rights | Copyright © The Author(s) 2021. Rights and permissions: Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/. | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | electric vehicles (EVs) | en_US |
dc.subject | charging demand prediction | en_US |
dc.subject | trip chain | en_US |
dc.subject | charging station planning | en_US |
dc.subject | hybrid particle swarm optimization (HPSO) | en_US |
dc.title | Electric vehicle charging station planning with dynamic prediction of elastic charging demand: a hybrid particle swarm optimization algorithm | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1007/s40747-021-00575-8 | - |
dc.relation.isPartOf | Complex & Intelligent Systems | - |
pubs.publication-status | Published online | - |
pubs.volume | 8 | - |
dc.identifier.eissn | 2198-6053 | - |
Appears in Collections: | Dept of Computer Science Research Papers |
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