The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
A Methodology for Optimization of Power Systems Demand Due to Electric Vehicle Charging Load
Abstract
This paper presents a methodology of optimizing power systems demand due to electric vehicle (EV) charging load. Following a brief introduction to the charging characteristics of EV batteries, a statistical model is presented for predicting the EV charging load. The optimization problem is then described, and the solution is provided based on the model. An example study is carried out with error and sensitivity analysis to validate the proposed method. Four scenarios of various combinations of EV penetration levels and charging modes are considered in the study. A series of numerical solutions to the optimization problem in these scenarios are obtained by serial quadratic programming. The results show that EV charging load has significant potential to improve the daily load profile of power systems if the charging loads are optimally distributed. It is demonstrated that flattened load profiles may be achieved at all EV penetration levels if the EVs are charged through a fast charging mode. In addition, the implementation of the proposed optimization is discussed with analyses on the impact of travel pattern and the willingness of customers.
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Citations
Cites
Lithium Batteries: Status, Prospects and Future
Scrosati, B., & Garche, J. (2010) · Journal of Power Sources · doi:10.1016/j.jpowsour.2009.11.048 · Cited by 5,153
Kempton, W., & Tomić, J. (2005) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.12.022 · Cited by 2,126
Integration of electric vehicles in the electric power system
Lopes, J. A. P., Soares, F. J., & Almeida, P. M. R. (2011) · Proceedings of the IEEE · doi:10.1109/jproc.2010.2066250 · Cited by 1,465
Modeling of Load Demand Due to EV Battery Charging in Distribution Systems
Qian, K., Zhou, C., Allan, M., & Yuan, Y. (2011) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2010.2057456 · Cited by 1,268
Self-citationMajeau-Bettez, G., Hawkins, T. R., & Strømman, A. H. (2011) · Environmental Science & Technology · doi:10.1021/es103607c · Cited by 662
Optimal Charging of Electric Vehicles in Low-Voltage Distribution Systems
Richardson, P., Flynn, D., & Keane, A. (2012) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2011.2158247 · Cited by 566 · cited 2×
“In [11]–[13], the charge rates of each EV or fleet of a EVs were also assumed to change continuously.”
BackgroundMasoum, A. S., Deilami, S., Moses, P. S., Masoum, M. A. S., & Abu-Siada, A. (2011) · IET Generation, Transmission & Distribution · doi:10.1049/iet-gtd.2010.0574 · Cited by 440
Cited by
Habib, S., Kamran, M., & Rashid, U. (2015) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.12.020 · Cited by 556
Electric vehicle charging demand forecasting model based on big data technologies
Arias, M. B., & Bae, S. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.08.080 · Cited by 346
Moradi, M. H., Abedini, M., Tousi, S. M. R., & Hosseinian, S. M. (2015) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2015.06.029 · Cited by 170
Simorgh, H., Doagou‐Mojarrad, H., Razmi, H., & Gharehpetian, G. B. (2018) · IET Generation, Transmission & Distribution · doi:10.1049/iet-gtd.2017.1663 · Cited by 96
Strezoski, L., & Stefani, I. (2024) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2024.109798 · Cited by 32
Metadata
- Authors
- Zhang, P., Qian, K., Zhou, C., Stewart, B. G., & Hepburn, D. M.
- Venue
- IEEE Transactions on Power Systems
- Published
- 2012-08
- Volume
- 27
- Issue
- 3
- Pages
- 1628-1636
- ISSN
- 0885-8950 (ISSN-L)
- 1558-0679
- OpenAlex
- W1979097584
- Cited by
- 286
- References
- 7
- FWCI
- 12.82
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zhang, P., Qian, K., Zhou, C., Stewart, B. G., & Hepburn, D. M. (2012). A Methodology for Optimization of Power Systems Demand Due to Electric Vehicle Charging Load. IEEE Transactions on Power Systems. https://doi.org/10.1109/tpwrs.2012.2186595
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
