Electric Vehicle Load Disaggregation Based on Limited Activation Matching Pursuits
Open accessAbstract
This paper proposes a novel limited activation matching pursuits (LAMP) method to monitor the number of eclectic vehicles (EVs) connected at a charging station and their charging activities. LAMP is able to reflect the two-dimensional characteristics of the number and charging time of the EVs. Constraints are entered on the activation coefficients of the matching pursuits to avoid over matching a particular type of EV. The method includes the development of the basis based on typical EV charging profiles and the improvement of matching pursuits. A case study is undertaken based on real EV data collected from London. The results show that 79.35% EVs can be accurately detected in charging station load profile duration of one week. The number of EVs connected within each half hour can be identified with a relative mean absolute deviation (RMAD) of 21.18%.
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It is possible to analyze residential EV charging behavior from aggregate electricity load.
“For this application, it is possible to analyze residential EV charging behavior from aggregate electricity load (energy disaggregation, see also Section 4.1) [827].”
From Tackling climate change with machine learning (2023) · cites this paper as [827]
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Matching pursuits with time-frequency dictionaries
Mallat, S., & Zhang, Z. (1993) · IEEE Transactions on Signal Processing · doi:10.1109/78.258082 · Cited by 9,023 · see citing works
Rubino, L., Capasso, C., & Veneri, O. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.06.097 · Cited by 218 · see citing works
Network Impacts and Cost Savings of Controlled EV Charging
Verzijlbergh, R., Grond, M. O. W., Lukszo, Z., Slootweg, J. G., & Ilić, M. (2012) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2012.2190307 · Cited by 209 · see citing works
Effects of electric vehicle charging strategies on the German power system
Hanemann, P., Behnert, M., & Brückner, T. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.06.039 · Cited by 86 · see citing works
Optimal Scheduling for EV Charging Stations in Distribution Networks: A Convexified Model
Song, Y., Zheng, Y. J., & Hill, D. J. (2016) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2016.2568746 · Cited by 84 · see citing works
Operational planning, modeling and control of virtual power plants with electric vehicles
Raab, A. F. (2018) · DepositOnce · doi:10.14279/depositonce-6650 · Cited by 11 · see citing works
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Wang, S., Li, R., Evans, A., & Li, F.
- Affiliations
- University of Bath (GB)
- Corresponding author
- Li, Ran
- Li, Furong
- Venue
- Energy Procedia
- Publisher
- Elsevier BV
- Published
- 2019-02
- Volume
- 158
- Pages
- 2611-2616
- ISSN
- 1876-6102 (ISSN-L)
- OpenAlex
- W2918881199
- Cited by
- 12
- References
- 6
- Citations by year
- 2026: 2 · 2025: 2 · 2023: 2 · 2022: 2 · 2021: 3 · 2020: 1
- FWCI
- 2.39
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Energy Procedia (publishedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
- Keywords
- EV charging profiles, electricity profile
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