Flexibility modeling for parking lots with multiple EV charging stations
Open accessAbstract
The paper proposes an aggregated model that represents the flexibility potential of car parks equipped with multiple electric vehicle (EV) charging stations. The model is used in a stochastic optimization procedure to estimate in advance the maximum flexibility margins of the parking lot. The EV aggregator responsible for the charging stations offers intra-day ancillary services to the grid by specifying the possible margins within which the absorbed power can be varied, either up or down. These adjustments are made at the request of the distribution system operator, ensuring an appropriate level of EV charging. The effectiveness of the model is evaluated for parking lots with different numbers of charging stations and different daily profile forecasts of the number of EV arrivals and departures.
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The k-medoid clustering procedure provides the medoid and probability value for each cluster and stage, ensuring an integer number of entering, exiting, and parked cars and preserving feasibility of the scenarios.
“For each cluster and each stage, this procedure provides the medoid (i.e., one of the original scenarios that minimizes the dissimilarity measure with respect to the other scenarios in the cluster) and the corresponding probability value 𝜋 𝜔 , ensuring an integer number of entering, exiting, and parked cars, thus preserving the feasibility of the scenarios.”
From Enhancing voltage optimization in distribution networks through flexible operation of EV parking lots (2025) · cites this paper as [37]
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Research works printed but not linked: 7
- [2][2] Y. Li, S. He, Y. Li, L. Ge, S. Lou, Z. Zeng, Probabilistic charging power forecast of EVCS: Reinforcement learning assisted deep learning approach, IEEE Trans. Intell. Veh. 8 (1) (2023) 344–357.
- [6][6] İ. Şengör, O. Erdinç, B. Yener, A. Taşcıkaraoğlu, J.P.S. Catalão, Optimal energy management of EV parking lots under peak load reduction based DR programs considering uncertainty, IEEE Trans. Sustain. Energy 10 (3) (2019) 1034–1043.
- [7][7] M. De Santis, A.R. Di Fazio, M. Russo, T. Harighi, A, Borghetti voltage optimization in distribution networks using EV parking lots and PV systems as flexibility options, in: 23th IEEE Int. Conf. on Environment and Electrical Engineering, EEEIC, 2023.
- [8][8] M.P. Evans, S.H. Tindemans, D. Angeli, Flexibility framework with recovery guarantees for aggregated energy storage devices, IEEE Trans. Smart Grid 13
- [9][9] Y. Li, M. Han, Z. Yang, G. Li, G, Coordinating flexible demand response and renewable uncertainties for scheduling of community integrated energy systems with an electric vehicle charging station: A bi-level approach, IEEE Trans. Sustain. Energy 12 (4) (2021) 2321–2331.
- [12][12] S.R. Dabbagh, M.K. Sheikh-El-Eslami, A. Borghetti, Optimal operation of vehicle- to-grid and grid-to-vehicle systems integrated with renewables, in: Prof. of 19th Power Syst. Comput. Conf., PSCC, 2016.
- [13][13] C. Orozco, A. Borghetti, F. Napolitano, F. Tossani, Day-ahead multistage stochas- tic optimization of a group of electric vehicle charging stations, in: 2021 IEEE CPE-POWERENG, Florence, Italy, 2021.
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De Santis, M., Harighi, T., Di Fazio, A. R., Borghetti, A., & Russo, M. (2025) · Sustainable Energy, Grids and Networks · doi:10.1016/j.segan.2024.101601 · Cited by 9 · see citing works
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- Authors
- Harighi, T., Borghetti, A., Napolitano, F., & Tossani, F.
- Affiliations
- University of Bologna (IT)
- Corresponding author
- Harighi, Tohid
- Venue
- Electric Power Systems Research
- Publisher
- Elsevier BV
- Published
- 2024-09
- Volume
- 234
- Pages
- 110732
- ISSN
- 0378-7796 (ISSN-L)
- OpenAlex
- W4400066943
- Cited by
- 21
- References
- 0
- Citations by year
- 2026: 6 · 2025: 13 · 2024: 2
- FWCI
- 2.17
- Type
- journal article
- Language
- en
- Update notices
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Electric Power Systems Research (publishedVersion, cc-by)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, cc-by)
- Funders
- Faculty of Science and Engineering, University of Manchester (PRIN-20228E3T44)
- Ministero dell’Istruzione, dell’Università e della Ricerca
- Ministero dell'Istruzione dell'Universita e della Ricerca
- 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 › Electric Power System Optimization
- Keywords
- electric vehicle charging, parking lots, ancillary services, electric vehicle aggregators, stochastic optimization, distribution system operators
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