Optimal Strategy to Exploit the Flexibility of an Electric Vehicle Charging Station
Open accessAbstract
The increasing use of electric vehicles connected to the power grid gives rise to challenges in the vehicle charging coordination, cost management, and provision of potential services to the grid. Scheduling of the power in an electric vehicle charging station is a quite challenging task, considering time-variant prices, customers with different charging time preferences, and the impact on the grid operations. The latter aspect can be addressed by exploiting the vehicle charging flexibility. In this article, a specific definition of flexibility to be used for an electric vehicle charging station is provided. Two optimal charging strategies are then proposed and evaluated, with the purpose of determining which strategy can offer spinning reserve services to the electrical grid, reducing at the same time the operation costs of the charging station. These strategies are based on a novel formulation of an economic model predictive control algorithm, aimed at minimising the charging station operation cost, and on a novel formulation of the flexibility capacity maximisation, while reducing the operation costs. These formulations incorporate the uncertainty in the arrival time and state of charge of the electric vehicles at their arrival. Both strategies lead to a considerable reduction of the costs with respect to a simple minimum time charging strategy, taken as the benchmark. In particular, the strategy that also accounts for flexibility maximisation emerges as a new tool for maintaining the grid balance giving cost savings to the charging stations.
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The approach in [13] focused on real-time operations, relying solely on current conditions and ignoring network constraints.
“Although, this approach focused on real-time operations, relying solely on current conditions and ignoring network constraints.”
From Optimal Operation of Electric Vehicles to Enhance the Flexibility Provision in Active Distribution Grids (2025) · cites this paper as [13]
Section 3 (Solution Strategies; page not provided) says the proposed strategies use forecast values together with new information. Section 3.2 says the controller plans over a future interval while accounting for current and future constraints, then updates the plan at each time slot. This contradicts the claim that it relies solely on current conditions; the cited passages do not establish whether distribution-network constraints are included. (LLM, conf=0.88)
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Panagi, S., Therapontos, P., Spanias, C., & Aristidou, P. (2025) · 2025 IEEE Kiel PowerTech · doi:10.1109/powertech59965.2025.11180664
- Authors
- Diaz-Londono, C., Colangelo, L., Ruiz, F., Patino, D., Novara, C., & Chicco, G.
- Affiliations
- Politecnico di Torino (IT)
- Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, 10129 Torino, Italy
- Pontificia Universidad Javeriana (CO)
- Corresponding author
- Diaz-Londono, Cesar
- Venue
- Energies
- Publisher
- MDPI AG
- Published
- 2019-10-10
- Volume
- 12
- Issue
- 20
- Pages
- 3834
- ISSN
- 1996-1073 (ISSN-L)
- OpenAlex
- W2979690388
- Cited by
- 55
- References
- 1
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 11 · 2023: 11 · 2022: 11 · 2021: 4 · 2020: 9 · 2019: 1
- FWCI
- 2.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Energies (publishedVersion, cc-by)
- PORTO@Iris (submittedVersion, cc-by)
- Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Politecnico di Torino
- Fundación CeiBA
- 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, charging station scheduling, charging flexibility, economic model predictive control, spinning reserve, time-varying electricity prices, arrival time uncertainty, grid balancing
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