Cited by 55Electric Vehicles and InfrastructureSmart Grid Energy ManagementElectric Power System Optimization

Optimal Strategy to Exploit the Flexibility of an Electric Vehicle Charging Station

Open access
Diaz-Londono, Cesar, Colangelo, Luigi, Ruiz, Fredy, Patino, Diego, Novara, Carlo, Chicco, Gianfranco·2019·Energies

Abstract

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/en12203834

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • 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)

    Checked by a reasoning model — may be wrong.

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 68

  1. Nghitevelekwa, K.; Bansal, R.C. A review of generation dispatch with large-scale photovoltaic systems. Renew. Sustain. Energy Rev. 2018, 81, 615-624. [CrossRef] · DOI
  2. Bakirtzis, E.A.; Simoglou, C.K.; Biskas, P.N.; Bakirtzis, A.G. Storage management by rolling stochastic unit commitment for high renewable energy penetration. Electr. Power Syst. Res. 2018, 158, 240-249. [CrossRef] · DOI
  3. Pandurangan, V.; Zareipour, H.; Malik, O. Frequency Regulation Services : A Comparative Study of Select North American and European Reserve Markets. In Proceedings of the 2012 North American Power Symposium (NAPS), Champaign, IL, USA, 9-11 September 2012; pp. 1-8.
  4. Deng, R.; Yang, Z.; Chow, M.Y.; Chen, J. A Survey on Demand Response in Smart Grids: Mathematical Models and Approaches. IEEE Trans. Ind. Inform. 2015, 11, 570-582. [CrossRef] · DOI
  5. Ma, J.; Silva, V.; Belhomme, R.; Kirschen, D.S.; Ochoa, L.F. Evaluating and Planning Flexibility in Sustainable Power Systems. IEEE Trans. Sustain. Energy 2013, 4, 200-209. [CrossRef] · DOI
  6. Holttinen, H.; Tuohy, A.; Milligan, M.; Silva, V.; Müller, S.; Söder, L. The Flexibility Workout: Managing Variable Resources and Assessing the Need for Power System Modification. IEEE Power Energy Mag. 2013, 11, 53-62. [CrossRef] · DOI
  7. Ulbig, A.; Andersson, G. Analyzing operational flexibility of electric power systems. Int. J. Electr. Power Energy Syst. 2015, 72, 155-164. [CrossRef] · DOI
  8. Li, J.; Liu, F.; Li, Z.; Shao, C.; Liu, X. Grid-side flexibility of power systems in integrating large-scale renewable generations: A critical review on concepts, formulations and solution approaches. Renew. Sustain. Energy Rev. 2018, 93, 272-284. [CrossRef] · DOI
  9. Hao, H.; Sanandaji, B.M.; Poolla, K.; Vincent, T.L. Aggregate Flexibility of Thermostatically Controlled Loads. IEEE Trans. Power Syst. 2015, 30, 189-198. [CrossRef] · DOI · Paper page
  10. Zhao, L.; Zhang, W. A Geometric Approach to Aggregate Flexibility Modeling of Thermostatically Controlled Loads. IEEE Trans. Power Syst. 2017, 32, 4721-4731. [CrossRef] · DOI
  11. Sajjad, I.A.; Chicco, G.; Napoli, R. Definitions of Demand Flexibility for Aggregate Residential Loads. IEEE Trans. Smart Grid 2016, 7, 2633-2643. [CrossRef] · DOI
  12. Diaz, C.; Ruiz, F.; Patino, D. Modeling and control of water booster pressure systems as flexible loads for demand response. Appl. Energy 2017, 204, 106-116. [CrossRef] · DOI
  13. Papadaskalopoulos, D.; Strbac, G.; Mancarella, P.; Aunedi, M.; Stanojevic, V. Decentralized Participation of Flexible Demand in Electricity Markets-Part II : Application With Electric Vehicles and Heat Pump Systems. IEEE Trans. Power Syst. 2013, 28, 3667-3674. [CrossRef] · DOI
  14. Good, N.; Mancarella, P. Flexibility in multi-energy communities with electrical and thermal storage: A stochastic, robust approach for multi-service demand response. IEEE Trans. Smart Grid 2019, 10, 503-513. [CrossRef] · DOI
  15. Wang, Y.; Cheng, J.; Zhang, N.; Kang, C. Automatic and linearized modeling of energy hub and its flexibility analysis. Appl. Energy 2018, 211, 705-714. [CrossRef] · DOI
  16. Haas, J.; Cebulla, F.; Cao, K.; Nowak, W.; Palma-behnke, R.; Rahmann, C.; Mancarella, P. Challenges and trends of energy storage expansion planning for fl exibility provision in low-carbon power systems- A review. Renew. Sustain. Energy Rev. 2017, 80, 603-619. [CrossRef] · DOI
  17. Pavić, I.; Capuder, T.; Kuzle, I. Value of flexible electric vehicles in providing spinning reserve services. Appl. Energy 2015, 157, 60-74. [CrossRef] · DOI
  18. Borba, B.S.M.; Szklo, A.; Schaeffer, R. Plug-in hybrid electric vehicles as a way to maximize the integration of variable renewable energy in power systems: The case of wind generation in northeastern Brazil. Energy 2012, 37, 469-481. [CrossRef] · DOI
  19. Khemakhem, S.; Rekik, M.; Krichen, L. A flexible control strategy of plug-in electric vehicles operating in seven modes for smoothing load power curves in smart grid. Energy 2017, 118, 197-208. [CrossRef] · DOI
  20. Wenzel, G.; Negrete-pincetic, M.; Olivares, D.E.; Macdonald, J.; Callaway, D.S. Real-Time Charging Strategies for an Electric Vehicle Aggregator to Provide Ancillary Services. IEEE Trans. Smart Grid 2018, 9, 5141-5151. [CrossRef] · DOI
  21. Giordano, F.; Ciocia, A.; Di Leo, P.; Spertino, F.; Tenconi, A.; Vaschetto, S. Self-Consumption Improvement for a Nanogrid with Photovoltaic and Vehicle-to-Home Technologies. In Proceedings of the 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), Palermo, Italy, 12-15 June 2018; pp. 1-6.
  22. Noel, L.; Rubens, G.Z.D.; Sovacool, B.K. Optimizing innovation, carbon and health in transport: Assessing socially optimal electric mobility and vehicle-to-grid pathways in Denmark. Energy 2018, 153, 628-637. [CrossRef] · DOI
  23. Cao, Y.; Wang, T.; Kaiwartya, O.; Min, G.; Ahmad, N.; Abdullah, A.H. An EV Charging Management System Concerning Drivers' Trip Duration and Mobility Uncertainty. IEEE Trans. Syst. Man Cybern. Syst. 2018, 48, 596-607. [CrossRef] · DOI
  24. García-Villalobos, J.; Zamora, I.; San Martín, J.I.; Asensio, F.J.; Aperribay, V. Plug-in electric vehicles in electric distribution networks: A review of smart charging approaches. Renew. Sustain. Energy Rev. 2014, 38, 717-731. [CrossRef] · DOI · Paper page
  25. Quirós-Tortós, J.; Ochoa, L.; Butler, T. How Electric Vehicles and the Grid Work Together: Lessons Learned from One of the Largest Electric Vehicle Trials in the World. IEEE Power Energy Mag. 2018, 16, 64-76, [CrossRef] · DOI · Paper page
  26. Haidar, A.M.A.; Muttaqi, K.M.; Haque, M.H. Multistage time-variant electric vehicle load modelling for capturing accurate electric vehicle behaviour and electric vehicle impact on electricity distribution grids. IET Gener. Transm. Distrib. 2015, 9, 2705-2716. [CrossRef] · DOI
  27. Sundström, O.; Binding, C. Flexible charging optimization for electric vehicles considering distribution grid constraints. IEEE Trans. Smart Grid 2012, 3, 26-37. [CrossRef] · DOI · Paper page
  28. Hu, J.; Morais, H.; Sousa, T.; Lind, M. Electric vehicle fleet management in smart grids: A review of services, optimization and control aspects. Renew. Sustain. Energy Rev. 2016, 56, 1207-1226. [CrossRef] · DOI
  29. Schuller, A.; Flath, C.M.; Gottwalt, S. Quantifying load flexibility of electric vehicles for renewable energy integration. Appl. Energy 2015, 151, 335-344. [CrossRef] · DOI
  30. Sadeghianpourhamami, N.; Refa, N.; Strobbe, M.; Develder, C. Quantitive analysis of electric vehicle flexibility: A data-driven approach. Int. J. Electr. Power Energy Syst. 2018, 95, 451-462. [CrossRef] · DOI · Paper page
  31. Mills, G.; Macgill, I. Assessing Electric Vehicle storage , flexibility, and Distributed Energy Resource potential. J. Energy Storage 2018, 17, 357-366. [CrossRef] · DOI
  32. Munshi, A.A.; Mohamed, Y. Extracting and Defining Flexibility of Residential Electrical Vehicle Charging Loads. IEEE Trans. Ind. Inform. 2018, 14, 448-461, [CrossRef] · DOI
  33. Grahn, P.; Alvehag, K.; Söder, L. PHEV Utilization Model Considering Type-of-Trip and Recharging Flexibility. IEEE Trans. Smart Grid 2014, 5, 139-148, [CrossRef] · DOI
  34. Clairand, J.M.; Rodríguez-García, J.; Álvarez-Bel, C. Smart Charging for Electric Vehicle Aggregators Considering Users' Preferences. IEEE Access 2018, 6, 54624-54635. [CrossRef] · DOI
  35. Qi, W.; Xu, Z.; Shen, Z.J.M.; Hu, Z.; Song, Y. Hierarchical coordinated control of plug-in electric vehicles charging in multifamily dwellings. IEEE Trans. Smart Grid 2014, 5, 1465-1474. [CrossRef] · DOI
  36. Yang, L.; Zhang, J.; Poor, H.V. Risk-aware day-ahead scheduling and real-time dispatch for electric vehicle charging. IEEE Trans. Smart Grid 2014, 5, 693-702. [CrossRef] · DOI
  37. Janjic, A.; Velimirovic, L.; Stankovic, M.; Petrusic, A. Commercial electric vehicle fleet scheduling for secondary frequency control. Electr. Power Syst. Res. 2017, 147, 31-41. [CrossRef] · DOI
  38. Wang, R.; Xiao, G.; Wang, P. Hybrid Centralized-Decentralized (HCD) Charging Control of Electric Vehicles. IEEE Trans. Veh. Technol. 2017, 66, 6728-6741. [CrossRef] · DOI
  39. Pertl, M.; Carducci, F.; Tabone, M.; Marinelli, M.; Kiliccote, S.; Kara, E.C. An Equivalent Time-Variant Storage Model to Harness EV Flexibility : Forecast and Aggregation. IEEE Trans. Ind. Inform. 2019, 15, 1899-1910. [CrossRef] · DOI
  40. Esmaili, M.; Goldoust, A. Multi-objective optimal charging of plug-in electric vehicles in unbalanced distribution networks. Int. J. Electr. Power Energy Syst. 2015, 73, 644-652. [CrossRef] · DOI
  41. Škugor, B.; Deur, J. Dynamic programming-based optimisation of charging an electric vehicle fleet system represented by an aggregate battery model. Energy 2015, 92, 456-465. [CrossRef] · DOI
  42. Sun, B.; Tan, X.; Tsang, D.H.K. Eliciting Multi-dimensional Flexibilities from Electric Vehicles: A Mechanism Design Approach. IEEE Trans. Power Syst. 2018. [CrossRef] · DOI
  43. Zhou, B.; Yao, F.; Littler, T.; Zhang, H. An electric vehicle dispatch module for demand-side energy participation. Appl. Energy 2016, 177, 464-474. [CrossRef] · DOI
  44. del Razo, V.; Goebel, C.; Jacobsen, H.A. Vehicle-Originating-Signals for Real-Time Charging Control of Electric Vehicle Fleets. IEEE Trans. Transp. Electrif. 2015, 1, 150-167. [CrossRef] · DOI
  45. Su, W.; Wang, J.; Zhang, K.; Huang, A.Q. Model predictive control-based power dispatch for distribution system considering plug-in electric vehicle uncertainty. Electr. Power Syst. Res. 2014, 106, 29-35. [CrossRef] · DOI
  46. Ruelens, F.; Vandael, S.; Leterme, W.; Claessens, B.J.; Hommelberg, M.; Holvoet, T.; Belmans, R. Demand side management of electric vehicles with uncertainty on arrival and departure times. In Proceedings of the IEEE PES Innovative Smart Grid Technologies Conference Europe, Berlin, Germany, 14-17 October 2012; pp. 1-8. [CrossRef] · DOI
  47. Ji, Z.; Huang, X.; Xu, C.; Sun, H. Accelerated model predictive control for electric vehicle integrated microgrid energy management: A hybrid robust and stochastic approach. Energies 2016, 9, 973. [CrossRef] · DOI
  48. Halvgaard, R.; Poulsen, N.K.; Madsen, H.; Jørgensen, J.B.; Marra, F.; Bondy, D.E.M. Electric vehicle charge planning using economic model predictive control. In Proceedings of the 2012 IEEE International Electric Vehicle Conference (IEVC 2012), Greenville, SC, USA, 4-8 March 2012; pp. 1-6. [CrossRef] · DOI
  49. Diaz, C.; Ruiz, F.; Patino, D. Smart Charge of an Electric Vehicles Station: A Model Predictive Control Approach. In Proceedings of the 2018 IEEE Conference on Control Technology and Applications (CCTA), Copenhagen, Denmark, 21-24 August 2018; pp. 54 -59. [CrossRef] · DOI
  50. Diaz, C.; Mazza, A.; Ruiz, F.; Patino, D.; Chicco, G. Understanding Model Predictive Control for Electric Vehicle Charging Dispatch. In Proceedings of the 2018 53nd International Universities Power Engineering Conference (UPEC), Glasgow, UK, 4-7 September 2018; pp. 1-6.
  51. Braun, M.W.; Rivera, D.E.; Carlyle, W.M.; Kempf, K.G. A model predictive control framework for robust management of multi-product, multi-echelon demand networks. IFAC Proc. Vol. (IFAC-PapersOnline) 2003, 27, 229-245. [CrossRef] · DOI
  52. Oldewurtel, F.; Parisio, A.; Jones, C.N.; Gyalistras, D.; Gwerder, M.; Stauch, V.; Lehmann, B.; Morari, M. Use of model predictive control and weather forecasts for energy efficient building climate control. Energy Build. 2012, 45, 15-27. [CrossRef] · DOI
  53. Colmenar-Santos, A.; Muñoz-Gómez, A.M.; Rosales-Asensio, E.; López-Rey, Á. Electric vehicle charging strategy to support renewable energy sources in Europe 2050 low-carbon scenario. Energy 2019, 183, 61-74. [CrossRef] · DOI
  54. Naharudinsyah, I.; Limmer, S. Optimal Charging of Electric Vehicles with Trading on the Intraday Electricity Market. Energies 2018, 11, 1416. [CrossRef] · DOI
  55. Mouli, G.R.C.; Kaptein, J.; Bauer, P.; Zeman, M. Implementation of dynamic charging and V2G using Chademo and CCS/Combo DC charging standard. In Proceedings of the 2016 IEEE Transportation Electrification Conference and Expo (ITEC 2016), Dearborn, MI, USA, 27-29 June 2016; pp. 1-6. [CrossRef] · DOI
  56. Amjad, M.; Ahmad, A.; Rehmani, M.H.; Umer, T. A review of EVs charging: From the perspective of energy optimization, optimization approaches, and charging techniques. Transp. Res. Part Transp. Environ. 2018, 62, 386-417. [CrossRef] · DOI
  57. Hoke, A.; Brissette, A.; Smith, K.; Pratt, A. Accounting for Lithium-Ion Battery Degradation in Electric Vehicle Charging Optimization. IEEE J. Emerg. Sel. Top. Power Electron. 2014, 2, 691-700. [CrossRef] · DOI
  58. Ottesen, S.Ø.; Tomasgard, A.; Fleten, S.E. Multi market bidding strategies for demand side flexibility aggregators in electricity markets. Energy 2018, 149, 120-134. [CrossRef] · DOI
  59. Lund, P.D.; Lindgren, J.; Mikkola, J.; Salpakari, J. Review of energy system flexibility measures to enable high levels of variable renewable electricity. Renew. Sustain. Energy Rev. 2015, 45, 785-807. [CrossRef] · DOI
  60. González, P.; Villar, J.; Díaz, C.A.; Campos, F.A. Joint energy and reserve markets: Current implementations and modeling trends. Electr. Power Syst. Res. 2014, 109, 101-111. [CrossRef] · DOI
  61. Baccino, F.; Conte, F.; Massucco, S.; Silvestro, F.; Grillo, S. Frequency Regulation by Management of Building Cooling Systems through Model Predictive Control. In Proceedings of the 18th Power Systems Computation Conference (PSCC), Wroclaw, Poland, 18-22 August 2014; pp. 1-7. [CrossRef] · DOI
  62. Kirby, B.J. Frequency Regulation Basics and Trends; U.S. Department of Energy: Washington, DC, USA, 2004; pp. 1-32.
  63. Kirby, B.; Kueck, J.; Laughner, T.; Morris, K. Spinning Reserve from Hotel Load Response: Initial Progress; Oak Ridge National Laboratory: Oak Ridge, TN, USA, 2008; pp. 1-35.
  64. Kara, E.C.; Tabone, M.D.; MacDonald, J.S.; Callaway, D.S.; Kiliccote, S. Quantifying flexibility of residential thermostatically controlled loads for demand response: A data-driven approach. In Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings, Memphis, TN, USA, 3-6 November 2014; pp. 140-147. [CrossRef] · DOI
  65. Quinn, C.; Zimmerle, D.; Bradley, T.H. The effect of communication architecture on the availability, reliability, and economics of plug-in hybrid electric vehicle-to-grid ancillary services. J. Power Sources 2010, 195, 1500-1509. [CrossRef] · DOI
  66. Kirby, B.; Hirst, E. Customer-Specific Metrics for the Regulation and Load-Following Ancillary Services; Technical Report; Oak Ridge National Laboratory: Oak Ridge, TN, USA, 2000. [CrossRef] · DOI
  67. Flammini, M.G.; Prettico, G.; Julea, A.; Fulli, G.; Mazza, A.; Chicco, G. Statistical characterisation of the real transaction data gathered from electric vehicle charging stations. Electr. Power Syst. Res. 2019, 166, 136-150. [CrossRef] · DOI
  68. Bascetta, L.; Gruosso, G.; Storti Gajani, G. Analysis of Electrical Vehicle behavior from real world data: A V2I Architecture. In Proceedings of the 2018 International Conference of Electrical and Electronic Technologies for Automotive AEIT, Milan, Italy, 9-11 July 2018; pp. 1-4.

Cited by

Sorted by citations

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
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review