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Assessment of Electric Vehicle Charging Costs in Presence of Distributed Photovoltaic Generation and Variable Electricity Tariffs

Author

Listed:
  • Marco Pasetti

    (Department of Information Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy)

  • Stefano Rinaldi

    (Department of Information Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy)

  • Alessandra Flammini

    (Department of Information Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy)

  • Michela Longo

    (Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy)

  • Federica Foiadelli

    (Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy)

Abstract

In this paper a general model for the estimation of the uncoordinated charging costs of Electric Vehicles (EVs) in the presence of distributed and intermittent generation, and variable electricity tariffs is presented. The proposed method aims at estimating the monthly average cost of uncoordinated charging of a single EV depending on the hour at which the EV is plugged into the EV Supply Equipment (EVSE). The feasibility and relevance of the proposed model is verified by applying the considered cost estimation method to a suitable use case. A single EV charging service offered at a public building equipped with a Photovoltaic (PV) system has been considered as reference case. The proposed model has been applied to the PV production and loads consumption data collected during one year, and the results of the study compared with the Time-Of-Use (TOU) electricity tariff. The application of the proposed model identified noticeable deviations among the computed EV charging costs and the reference TOU profile, with differences up to 40%, depending on the considered month and on the time of charging during the day. It can be concluded that such model could be used to properly detect opportunities of energy savings, and to define dedicated EV price signals that could help to promote the optimal use of distributed energy resources.

Suggested Citation

  • Marco Pasetti & Stefano Rinaldi & Alessandra Flammini & Michela Longo & Federica Foiadelli, 2019. "Assessment of Electric Vehicle Charging Costs in Presence of Distributed Photovoltaic Generation and Variable Electricity Tariffs," Energies, MDPI, vol. 12(3), pages 1-20, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:3:p:499-:d:203559
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    References listed on IDEAS

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    Cited by:

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    3. Ruijiu Jin & Dongsheng Zuo & Xiangfeng Zhang & Wengang Sun, 2019. "An Orderly Power Utilization Scheme Based on an Intelligent Multi-Agent Apanage Management System," Energies, MDPI, vol. 12(23), pages 1-21, November.
    4. Davide Della Giustina & Stefano Rinaldi & Stefano Robustelli & Andrea Angioni, 2021. "Massive Generation of Customer Load Profiles for Large Scale State Estimation Deployment: An Approach to Exploit AMI Limited Data," Energies, MDPI, vol. 14(5), pages 1-26, February.
    5. Eric Galvan & Paras Mandal & Shantanu Chakraborty & Tomonobu Senjyu, 2019. "Efficient Energy-Management System Using A Hybrid Transactive-Model Predictive Control Mechanism for Prosumer-Centric Networked Microgrids," Sustainability, MDPI, vol. 11(19), pages 1-24, September.
    6. Marco Pasetti, 2021. "Assessing the Effectiveness of the Energy Storage Rule-Based Control in Reducing the Power Flow Uncertainties Caused by Distributed Photovoltaic Systems," Energies, MDPI, vol. 14(8), pages 1-25, April.
    7. Kameswara Satya Prakash Oruganti & Chockalingam Aravind Vaithilingam & Gowthamraj Rajendran & Ramasamy A, 2019. "Design and Sizing of Mobile Solar Photovoltaic Power Plant to Support Rapid Charging for Electric Vehicles," Energies, MDPI, vol. 12(18), pages 1-22, September.

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