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Enabling Privacy in Vehicle-to-Grid Interactions for Battery Recharging

Author

Listed:
  • Cristina Rottondi

    (Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano 20133, Italy)

  • Simone Fontana

    (Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano 20133, Italy)

  • Giacomo Verticale

    (Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano 20133, Italy)

Abstract

The diffusion of Electric Vehicles (EV) fostered by the evolution of the power system towards the new concept of Smart Grid introduces several technological challenges related to the synergy among electricity-propelled vehicle fleets and the energy grid ecosystem. EVs promise to reduce carbon emissions by exploiting Renewable Energy Sources (RESes) for battery recharge, and could potentially serve as storage bank to flatten the fluctuations of power generation caused by the intermittent nature of RESes by relying on a load aggregator, which intelligently schedules the battery charge/discharge of a fleet of vehicles according to the users’ requests and grid’s needs. However, the introduction of such vehicle-to-grid (V2G) infrastructure rises also privacy concerns: plugging the vehicles in the recharging infrastructures may expose private information regarding the user’s locations and travelling habits. Therefore, this paper proposes a privacy-preserving V2G infrastructure which does not disclose to the aggregator the current battery charge level, the amount of refilled energy, nor the time periods in which the vehicles are actually plugged in. The communication protocol relies on the Shamir Secret Sharing threshold cryptosystem. We evaluate the security properties of our solution and compare its performance to the optimal scheduling achievable by means of an Integer Linear Program (ILP) aimed at maximizing the ratio of the amount of charged/discharged energy to/from the EV’s batteries to the grid power availability/request. This way, we quantify the reduction in the effectiveness of the scheduling strategy due to the preservation of data privacy.

Suggested Citation

  • Cristina Rottondi & Simone Fontana & Giacomo Verticale, 2014. "Enabling Privacy in Vehicle-to-Grid Interactions for Battery Recharging," Energies, MDPI, vol. 7(5), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:5:p:2780-2798:d:35519
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    References listed on IDEAS

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    Cited by:

    1. Yuttana Kongjeen & Krischonme Bhumkittipich, 2018. "Impact of Plug-in Electric Vehicles Integrated into Power Distribution System Based on Voltage-Dependent Power Flow Analysis," Energies, MDPI, vol. 11(6), pages 1-16, June.
    2. Reza Ahmadi Kordkheili & Seyyed Ali Pourmousavi & Mehdi Savaghebi & Josep M. Guerrero & Mohammad Hashem Nehrir, 2016. "Assessing the Potential of Plug-in Electric Vehicles in Active Distribution Networks," Energies, MDPI, vol. 9(1), pages 1-17, January.
    3. Yuancheng Li & Pan Zhang & Yimeng Wang, 2018. "The Location Privacy Protection of Electric Vehicles with Differential Privacy in V2G Networks," Energies, MDPI, vol. 11(10), pages 1-17, October.

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