Abstract
In the next decades, Electric Vehicles (EVs) are expected to gain increasing popularity and huge penetration in the automotive market, thanks to their potentialities for close interaction with the Smart Grid ecosystem. Firstly, recharging EV’s batteries with energy produced by renewables will allow for a consistent reduction of pollution due to the carbon emissions of traditional gasoline combustion; secondly, batteries could be exploited to store/inject energy from/to the grid in order to compensate the unpredictable fluctuations caused by Renewable Energy Sources (RES). To this aim, a load aggregator is envisioned as a scheduling entity to plan the EVs’ battery recharge/discharge according to the user’s needs and the current power generation of the grid. The main drawback of the introduction of such load aggregator is a potential harm of users’ privacy: gathering information about the EVs’ recharge requests and plug/unplug events could make the scheduler able to infer the private travelling habits of the customers, thus exposing them to the risk of tracking attacks and to other privacy threats. To address this issue, this paper proposes a security infrastructure for privacy-friendly Vehicle-to-Grid (V2G) interactions, which enables the load aggregator to schedule the EV’s battery charge/discharge without learning the current battery level, nor the amount of charged/discharged energy, nor the time periods in which the EVs are available for recharge. Our proposed scheduling protocol is based on the Shamir Secret Sharing scheme. We provide a security analysis of the privacy guarantees provided by our framework and compare its performance to the optimal schedule that would be obtained if the aggregator had full knowledge of the charging-related information.
The work in this paper has been partially funded by the Italian Ministry of Education, University and Reserach (MIUR) project ESPRESSo.
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Notes
- 1.
Note that in a modulo \(n\) field negative numbers are represented by the upper half of the range \([0,n-1]\).
- 2.
For the sake of easiness, in this paper we set as SSS threshold \(t=w\), meaning that all the Aggregators must collaborate to perform the charge/discharge scheduling procedure. However, to improve resiliency to faults and malfunctions, \(t\) could be lower than \(w\). For a discussion on the correct choice of \(t\) and \(w\), the reader is referred to [27].
References
Chan, C., Bouscayrol, A., Chen, K.: Electric, hybrid, and fuel-cell vehicles: architectures and modeling. IEEE Trans. Veh. Technol. 59(2), 589–598 (2010)
Offer, G., Howey, D., Contestabile, M., Clague, R., Brandon, N.: Comparative analysis of battery electric, hydrogen fuel cell and hybrid vehicles in a future sustainable road transport system. Energy Policy 38(1), 24–29 (2010)
Parks, K., Denholm, P., Markel, A.J.: Costs and emissions associated with plug-in hybrid electric vehicle charging in the Xcel Energy Colorado service territory. National Renewable Energy Laboratory Golden, CO (2007)
Sioshansi, R., Denholm, P.: Emissions impacts and benefits of plug-in hybrid electric vehicles and vehicle-to-grid services. Environ. Sci. Technol. 43(4), 1199–1204 (2009)
Kempton, W., Tomić, J.: Vehicle-to-grid power implementation: from stabilizing the grid to supporting large-scale renewable energy. J. Power Sources 144(1), 280–294 (2005)
DeForest, N., Funk, J., Lorimer, A., Ur, B., Sidhu, I., Kaminsky, P., Tenderich, B.: Impact of widespread electric vehicle adoption on the electrical utility business-threats and opportunities. Center for Entrepreneurship and Technology (CET) (2009)
U.S. Department of Energy (DOE): Communication requirements for smart grid technologies (2010)
Kempton, W., Tomic, J., Letendre, S., Brooks, A., Lipman, T.: Vehicle-to-grid power: battery, hybrid, and fuel cell vehicles as resources for distributed electric power in california. In of Transportation Studies (UCD), U.D.I., ed.: Working Paper Series ECD-ITS-Rr-=1-03 (2001)
Brooks, A.: Integration of electric drive vehicles with the power grid-a new application for vehicle batteries. In: The Seventeenth Annual Battery Conference on Applications and Advances, p. 239 (2002)
Kempton, W., Marra, F., Andersen, P., Garcia-Valle, R.: Business models and control and management architectures for ev electrical grid integration. In: Garcia-Valle, R., Peas Lopes, J.A. (eds.) Electric Vehicle Integration into Modern Power Networks. Power Electronics and Power Systems, pp. 87–105. Springer, New York (2013)
Brooks, A.: Vehicle-to-grid demonstration project: grid regulation ancillary service with a battery electric vehicle. In: Research Report to CARB, AC Propulsion (2002)
Hoh, B., Gruteser, M., Xiong, H., Alrabady, A.: Enhancing security and privacy in traffic-monitoring systems. IEEE Pervasive Comput. 5(4), 38–46 (2006)
Liao, L., Patterson, D.J., Fox, D., Kautz, H.: Learning and inferring transportation routines. Artif. Intell. 171(5–6), 311–331 (2007)
Liu, R., Dow, L., Liu, E.: A survey of pev impacts on electric utilities. In: 2011 IEEE PES Innovative Smart Grid Technologies (ISGT), pp. 1–8 (2011)
Han, Y., Chen, Y., Han, F., Liu, K.: An optimal dynamic pricing and schedule approach in v2g. In: 2012 Asia-Pacific Signal Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 1–8 (2012)
Mets, K., Verschueren, T., Haerick, W., Develder, C., De Turck, F.: Optimizing smart energy control strategies for plug-in hybrid electric vehicle charging. In: 2010 IEEE/IFIP Network Operations and Management Symposium Workshops (NOMS Wksps), pp. 293–299, April 2010
Stegelmann, M., Kesdogan, D.: Design and evaluation of a privacy-preserving architecture for vehicle-to-grid interaction. In: Petkova-Nikova, S., Pashalidis, A., Pernul, G. (eds.) EuroPKI 2011. LNCS, vol. 7163, pp. 75–90. Springer, Heidelberg (2012)
Stegelmann, M., Kesdogan, D.: Location privacy for vehicle-to-grid interaction through battery management. In: 2012 Ninth International Conference on Information Technology: New Generations (ITNG), pp. 373–378 (2012)
Yang, Z., Yu, S., Lou, W., Liu, C.: \(p^{2}\): privacy-preserving communication and precise reward architecture for v2g networks in smart grid. IEEE Trans. Smart Grid 2(4), 697–706 (2011)
Liu, J.K., Au, M.H., Susilo, W., Zhou, J.: Enhancing location privacy for electric vehicles (at the Right time). In: Foresti, S., Yung, M., Martinelli, F. (eds.) ESORICS 2012. LNCS, vol. 7459, pp. 397–414. Springer, Heidelberg (2012)
Nicanfar, H., Hosseininezhad, S., TalebiFard, P., Leung, V.C.M.: Robust privacy-preserving authentication scheme for communication between electric vehicle as power energy storage and power stations. In: INFOCOM, pp. 3429–3434. IEEE (2013)
Shamir, A.: How to share a secret. Commun. ACM 22, 612–613 (1979)
Bogdanov, D.: Foundations and properties of shamir’s secret sharing scheme (2007). Research Seminar in Cryptography
Nishide, T., Ohta, K.: Multiparty computation for interval, equality, and comparison without bit-decomposition protocol. In: Okamoto, T., Wang, X. (eds.) PKC 2007. LNCS, vol. 4450, pp. 343–360. Springer, Heidelberg (2007)
Kerschbaum, F., Biswas, D., de Hoogh, S.: Performance comparison of secure comparison protocols. In: 20th International Workshop on Database and Expert Systems Application, pp. 133–136 (2009)
Bychkovsky, V., Hull, B., Miu, A., Balakrishnan, H., Madden, S.: A measurement study of vehicular internet access using in situ wi-fi networks. In: Proceedings of the 12th Annual International Conference on Mobile Computing and Networking, MobiCom ’06, pp. 50–61. ACM, New York (2006)
Rottondi, C., Verticale, G., Capone, A.: Privacy-preserving smart metering with multiple data consumers. Comput. Netw. 57(7), 1699–1713 (2013)
Stinson, D.: Cryptography Theory and Practice, 2nd edn. CRC Press, Boca Raton (2005)
Rottondi, C., Mauri, G., Verticale, G.: A protocol for metering data pseudonymization in smart grids to appear on Transactions on Emerging Telecommunications Technologies. doi:10.1002/ett.2760
Rottondi, C., Fontana, S., Verticale, G.: Enabling privacy in vehicle-to-grid interactions for battery recharging. Energies 7, 2780–2798 (2014)
Barker, S., Mishra, A., Irwin, D., Cecchet, E., Shenoy, P., Albrecht, J.: Smart*: an open data set and tools for enabling research in sustainable homes. In: The 1st KDD Workshop on Data Mining Applications in Sustainability (SustKDD) (2011)
Global Energy Forecasting Competition 2012: Wind forecasting. http://www.kaggle.com/c/GEF2012-wind-forecasting/data
U.S. Department of Transportation, Federal Highway Administration: National household travel survey (2009)
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Rottondi, C., Fontana, S., Verticale, G. (2014). A Privacy-Friendly Framework for Vehicle-to-Grid Interactions. In: Cuellar, J. (eds) Smart Grid Security. SmartGridSec 2014. Lecture Notes in Computer Science(), vol 8448. Springer, Cham. https://doi.org/10.1007/978-3-319-10329-7_8
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