Abstract
Electric vehicles (EVs) are being introduced by different manufacturers as an environment-friendly alternative to vehicles with internal combustion engines. The number of EVs is expected to grow rapidly in the coming years which will act as distributed loads in the demand side of the smart grid. On the supply side, an aggregator has to predict a load schedule 12-36 hours in advance to charge the EVs and purchase energy accordingly from the day-ahead market. The goal of the aggregator is to schedule the charging of EVs at different charging stations so that the load prediction made by the aggregator are met and the energy imbalance between the energy purchased and the energy consumed by the EVs is minimized. In this work, we refer to this problem as the Maximum Energy Usage EV Charging Problem where mobile EVs communicate their charging preferences apriori to the aggregator and the aggregator schedules EVs to different charging stations in their route so that it incurs maximum energy usage (and therefore, minimum energy imbalance). We first prove that the problem is NP-complete. A pseudo-polynomial algorithm is proposed for a restricted version of the problem that can act as an upper bound for the solution of the general problem. A greedy heuristic is then proposed to solve the problem. Detailed simulation results in different city traffic scenarios show that the usage of energy achieved by the heuristic proposed is close to the upper bound proved.
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References
Al-Awami, A.T., Sortomme, E.: Coordinating vehicle-to-grid services with energy trading. IEEE Transactions on Smart Grid 3(1), 453–462 (2012)
Bai, F., Krishnan, H., Sadekar, V., Holl, G., Elbatt, T.: Towards characterizing and classifying communication-based automotive applications from a wireless networking perspective. In: IEEE Workshop on Automotive Networking and Applications, AutoNet (2006)
Boulanger, A.G., Chu, A., Maxx, S., Waltz, D.: Vehicle electrification: Status and issues. Proceedings of the IEEE 99(6), 1116–1138 (2011)
Clement-Nyns, K., Haesen, E., Driesen, J.: The impact of charging plug-in hybrid electric vehicles on a residential distribution grid. IEEE Transactions on Power Systems 25(1), 371–380 (2010)
Erol-Kantarci, M., Mouftah, H.T.: Prediction-based charging of phevs from the smart grid with dynamic pricing. In: LCN, pp. 1032–1039 (2010)
Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman (1979)
Han, S., Han, S., Sezaki, K.: Development of an optimal vehicle-to-grid aggregator for frequency regulation. IEEE Transactions on Smart Grid 1(1), 65–72 (2010)
He, Y., Venkatesh, B., Guan, L.: Optimal scheduling for charging and discharging of electric vehicles. IEEE Transactions on Smart Grid 3(3), 1095–1105 (2012)
Khodayar, M.E., Wu, L., Shahidehpour, M.: Hourly coordination of electric vehicle operation and volatile wind power generation in scuc. IEEE Transactions on Smart Grid 3(3), 1271–1279 (2012)
Kim, H.-J., Lee, J., Park, G.-L.: Constraint-based charging scheduler design for electric vehicles. In: Pan, J.-S., Chen, S.-M., Nguyen, N.T. (eds.) ACIIDS 2012, Part III. LNCS, vol. 7198, pp. 266–275. Springer, Heidelberg (2012)
Lee, J., Kim, H.-J., Park, G.-L., Jeon, H.: Genetic algorithm-based charging task scheduler for electric vehicles in smart transportation. In: Pan, J.-S., Chen, S.-M., Nguyen, N.T. (eds.) ACIIDS 2012, Part I. LNCS, vol. 7196, pp. 208–217. Springer, Heidelberg (2012)
Sortomme, E., El-Sharkawi, M.A.: Optimal combined bidding of vehicle-to-grid ancillary services. IEEE Transactions on Smart Grid 3(1), 70–79 (2012)
Sortomme, E., El-Sharkawi, M.A.: Optimal charging strategies for unidirectional vehicle-to-grid. IEEE Transactions on Smart Grid 2(1), 131–138 (2011)
Sortomme, E., El-Sharkawi, M.A.: Optimal scheduling of vehicle-to-grid energy and ancillary services. IEEE Transactions on Smart Grid 3(1), 351–359 (2012)
Sortomme, E., Hindi, M.M., MacPherson, S.D.J., Venkata, S.S.: Coordinated charging of plug-in hybrid electric vehicles to minimize distribution system losses. IEEE Transactions on Smart Grid 2(1), 198–205 (2011)
Sundstrom, O., Binding, C.: Flexible charging optimization for electric vehicles considering distribution grid constraints. IEEE Transactions on Smart Grid 3(1), 26–37 (2012)
Vandael, S., Boucké, N., Holvoet, T., Craemer, K.D., Deconinck, G.: Decentralized coordination of plug-in hybrid vehicles for imbalance reduction in a smart grid. In: AAMAS, pp. 803–810 (2011)
Vasirani, M., Ossowski, S.: Lottery-based resource allocation for plug-in electric vehicle charging. In: 11th International Conference on Autonomous Agents and Multiagent Systems, pp. 1173–1174 (2012)
Wu, C., Mohsenian-Rad, H., Huang, J.: Vehicle-to-aggregator interaction game. IEEE Transactions on Smart Grid 3(1), 434–442 (2012)
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Mukherjee, J.C., Gupta, A. (2014). Mobility Aware Charge Scheduling of Electric Vehicles for Imbalance Reduction in Smart Grid. In: Chatterjee, M., Cao, Jn., Kothapalli, K., Rajsbaum, S. (eds) Distributed Computing and Networking. ICDCN 2014. Lecture Notes in Computer Science, vol 8314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45249-9_25
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DOI: https://doi.org/10.1007/978-3-642-45249-9_25
Publisher Name: Springer, Berlin, Heidelberg
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