Mobility-Aware Vehicle-to-Grid (V2G) Optimization for Uniform Utilization in Smart Grid Based Power Distribution Network

  • Muhammad A. Hussain
  • Werner Brandauer
  • Myung J. Lee


One of the critical bottlenecks of high penetration of Electric Vehicles (EV) is uncoordinated, simultaneous charging of many EVs that can potentially impact the electric distribution grid with unwanted peak load demand. V2G technology enables the bidirectional flow of electric energy where EVs can also discharge energy to the grid from their batteries aiming to lower the peak demand. Our V2G optimization approach employs mobility information to balance peak utilization among differently utilized distribution segments by assigning each EV to an optimal Electric Vehicle Supply Equipment (EVSE) enabled parking lot. By aggregating geographically dispersed EVs and micro-grids with renewable energy sources as a virtual power plant (VPP), we proposed a scalable VPP based V2G optimization architecture integrated with VANET. Compared with existing solutions, our convex optimization algorithm uses fewer variables, attains uniform utilization of grid nodes by optimal EV charging/discharging profiles. By simulation, we showed that this novel mobility-aware, scalable V2G optimization algorithm can reduce or significantly postpone the need of expensive upgrade of power distribution infrastructure.


V2G optimization Smart grid Electric vehicle Grid utilization Mobility VANET 


  1. 1.
    Duan Z, Gutierrez B, Wang L (2014) Forecasting plug-in electric vehicle sales and the diurnal recharging load curve. In: Smart grid, IEEE Transactions on, vol. 5, no. 1, p 527–535Google Scholar
  2. 2.
    Sortomme E, Hindi MM, James MacPherson SD, Venkata SS (2011) Coordinated charging of plug-in hybrid electric vehicles to minimize distribution system losses. IEEE Trans Smart Grid 2(1):198–205CrossRefGoogle Scholar
  3. 3.
  4. 4.
    ISO/RTO Council (IRC) (2010) Assessment of plug-in electric vehicle integration with ISO/RTO systems, p 31.
  5. 5.
    NIST Framework and Roadmap for Smart Grid Interoperability Standards, Release 3.0, Sep 2014.
  6. 6.
    Baimel D, Tapuchi S, Baimel N (2016) Smart grid communication technologies- overview, research challenges and opportunities. 2016 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), Anacapri, p 116–120Google Scholar
  7. 7.
    Sortomme E, El-Sharkawi MA (2011) Optimal charging strategies for unidirectional vehicle-to-grid. IEEE Trans Smart Grid 2(1):131–138CrossRefGoogle Scholar
  8. 8.
    Lin J, Leung K-C, Li VOK (2014) Optimal scheduling with vehicle-to-grid regulation service. In: Internet of Things Journal, IEEE, vol. 1, no. 6, pp 556–569Google Scholar
  9. 9.
    Wang M, Liang H, Zhang R, Deng R, Shen X (2014) Mobility-aware coordinated charging for electric vehicles in VANET-enhanced smart grid. In: Selected Areas in Communications, IEEE Journal on, vol. 32, no. 7, pp 1344–1360Google Scholar
  10. 10.
    Estimated lot level energy consumption:
  11. 11.
    V-Charge, Automated Valet Parking and Charging for e-Mobility.
  12. 12.
    He Y, Venkatesh B, Guan L (2012) Optimal scheduling for charging and discharging of electric vehicles. IEEE Trans Smart Grid 3(3):1095–1105CrossRefGoogle Scholar
  13. 13.
    Khodayar ME, Wu L, Shahidehpour M (2012) Hourly coordination of electric vehicle operation and volatile wind power generation in scuc. IEEE Trans Smart Grid 3(3):1271–1279CrossRefGoogle Scholar
  14. 14.
    Sortomme E, El-Sharkawi MA (2012) Optimal scheduling of vehicle-to-grid energy and ancillary services. In: Smart Grid, IEEE Transactions on, vol. 3, no. 1, pp. 351–359Google Scholar
  15. 15.
    Su W, Chow M-Y (2011) Investigating a large-scale PHEV/EV parking deck in a smart grid environment. In: North American power symposium (NAPS), 2011, vol., no., p 1–6Google Scholar
  16. 16.
    Su W, Chow M-Y (2011) Performance evaluation of a PHEV parking station using particle swarm optimization. In: Proc. 2011 I.E. power and energy society general meeting, Detroit, Michigan, U.S.A. July 24–29, 2011Google Scholar
  17. 17.
    Su W, Chow M-Y (2011) Evaluation on Large-scale PHEV Parking Deck using Monte Carlo Simulation. In: Proc, The Fourth International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT2011), Shandong, China, July 6-9, 2011Google Scholar
  18. 18.
    Mukherjee JC, Gupta A (2014) A mobility aware scheduler for low cost charging of electric vehicles in smart grid. In: Communication Systems and Networks (COMSNETS), 2014 Sixth International Conference on, vol., no., p 1–8, 6–10 Jan. 2014Google Scholar
  19. 19.
    da Cunha FD, Boukerche A, Villas L, Viana AC, Loureiro AAF (2014) Data communication in VANETs: a survey, challenges and applications. RR-8498, INRIA Saclay; INRIAGoogle Scholar
  20. 20.
    Dedicated Short Range Communications (DSRC) (2009) Message set dictionary, SAE Std. J2735Google Scholar
  21. 21.
    Kamal H, Picone M, Amoretti M (2014) A survey and taxonomy of urban traffic management: towards vehicular networks. Google Scholar
  22. 22.
    Okuda R, Kajiwara Y, Terashima K (2014) A survey of technical trend of ADAS and autonomous driving. In: VLSI Technology, Systems and Application (VLSI-TSA), Proceedings of Technical Program −2014 international symposium on, vol., no., p 1–4, 28–30 April 2014Google Scholar
  23. 23.
  24. 24.
    Rui Y (2006) Research on the mathematical method and its application in electric load forecast,” Ph.D. dissertation, Dept. Mech. Elect. Eng., Central South University, Hunan, ChinaGoogle Scholar
  25. 25.
    Cui K, Li JR, Chen W, Zhang HY (2009) Research on load forecasting methods of urban power grid. Elect Power Technol Econ 21:33–38Google Scholar
  26. 26.
    Yao G, Chen ZS, Li XZ (2011) BP network based on particle swarm optimization of short-term electric load forecasting. J Guangdong Univ Petrochem Technol 21:47–50Google Scholar
  27. 27.
    Grant M, Boyd S (2007) “CVX” MATLAB| software for disciplined convex programming, version 1.1 (September 2007),
  28. 28.
    NYISO (New York Independent System Operator) Hourly actual load data

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Muhammad A. Hussain
    • 1
  • Werner Brandauer
    • 1
  • Myung J. Lee
    • 1
  1. 1.Department of Electrical EngineeringCity University of New York, City CollegeNew YorkUSA

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