Smart Charging of Plug-in Electric Vehicles Under Driving Behavior Uncertainty

  • Marina González Vayá
  • Göran Andersson
Part of the Reliable and Sustainable Electric Power and Energy Systems Management book series (RSEPESM)


An upcoming introduction of plug-in hybrid electric vehicles and electric vehicles could put power systems’ infrastructure under strain in the absence of charging control. The charging of electric vehicles could be managed centrally by a so-called aggregator, which would take advantage of the flexibility of these loads. To determine optimal charging profiles day-ahead, the aggregator needs information on vehicles’ driving behavior, such as departure and arrival time, parking location, and energy consumption, none of which can be perfectly forecasted. In this chapter, we introduce an approach to derive day-ahead charging profiles that minimize generation costs while respecting network and drivers’ end-use constraints, as well as taking into account the uncertainty in driving patterns. The charging profiles are derived by aggregating vehicles at each network node into virtual battery resources and dispatching them with a multiperiod optimal power flow (OPF). To take driving pattern uncertainty into consideration, different possible realizations of individual driving patterns are generated with a Monte Carlo simulation, modeling individual driving behavior with non-Markov chains. This information is integrated into the OPF, where constraints concerning the virtual batteries are modeled as chance constraints, i.e., as constraints that may be violated with a certain probability. Compared with a deterministic approach, this framework increases the chances of not violating the constraints subject to uncertainty.


Electric Vehicle Model Predictive Control Optimal Power Flow Chance Constraint Trip Duration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Ahn C, Li CT, Peng H (2011) Decentralized charging algorithm for electrified vehicles connected to smart grid. American control conference (ACC), 2011, pp 3924–3929Google Scholar
  2. 2.
    Bessa RJ, Matos MA (2011) Economic and technical management of an aggregation agent for electric vehicles: a literature survey. Eur Trans Electr Power 22(3):334–350CrossRefGoogle Scholar
  3. 3.
    Bundesamt für Statistik (2005) Mobilität in der Schweiz—Ergebnisse des Mikrozensus 2005 zum, Verkehrsverhalten (Swiss transportation statistics, in German), 2005Google Scholar
  4. 4.
    Bundesamt für Energie (2010) Schweizerische Elektrizitätsstatistik 2009 (Swiss electricity statistics, in German), 2010Google Scholar
  5. 5.
    Denholm P, Short W (2006) An evaluation of utility system impacts and benefits of optimally dispatched plug-in hybrid electric vehicles. Technical report, National Renewable Energy LaboratoryGoogle Scholar
  6. 6.
    Galus MD, González Vayá M, Krause T, Andersson G (2012) The role of electric vehicles in smart grids. Wiley Interdisciplinary Reviews, Energy and EnvironmentGoogle Scholar
  7. 7.
    González Vayá M, Andersson G (2012) Centralized and decentralized approaches to smart charging of plug-in vehicles. IEEE PES general meeting, San Diego, 2012Google Scholar
  8. 8.
    González Vayá M, Andersson G (2012) Smart charging of plug-in vehicles under driving behaviour uncertainty. 12th international conference on probabilistic methods applied to power systems (PMAPS), Istanbul, 2012Google Scholar
  9. 9.
    González Vayá M, Galus MD, Waraich RA, Andersson G (2012) On the interdependence of intelligent charging approaches for plug-in electric vehicles in transmission and distribution networks. IEEE PES innovative smart grid technologies Europe (ISGT Europe), Berlin, 2012Google Scholar
  10. 10.
    González Vayá M, Krause T, Waraich RA, Andersson G (2011) Locational marginal pricing based impact assessment of plug-in hybrid electric vehicles on transmission networks. CIGRE International Symposium, 2011Google Scholar
  11. 11.
    Kempton W, Tomic J (2005) Vehicle-to-grid power fundamentals: calculating capacity and net revenue. J Power Sources 144(1):268–279CrossRefGoogle Scholar
  12. 12.
    Kempton W, Tomic J (2005) Vehicle-to-grid power implementation: from stabilizing the grid to supporting large-scale renewable energy. J Power Sources 144(1):280–294CrossRefGoogle Scholar
  13. 13.
    Kristoffersen TK, Capion K, Meibom P (2011) Optimal charging of electric drive vehicles in a market environment. Appl Energy 88(5):1940–1948CrossRefGoogle Scholar
  14. 14.
    Lopes J, Soares F, Almeida P (2009) Identifying management procedures to deal with connection of electric vehicles in the grid. IEEE Power Tech Conference, 2009Google Scholar
  15. 15.
    Lopes JAP, Almeida P, Soares FJ (2009) Using vehicle-to-grid to maximize the integration of intermittent renewable energy resources in islanded electric grids. International conference on clean electrical power, 2009, pp 290–295Google Scholar
  16. 16.
    Pashajavid E, Golkar MA (2012) Multivariate stochastic modeling of plug-in electric vehicles demand profile within domestic grid. 12th international conference on probabilistic methods applied to power systems (PMAPS), Istanbul, 2012Google Scholar
  17. 17.
    Roe C, Meisel J, Meliopoulos AP, Evangelos F, Overbye T (2009) Power system level impacts of PHEVs. 42nd Hawaii international conference on system sciences (HICSS), 2009Google Scholar
  18. 18.
    Ruelens F, Vandael S, Leterme W, Claessens BJ, Hommelberg M, Holvoet T, Belmans R (2012) Demand side management of electric vehicles with uncertainty on arrival and departure times. IEEE PES innovative smart grid technologies Europe (ISGT Europe), Berlin, 2012Google Scholar
  19. 19.
    San Román TG, Momber I, Abbad MR, Sánchez Miralles A (2011) Regulatory framework and business models for charging plug-in electric vehicles: infrastructure, agents, and commercial relationships. Energy Policy 39(10):6360–6375CrossRefGoogle Scholar
  20. 20.
    Schneider K, Gerkensmeyer C, Kintner-Meyer M, Fletcher R (2008) Impact assessment of plug-in hybrid vehicles on Pacific Northwest distribution systems. IEEE power and energy society general meeting—conversion and delivery of electrical energy in the 21st Century, 2008Google Scholar
  21. 21.
    Sen S, Higle JL (1999) An introductory tutorial on stochastic linear programming models. Interfaces 29:33–61CrossRefGoogle Scholar
  22. 22.
    Soares FJ, Lopes JAP, Almeida PMR (2010) A Monte Carlo method to evaluate electric vehicles impacts in distribution networks. IEEE conference on innovative technologies for an efficient and reliable electricity supply (CITRES), 2010, pp 365–372Google Scholar
  23. 23.
    Sundström O, Binding C (2012) Flexible charging optimization for electric vehicles considering distribution grid constraints. IEEE Trans Smart Grid 3(1):26–37CrossRefGoogle Scholar
  24. 24.
    Vlachogiannis JG (2009) Probabilistic constrained load flow considering integration of wind power generation and electric vehicles. IEEE Trans Power Syst 24(4):1808–1817CrossRefGoogle Scholar
  25. 25.
    Wang L, Lin A, Chen Y (2010) Potential impact of recharging plug-in hybrid electric vehicles on locational marginal prices. Nav. Res. Logist. (NRL) 57(8):686–700CrossRefMATHMathSciNetGoogle Scholar
  26. 26.
    Waraich RA, Galus MD, Dobler C, Balmer M, Andersson G, Axhausen K (2009) Plug-in hybrid electric vehicles and smart grid: investigations based on a micro-simulation. 12th international conference of the international association for travel behaviour research, 2009Google Scholar
  27. 27.
    Zhongjing M, Callaway D, Hiskens I (2010) Decentralized charging control for large populations of plug-in electric vehicles. 49th IEEE conference on decision and control (CDC), 2010, pp 206–212Google Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Power Systems LaboratoryETHZurichSwitzerland

Personalised recommendations