Computational Management Science

, Volume 11, Issue 1–2, pp 87–110 | Cite as

Smart charging profiles for electric vehicles

  • Fernando Banez-ChicharroEmail author
  • Jesus M. Latorre
  • Andres Ramos
Original Paper


Electric vehicles (EVs) can help decarbonise the transportation sector, which is responsible for a great share of greenhouse gas emissions. Although different measures have been introduced to foster the penetration of EVs in the society, they have not been deployed at a large scale yet. Electric companies are concerned about the effects of introducing EVs into the grid, especially with a large amount. The charging pattern of EVs is the main factor that determines these effects. Unregulated charging (probably when returning home) would have undesirable consequences (e.g. increase in variable costs, emissions, reduction of reliability) for the system, it is therefore necessary to develop an “intelligent” charging strategy. These characteristics justify the existence of different smart charging profiles. It is also important to assess the effect of using day-ahead management systems instead of pre-set profiles. This document compares different possible strategies for charging EVs and their consequences in the power system. The impact on variable costs, emissions and renewable energy sources integration will be obtained using an operation planning model. The Spanish power system for 2020 is analysed under different EV penetration levels and charging strategies. The results show the benefits of using smart charging profiles instead of an unregulated profile, obtaining large cost reductions and maintaining system reliability levels. Moreover, the benefits of using a day-ahead management system are also evaluated, resulting in a small reduction of system variable cost compared to the use of pre-defined charging profiles.


Electric vehicles Smart charging RES integration  Wind spillage Power system operation 

List of symbols



Types of EV


Time periods (h)

\(s,s^{\prime }\)

State of the EV (\(sc\), \(sd\) and \(sm\))


Thermal units


\(\epsilon _{x}\)

Selective parameters for the sub-objectives


Maximum power charged by EV \(ev\)


Maximum energy charged by EV \(ev\)

\(CEVS_{p}^{ev,s,s^{\prime }}\)

Percentage of EV \(ev\) moving from state \(s\) to state \(s^{\prime }\) in period \(p\)


Demand in period \(p\)


Battery-to-wheel efficiency for EV \(ev\)


Grid-to-battery efficiency for EV \(ev\)


Percentage of EV \(ev\) at state \(s\) in period \(p\)


Fixed cost of thermal unit \(t\)


Not-served energy cost


Battery charge ramp of EV \(ev\)


Start-up cost of thermal unit \(t\)


Battery energy used in transport for EV \(ev\) in state \(s\) and period \(p\)


Upward and downward reserve deficit cost


Variable cost of thermal unit \(t\)


Wind spillage without introducing EVs in period \(p\)



Commitment of thermal unit \(t\) in period \(p\)


Power charged by EV \(ev\) at state \(s\) in period \(p\)


Power charged by EV \(ev\) at state \(s\) in period \(p\) determined by the maximisation of the minimum total demand of the system and the flatten of the demand curve


Power charged by EV \(ev\) at state \(s\) in period \(p\) to reduce wind spillage

\(d^{\prime }_{p}\)

Demand plus EV charge in period \(p\)


Minimum total system demand


Positive and negative demand variation between periods \(p\) and \(p-1\)


State-of-charge (SOC) of the battery of EV \(ev\) at state \(s\) at the end of period \(p\)


Output of thermal unit \(t\) in period \(p\)


Objective variable for the valley-filling method


Objective variable for the RES Integration method


Not-served energy in period \(p\)


Total system operational cost


Start-up of thermal unit \(t\) in period \(p\)

\(urdef_{p}\), \(drdef_{p}\)

Upward and downward reserve deficit in period \(p\)



This work was developed in the context of the MERGE Project. The MERGE Project was supported by the European Commission under the Seventh Framework Programme. The sole responsibility of the content of this paper lies with the authors. It does not represent the opinion of the Commission. The European Commission is not responsible for any use that may be made for the information contained therein. For further information visit


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Fernando Banez-Chicharro
    • 1
    Email author
  • Jesus M. Latorre
    • 1
  • Andres Ramos
    • 1
  1. 1.Institute for Research in TechnologyComillas Pontifical UniversityMadridSpain

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