Smart charging profiles for electric vehicles

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.

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3. The term NSE is used when generation is not able to supply all the demand.

4. Generation surplus appears when there is more generation than demand.

5. The mobility patterns data are deterministic. For simplicity, these data are assumed to be the same for every day in the case study, although the model allows to differentiate between days.

6. These constraints can be seen in Báñez et al. (2011) and Ramos et al. (2011).

7. The valley hours of the system refer to the hours with lower demand.

8. The average percentage of the absolute wind forecast error with respect to the day-ahead prediction is 16 %.

9. A penetration of 1 % EVs represents about 0.3 % of the system demand, and 50 % penetration represents about 14 % of the system demand.

10. In the model it means that the EV charge is optimised for every day on an hourly basis.

11. The average generation cost is the total variable cost of the generation units divided by the total demand of the system (including EVs). The marginal cost is the cost for the system of supplying one extra MW of demand, and the average marginal cost is the average of the hourly marginal cost of the system for the whole year. The costs of the generation units include the start-up, fuel, operation and maintenance and \(\hbox {CO}_{2}\) emissions costs.

12. The \(\hbox {CO}_{2}\) emissions considered are the quantity of \(\hbox {CO}_{2}\) emitted by the generation units of the system.

13. The use of an unregulated charge would require a generation expansion to maintain the same reliability levels in the system.

\(ev\) :

Types of EV

\(p\) :

Time periods (h)

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

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

\(t\) :

Thermal units

\(\epsilon _{x}\) :

Selective parameters for the sub-objectives

\(\overline{CH}^{ev}\) :

Maximum power charged by EV \(ev\)

\(\overline{EN}^{ev}\) :

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 \(p\)

\(D_{p}\) :

Demand in period \(p\)

\(EffBtW^{ev}\) :

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

\(EffGtB^{ev}\) :

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

\(EVS_{p}^{ev,s}\) :

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

\(FC^{t}\) :

Fixed cost of thermal unit \(t\)

\(NSEC\) :

Not-served energy cost

\(RC^{ev}\) :

Battery charge ramp of EV \(ev\)

\(SC^{t}\) :

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

\(TR_{p}^{ev,s}\) :

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

\(URC\),\(DRC\) :

Upward and downward reserve deficit cost

\(VC^{t}\) :

Variable cost of thermal unit \(t\)

\(WGS_{p}\) :

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

\(c_{p}^{t}\) :

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

\(ch_{p}^{ev,s}\) :

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

\(chf_{p}^{ev,s}\) :

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

\(chs_{p}^{ev,s}\) :

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

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

Demand plus EV charge in period \(p\)

\(dmin\) :

Minimum total system demand

\(dp_{p},dn_{p}\) :

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

\(en_{p}^{ev,s}\) :

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

\(g_{p}^{t}\) :

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

\(maxmind\) :

Objective variable for the valley-filling method

\(minwgs\) :

Objective variable for the RES Integration method

\(nse_{p}\) :

Not-served energy in period \(p\)

\(opcost\) :

Total system operational cost

\(st_{p}^{t}\) :

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 http://www.ev-merge.eu.

Authors and Affiliations

1. Institute for Research in Technology, Comillas Pontifical University, Alberto Aguilera, 23, 28015 , Madrid, Spain

   Fernando Banez-Chicharro, Jesus M. Latorre & Andres Ramos

Corresponding author

Correspondence to Fernando Banez-Chicharro.

This paper was awarded with the Best Student Paper Prize at the Computational Management Science Conference 2012.

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