Probabilistic agent-based model of electric vehicle charging demand to analyse the impact on distribution networks
Rights accessOpen Access
European Commisision's projectEMPOWER - Local Electricity retail Markets for Prosumer smart grid pOWER services (EC-H2020-646476)
Electric Vehicles (EVs) have seen significant growth in sales recently and it is not clear how power systems will support the charging of a great number of vehicles. This paper proposes a methodology which allows the aggregated EV charging demand to be determined. The methodology applied to obtain the model is based on an agent-based approach to calculate the EV charging demand in a certain area. This model simulates each EV driver to consider its EV model characteristics, mobility needs, and charging processes required to reach its destination. This methodology also permits to consider social and economic variables. Furthermore, the model is stochastic, in order to consider the random pattern of some variables. The model is applied to Barcelona’s (Spain) mobility pattern and uses the 37-node IEEE test feeder adapted to common distribution grid characteristics from Barcelona. The corresponding grid impact is analyzed in terms of voltage drop and four charging strategies are compared. The case study indicates that the variability in scenarios without control is relevant, but not in scenarios with control. Moreover, the voltages do not reach the minimum voltage allowed, but the MV/LV substations could exceed their capacities. Finally, it is determined that all EVs can charge during the valley without any negative effect on the distribution grid. In conclusion, it is determined that the methodology presented allows the EV charging demand to be calculated, considering different variables, to obtain better accuracy in the results.
CitationOlivella, P., R. Villafafila-Robles, Sumper, A., Bergas, J. Probabilistic agent-based model of electric vehicle charging demand to analyse the impact on distribution networks. "Energies", 11 Maig 2015, núm. 8, p. 4160-4187.