A review of the population-based and individual-based approaches for electric vehicles in network energy studies

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URI: http://hdl.handle.net/11556/969
ISSN: 0378-7796
DOI: 10.1016/j.epsr.2020.106785
Publication date
2020-12
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Elsevier Ltd
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Abstract
The growing trend of introducing electric vehicles (EVs) into power systems to reduce the environmental emissions in the transport sector is gaining significant attention among electrical power system agents for two reasons: the potential grid services the EVs can offer in an aggregated manner and the possible undesirable effects of massive integration in grid operation that can increase the requirement for investment in new assets. In this context, the aggregator is the representative entity that needs to maximise the benefits in the management of these sizeable quantities of vehicles while fulfilling the requirements of grid services requested by the distribution system operator. In this study, we review the concept of EV aggregators and their potential services to the distribution network. Several studies related to EVs aggregation modelling have been analysed and classified into three groups: individual-based, population-based, and hybrid approaches. We present the current status of EVs aggregation modelling as well as future research trends. Furthermore, we discussed the performance comparison of EVs models from several manufacturers utilised in network integration studies, likewise the most relevant databases and surveys. Finally, we arranged and annexed the most relevant mathematical expressions of the reviewed approaches, thereby simplifying the comprehension of the methods.
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Aggregator , Electric vehicles , Grid services , Individual-based approach , Population-based approach , Vehicle surveys
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journal article
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Cortés Borray, Andrés Felipe, Julia Merino, Esther Torres, and Javier Mazón. “A Review of the Population-Based and Individual-Based Approaches for Electric Vehicles in Network Energy Studies.” Electric Power Systems Research 189 (December 2020): 106785. doi:10.1016/j.epsr.2020.106785.