A Novel Open-Source Simulator Of Electric Vehicles in a Demand-Side Management Scenario

Abstract

Recently, due to the growth of the electric vehicle (EV) market, the investigation of grid-to-vehicle and vehicle-to-grid strategies has become a priority in both the electric mobility and distribution grid research areas. However, there is still a lack of large-scale data sets to test and deploy energy management strategies. In this paper, a fully customizable EV population simulator is presented as an attempt to fill this gap. The proposed tool is designed as a web simulator as well as a Matlab/Simulink block, in order to facilitate its integration in different projects and applications. It provides individual and aggregated charge, discharge and plugin/out event data for a population of EVs, considering both home and public charging stations. The population is generated on the basis of statistical data (which can be fully customized) including commuting distances, vehicle models, traffic and social behavior of the owners. A peak-shaving case study is finally proposed to show the potential of the simulator.

Energy-Environmental Planning of Electric Vehicles (EVs): A Case Study of the National Energy System of Pakistan

“…The present work used two different tools, whose reliability has been already validated in other researches in the literature, where real data were available [17,18], to forecast the impact of the progressive EVs' implementation in Pakistan, and the fulfilment of their electricity demand through additional PV capacity to be installed by the years 2030 and 2040. To the authors' knowledge, the gradual implementation of EVs was studied more on district [19,20] and city [21,22] levels than on a country level.…”

“…Each simulated end-user is modeled as an autonomous unit with a certain domestic energy plan for the EVs' charge. The ePop simulator has been developed and presented by Ciabattoni et al [17] and uses a bottom-up approach to assist the deployment of depth first search (DSF) algorithms, and design optimization procedures as well. In particular, they simulated the electrical demand of a considered region according to the behavior of EVs' users.…”

Rural EV charging: The effects of charging behaviour and electricity tariffs

“…One of which is Demand Side Management (DSM), which utilises pricing signals and behavioural changes (López et al, 2015) to manage fluctuating loads and reduce peak demands. An example investigated by Ciabattoni et al (2021) limited the power availability to EV chargers, although this would increase duration of charging it reduced peak power demands.…”