Determination of the level of service and customer crowding for electric charging stations through fuzzy models and simulation techniques. (15th December 2017)

• Record Type: Journal Article
• Authors: Andrenacci, N.; Genovese, A.; Ragona, R.
• Is Part Of: Applied energy. Volume 208(2017)
• Page Start: 97
• Page End: 107
• Publication Date: 2017-12-15
• Subjects: Electric mobility -- Electric charging station deployment -- Big Data analysis -- Fuzzy modelling -- Level of service -- Scenario simulation; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2017.10.053
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A new method to assess service level for electric charging stations is proposed. A big-data traffic source allows the identification of a drivers' sample. A fuzzy model that mimics the drivers' behavior for station choice is presented. A simulation procedure to stack and queue in time the charging requests is shown. The instantaneous crowding and energy load for charging stations are obtained. Abstract: Electric mobility is regarded as an important option for reducing environmental impacts of transport. State incentives and planning efforts for the mass deployment of a public charging infrastructure (CI) are in hand in many countries; in particular, public CIs based on the Level 3 DC fast charge are most likely to become commercially viable in the short to medium term, as the drivers are more likely to view the operation as traditional refuelling. The aim of this work is to develop a procedure for the evaluation of the level of service of a configuration of electric fast charging stations (CI scenario), located in a selected urban area of the city of Rome. By varying the configuration of the stations in the area, and taking into account a charge demand inferred from real-world traffic data, we are able to make comparative analyses among different CI scenarios, and to determine the best one in terms of average and maximum waiting time to recharge (demand-side analysis). The steps considered included: creation of realistic CI scenarios based on lists of existing car parks and petrol stations; estimation of the potential battery electrical vehicle (BEV) users in the selected urban area using a Big Data analysis procedure; development of a fuzzy model to assign BEV users to stations with a criterion of convenience; use of a simulation procedure of all the charge events, in order to obtain a time profile of customer crowding at stations.