A Multiagent Framework for Electric Vehicles Charging Power Forecast and Smart Planning of Urban Parking Lots

Abstract

This article proposes a novel stochastic agent-based framework to predict the day-ahead charging demand of electric vehicles (EVs) considering key factors including the initial and final state-of-charge (SOC), the type of day, traffic conditions, and weather conditions. The accurate forecast of EVs charging demand enables the proposed model to optimally determine the location of common prime urban parking lots (PLs) including residential, offices, food centers, shopping malls, and public parks. By incorporating both macro- and micro-level parameters, the agents used in this framework provide significant benefits to all stakeholders, including EV owners, PL operators, PL aggregators, and distribution network operators. Furthermore, the path tracing algorithm is employed to find the nearest PL for the EVs and the probabilistic method is applied to evaluate the uncertainties of driving patterns of EV drivers and the weather conditions. The simulation has been carried out in an agent-based modeling (ABM) software called NETLOGO with the traffic and weather data of the city of Newcastle Upon Tyne, while the IEEE 33 bus system is mapped on the traffic map of the city. The findings reveal that the total charging demand of EVs is significantly higher on a sunny weekday than on a rainy weekday during peak hours, with an increase of over 150 kW. Furthermore, on weekdays higher load demand could be seen during the nighttime as opposed to weekends where the load demand usually increases during the daytime.