M. W. Mudiyanselage et al., "A Multi-Agent Framework for Electric Vehicles Charging Power Forecast and Smart Planning of Urban Parking Lots," in IEEE Transactions on Transportation Electrification, doi: 10.1109/TTE.2023.3289196
A multi-agent framework for electric vehicles charging power forecast and smart planning of urban parking lots
|Author:||Mudiyanselage, Manthila Wijesooriya1; Hamzeh Aghdam, Farid2; Kazemi-Razi, S. Mahdi1;|
1Department of Mathematics, Physics and Electrical Engineering, University of Northumbria, Newcastle, United Kingdom
2Water, Energy and Environmental Engineering Research Unit, University of Oulu, P.O.Box 4300, Oulu, Finland
3Sargent & Lundy, 55 E Monroe St, Chicago, IL, USA
4Net Zero Industry Innovation Centre, Campus Masterplan, Teesside University, Middlesbrough, United Kingdom
5Center of research excellence in renewable energy and power systems, King Abdulaziz University, Jeddah, Saudi Arabia
6Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield, UK
7Center of Research Excellence in Renewable Energy and Power Systems and Department of Electrical and Computer Engineering, Faculty of Engineering, K. A. CARE Energy Research and Innovation Center, Renewable Energy and Power Systems Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
|Online Access:||PDF Full Text (PDF, 4.1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe20231004138675
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2023-10-04
This paper 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 the 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-level 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. Further, 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 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 150kW. Furthermore, on weekdays higher load demand could be seen during the night time as opposed to weekends where the load demand usually increases during the day time.
IEEE transactions on transportation electrification
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
218 Environmental engineering
The Deanship of Scientific Research (DSR) at King Abdulaziz University (KAU), Jeddah, Saudi Arabia has funded this project, under grant no. (RG-11-135-43). In addition, this work was supported from DTE Network+ funded by EPSRC grant reference EP/S032053/1.
© The Author(s) 2023. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.