Summary
This paper presents a comparative analysis of three primary approaches to modeling spatial demand for electric vehicle (EV) charging: geospatial, statistical, and agent-based. The calculated spatial EV charging demand results are verified using real-world data from public charging station usage in a major Russian city over a period of 2.5 years. A detailed description of the three models under consideration is provided, and the calculation of the most promising locations for charging station deployment using each model is performed. The comparative analysis of the simulation results is based on metrics for the accuracy of calculating EV charging demand and for determining the ranking of the most attractive locations for EV charging station placement. Based on the analysis results, the scope of application for the considered methods of determining EV charging demand is defined. The obtained results can be used for planning the development of EV charging infrastructure.
Additional informations
| Publication type | Session Materials |
|---|---|
| Reference | C6_11217_2026 |
| Publication year | |
| Publisher | CIGRE |
| Country | Russian Federation |
| Study committees | |
| File size | 474 KB |
| Price for non member | 30 € |
| Price for member | 30 € |
Authors
VORONIN Vyacheslav - T.F. Gorbachev Kuzbass State Technical University; NEPSHA Fedor - RTSoft Smart Grid, LLC
Keywords
Electric Vehicles, Charging Stations, Statistical Analysis, Machine Learning, Geospatial Analysis, Agent modeling
