Spatial Load Forecasting of Electric Vehicle Charging using GIS and Diffusion Theory

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Heyman, F (Author) Other; Pereira, C (Author) Other; Vladimiro Miranda (Author) FEUP; Soares, FJ (Author) Other

Conference proceedings International

Title: 2017 IEEE PES INNOVATIVE SMART GRID TECHNOLOGIES CONFERENCE EUROPE (ISGT-EUROPE)

Pages: 1-6

IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)

Torino, ITALY, SEP 26-29, 2017

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 9 Citations

Other information

Authenticus ID: P-00N-J1Y

DOI: 10.1109/isgteurope.2017.8260172

Abstract (EN): The uptake of electric vehicles (EV) will require important modifications in traditional grid planning and load forecasting techniques. Existing literature suggests that the integration of EVs will be more adversarial to elements of the existing electricity infrastructure in terms of power supply (kW) than energy (kWh) delivery. While several studies analyzed the grid impact of electric vehicle fleets, few consider the adoption process itself which may lead to strong spatial variations of the utilization of charging infrastructure. The presented approach extends spatial load forecasting, introducing diffusion theory elements to analyze spatio-temporal clustering of EV charging demand. Using open-access census and grid data, this work develops a deterministic framework to forecast spatial patterns of EV charging applied to a real-world environment. Outcomes suggest substantial spatial clustering of EV adoption patterns, showing substation overrating for EV penetration rates of 25% and above with 7.4kW charging power.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6