| Phase: |
Theme |
| Theme: | Grids and Storage (T06) |
| Status: | Active |
| Start Date: | 2024-04-01 |
| End Date: | 2026-08-31 |
| Principal Investigator |
| Mertiny, Pierre |
Project Overview
The Canadian Government mandates every light-duty vehicle sold to be electric as of 2035. The Town of Banff (ToB) receives a substantial influx of visitors every year. Its 8,300 residents are dwarfed by a Visitor Adjusted Population that rises to 43,000 on peak travel days. Visitors occupy approximately 3,700 hotel rooms and around 25,000 vehicles drive into the town. In response, ToB and the Banff National Park have taken steps to mitigate negative traffic impacts by implementing an electrified public bus fleet. A transition to a full fleet of passenger electric vehicles (EVs) after 2035 poses considerable challenges to ToB in terms of traffic management and electrical infrastructure.
This project draws on FES projects T06-P02 ‘Operational decision support for smart grids’ (led by Dr. Musilek) and T06-P03 ‘Distributed energy storage’ (led by Dr. Mertiny). Dr. Anders contributes required complementary expertise in terms of public preferences and the economics of energy transition. The team is well positioned to complete this project that aligns with the Accelerator Fund’s goals.
Outputs
| Title |
Category |
Date |
Authors |
| Agent-Based EV Charging Simulation for a Rural Tourist Community: A Town of Banff Case StudyThe accelerating electrification of transport presents new challenges for rural, tourism oriented communities such as the Town of Banff (TOB), Alberta, where visitor traffic far exceeds the resident population. This thesis develops and applies a stochastic, agent-based simulation framework to evaluate the performance of the TOB’s electric vehicle (EV) charging network under increasing levels of EV adoption. The model integrates empirical hourly traffic counts, visitor profiles, and probabilistic driver behavior to simulate individual charging events at public and private slow- and fast charging ports. A Monte Carlo approach captures trends in key system- and user-level performance metrics, including load, utilization, waiting time, and charge failure rate. Validation through sensitivity analysis confirmed that the model responds logically to variations in adoption rate, charger quantity and level, and access type. The baseline scenario (0.38% EV adoption) showed that existing infrastructure meets current demand with negligible waiting. However, scenario analyses revealed three thresholds: onset of queuing near 1% adoption, capacity saturation near 3%, and widespread service degradation between 4–5%. Beyond 10%, exploratory simulations indicated severe congestion, with over 30% of EVs departing uncharged. Although further validation is needed, the framework offers a transferable tool for evaluating infrastructure expansion and policy options in tourism-dependent regions. The findings highlight the need for early, data-driven planning to ensure a reliable transition to electric mobility in small, high-visitation communities. | Publication | 2026-03-01 | Grayden Wiebe |
| Electric Vehicle Load Forecasting in Rural Areas: A Systematic ReviewThe growing adoption of electric vehicles, combined with increasing interdependence between urban and rural areas, raises concerns about the resilience of electrical networks, particularly in rural regions where infrastructure is less robust and more limited in complexity. Accurate load forecasting is therefore essential to support effective planning and mitigate potential stress on the grid. This study aims to evaluate and synthesize methodologies for predicting electrical loads generated by electric vehicles in rural areas, with the objective of identifying current practices, data characteristics, and methodological gaps. Following a systematic review approach, the work compiles and analyzes recent literature to provide a structured reference framework for researchers and practitioners. The findings reveal a growing research interest in this field, particularly in Europe and North America, with both model-based and data-driven approaches used in comparable proportions, and short-term forecasting emerging as the most common horizon. However, a lack of standardization in the documentation of network characteristics remains a significant limitation across studies. The review contributes by clarifying the state of research, highlighting critical gaps, and offering guidance for future work. These results underscore the importance of developing standardized criteria for documenting network properties and integrating diverse data sources to enhance the accuracy and applicability of load forecasting in rural distribution networks. University of Alberta | Publication | 2025-10-13 | Adrian Barradas Barradas, Grayden Wiebe, Aynaz Gerami, Mertiny, P., Anders, S., Musilek, P. |
