Agent-Based Modelling Approach to Support Strategic Planning for Renewable Energy Integration into EV Charging Stations in A University Campus

International audience The future of transportation is evolving rapidly with the widespread adoption of electric vehicles (EVs), The future of transportation is evolving rapidly with the widespread adoption of electric vehicles (EVs), marking a global shift toward sustainability. This transition presents a significant opportunity to redefine mobility and energy management through the integration of renewable energy sources. However, as the number of EVs continues to grow, inadequate charging facilities pose a risk of user dissatisfaction, and increased pressure on financial and power resources demands strategic planning. The need to incorporate renewable energy effectively into the system becomes crucial to avoid misallocation of resources. This study addresses these challenges within the context of a university campus, where the synchronization of EV user behaviour (arrival times, charging durations) and competing electrical demands (charging versus other activities) intensifies the issues. We propose a Simulation Environment using an Agent-Based Modeling (ABM) approach implemented in the GAMA Platform, which simulates EV user charging behaviours, optimizes the integration of renewable energy, and supports decision-making for charging station operations. Simulations based on real-world data explore multiple scenarios with varying numbers of vehicles to guide infrastructure planning and policy development. The simulation environment, combined with performance analysis, examined the impact of different parameter sets-including the number of charging ports, locations, solar panels, wind turbines and policy factors-on infrastructure efficiency. Preliminary results show significant improvements in renewable energy utilization, reductions in peak grid loads, and increased system efficiency. The findings suggest optimal configurations for the charging infrastructure, including the number of active charging ports and solar panels for three scenarios: the current number of EVs, a scenario where the number of EVs matches the existing gasoline vehicle fleet, and a future scenario with more EVs than gasoline vehicles. These configurations achieved over 85% satisfaction for charging, a renewable energy self-consumption rate of 95%, and a payback period of less than 2.5 years. This approach helped identify optimal configurations that balance cost-effectiveness while reducing electricity usage levels comparable to the overall campus consumption. This research highlights the potential of smart EV charging systems to foster sustainable transportation and energy ecosystems, establishing a foundation for resilient and eco-friendly urban infrastructure. Future work includes leveraging an Internet of Things (IoT) platform for a testbed combined with advanced computational models to create a dynamic digital twin of the charging network, enabling real-time monitoring, predictive analytics, and load balancing. The proposed solutions can be extended to other university settings or tourist areas, providing a scalable model for cities aiming to transition to a low-carbon future.