A modular simulation-optimization framework for planning and optimizing electric vehicle charging infrastructure expansion

Background

The rapid growth in electric vehicle (EV) adoption has created an urgent need for robust, accessible, and efficient charging infrastructure. As more drivers transition from internal combustion engine vehicles to EVs, ensuring that charging stations are conveniently located and adequately equipped becomes critical for user satisfaction and continued market growth. The field of EV infrastructure planning is inherently complex, as it must account for diverse user behaviors, varying travel patterns, and fluctuating charging demands across different times and locations. Moreover, the expansion of charging networks must be balanced with considerations for cost-effectiveness, spatial constraints, and the stability of the underlying power grid, making it a multidimensional challenge for urban planners and policymakers.

Current approaches to EV charging infrastructure planning often fall short in several key areas. Many rely on static or aggregate demand estimates, neglecting the stochastic and highly individualized nature of EV user mobility and charging behaviors. This can lead to suboptimal station placement, resulting in excessive detour distances, long wait times, and underutilized infrastructure. Furthermore, existing methods frequently overlook the intricate interplay between charging demand and power grid constraints, risking transformer overloads or unnecessary grid upgrades. The computational complexity of optimizing station locations, charger types, and grid connections—especially under multiple, sometimes conflicting objectives—renders traditional methods inadequate, particularly as EV adoption scales. As a result, there is a pressing need for more sophisticated, data-driven solutions that can dynamically model user behavior, optimize infrastructure deployment, and ensure grid reliability in a cost-effective manner.

Technology

CHARGE-MAP is a comprehensive, modular framework designed to optimize the expansion of Electric Vehicle (EV) charging infrastructure by integrating simulation, optimization, and power grid analysis. It features an Agent-based Simulation Module that models the mobility and charging behaviors of individual EV users using synthetic population data and behavioral theories to estimate when and where charging demand will arise. This simulation identifies infrastructure deficiencies, such as excessive detour distances or long wait times at stations. The Optimization Module then uses multi-objective integer programming to determine the best locations for new charging stations and employs combinatorial algorithms to decide the types and numbers of chargers needed at each site. Finally, the Power Module evaluates the impact of these infrastructure changes on the electrical grid, analyzing transformer capacity requirements and upgrade needs using synthetic distribution network data and load profiles. The framework operates in an iterative simulation-optimization loop, continuously refining infrastructure plans based on evolving demand scenarios and grid constraints.

What sets CHARGE-MAP apart is its holistic, data-driven approach that tightly integrates user behavior modeling, multi-criteria optimization, and power grid impact assessment. Unlike traditional methods that may rely on aggregate demand estimates or static optimization, CHARGE-MAP simulates individual-level charging events, capturing the stochastic and dynamic nature of real-world EV usage. It incorporates advanced behavioral theories to realistically model charging decisions and leverages diverse datasets—ranging from synthetic population demographics to detailed power grid models. The iterative loop between simulation and optimization ensures that infrastructure plans are robust to demand variability, while the power module guarantees grid reliability without unnecessary overinvestment. This unified, scalable methodology enables policymakers and planners to make informed, cost-effective decisions that balance user experience, infrastructure costs, and grid stability, as demonstrated in its successful application to statewide planning in Virginia.

Benefits

• Accurately models individual EV user behavior and mobility to estimate realistic spatiotemporal charging demand.
• Optimizes placement and sizing of new charging stations and chargers to minimize detour distances and wait times.
• Integrates power grid impact assessment to ensure transformer capacity and grid stability with EV infrastructure expansion.
• Employs an iterative simulation-optimization approach to handle stochastic demand and refine infrastructure plans.
• Supports multiple charging behaviors and charger types for comprehensive infrastructure planning.
• Enables cost-effective, data-driven decision-making for policymakers and urban planners.
• Demonstrated scalability and effectiveness through a large-scale case study with significant reductions in user inconvenience.
• Facilitates targeted grid investments by identifying specific transformer upgrade needs rather than wholesale upgrades.

Applications

• Urban EV charging network planning
• Utility grid upgrade forecasting
• Retail site charger deployment optimization
• Municipal EV infrastructure investment analysis
• EV adoption scenario simulation

Additional info

This framework is a data-driven system for optimizing Electric Vehicle charging infrastructure expansion. It integrates agent-based simulation of user mobility with multi-objective integer programming to determine optimal station locations and charger specifications. A power module then assesses grid capacity and upgrade needs, ensuring stable, efficient infrastructure development.