A Two-Stage Optimization Framework for Validating Electric Vehicle Charging Infrastructure under Grid Constraints

TL;DR

This paper introduces a two-stage optimization framework linking EV infrastructure planning with grid-constrained operation, demonstrating the importance of spatial distribution and grid considerations for effective deployment.

Contribution

It presents a novel mixed-integer programming approach that explicitly integrates infrastructure planning with grid constraints, considering heterogeneous charging technologies and spatial deployment effects.

Findings

Uniform deployment reduces energy shortfall by up to 74%.

Cost-optimal configurations tend to concentrate charging resources.

Effective planning requires joint consideration of cost, distribution, and grid constraints.

Abstract

This paper proposes a two-stage optimization framework to evaluate whether cost-optimal electric vehicle (EV) charging infrastructure translates into effective operation under distribution grid constraints. The proposed approach explicitly links infrastructure planning with grid-constrained charging operation through a consistent optimal power flow (OPF) formulation applied in both stages. The framework is formulated as a mixed-integer program (MIP) and evaluated across different fleet sizes, demonstrating its scalability and applicability to realistic planning scenarios. The model incorporates heterogeneous charging technologies, including fast and slow chargers with both single-port and multi-port configurations. The results show a fundamental trade-off between cost optimality and service performance. Infrastructure configurations that minimize capital investment tend to spatially concentrate charging resources, resulting in lower achieved state-of-charge (SOC) and higher unmet energy demand. In contrast, uniformly distributed deployments of the same infrastructure significantly improve the spatial availability of charging and operational performance, reducing energy shortfall by up to 74%. Our findings reveal that cost-optimal planning alone is insufficient to guarantee satisfactory system performance. Effective EV charging infrastructure design must jointly consider cost optimality, spatial distribution of charging resources, and grid constraints. Sensitivity analysis with respect to battery capacity further highlights the nonlinear scaling of infrastructure requirements.