Combining the optimization of electric vehicle charging station placement and charge scheduling

Renewable energy sources like solar and wind reduce greenhouse gas emissions but introduce price volatility that challenges charging infrastructure integration for battery electric vehicles (BEV). While prior approaches have primarily targeted industrial applications, such as airport ground vehicles, urban buses, or utility fleets with centralised control and predictable patterns, this study presents the first comprehensive framework for private passenger cars, whose decentralised, heterogeneous usage poses far greater modelling challenges. This study presents a novel framework integrating Charging Station Placement (CSP) and Electric Vehicle Charge Scheduling (EVCSP) to minimize system costs under such volatility. The framework begins with Step 1 generating synthetic mobility profiles for hundreds of private cars across 35,040 quarter-hourly intervals yearly, by using MiD (Mobility in Germany) surveys, activity databases for temporal detail, and OpenStreetMap mapping for dwell-time charging opportunities. Step 2 derives quarter-hourly electricity prices using the ParFuM energy market model. Step 3 formulates the integrated CSP-EVCSP as a mixed-integer linear programme (MILP) and addresses computational complexity via a three-stage hybrid approach: (i) a greedy-Lagrangian Set Covering Problem (SCP) selects minimal station locations covering all profiles; (ii) a charge-level heuristic enforces state-of-charge feasibility and capacity limits through iterative cost adjustments and urgency-based allocation; (iii) a reduced EVCSP—with fixed locations—optimizes price-responsive schedules. A case study for the city of Essen demonstrates its applicability, revealing cost savings from operational-spatial integration versus uncontrolled or coverage-only baselines. Case 1 optimizes placement and scheduling; Case 2 optimizes placement only; Case 3 benchmarks full-coverage optimal operation under 2030 prices.