Smart microgrid optimization with electric vehicle flexibility for intra-day energy management

• Novel multi-timescale framework combining day-ahead planning with intra-day optimization. • Detailed modeling of four EV types with different battery sizes (16–57 kWh) and usage patterns. • Achieved 18% peak load reduction through coordinated EV charging and demand response. • Large EVs (57 kWh) provided major grid services while smaller EVs showed dispersed charging. • Day-ahead to intra-day power deviations maintained under 10% using rolling optimization. This paper presents a coordinated multi-timescale optimization framework for smart microgrids that effectively integrates electric vehicle (EV) flexibility with time-of-use pricing. The framework addresses critical limitations in existing approaches through a hierarchical structure that coordinates day-ahead scheduling with intra-day rolling optimization. Unlike conventional single-timescale methods, our approach dynamically adapts to real-time conditions while maintaining operational stability. We introduce detailed modeling of four heterogeneous EV types (BYD E6, Nissan Leaf, Mitsubishi i-MiEV, and BMW Mini), capturing their distinct battery capacities, charging/discharging efficiencies, and driving patterns. The framework systematically integrates shiftable and interruptible load management to enhance system flexibility. Comprehensive case studies using MATLAB/YALMIP with CPLEX demonstrate significant economic and technical improvements, including operational cost reduction, effective peak-shaving, and balanced power deviations consistently below 10% of total load. Results reveal that large-capacity EVs provide substantial grid services while smaller EVs exhibit distributed charging patterns. By precisely coordinating diverse flexibility resources across multiple timescales, the proposed framework offers microgrid operators a robust decision support tool that balances economic efficiency with technical stability while accommodating renewable energy uncertainties.