A three-tier stackelberg game-based hierarchical optimization framework for integrated electric vehicle battery...

Area of Science:

• Energy Systems Engineering
• Operations Research
• Artificial Intelligence

Background:

• Integrated electric vehicle (EV) charging and battery swapping station (BSS) operations require sophisticated energy management strategies to balance grid stability, economic efficiency, and user demands.
• Existing systems often operate with decentralized decision-making, leading to suboptimal performance and increased grid stress.
• The dynamic nature of electricity pricing and EV user behavior necessitates advanced optimization techniques for effective resource allocation.

Purpose of the Study:

• To develop a hierarchical optimization framework for integrated EV battery swapping stations (BSS) and charging point operator (CPO) systems.
• To model and analyze the strategic interactions among grid operators, CPO-BSS operators, and EV users within a multi-stakeholder energy management context.
• To optimize dynamic electricity pricing, charging/swapping schedules, and grid power utilization while ensuring operational and grid constraints are met.

Main Methods:

• A three-tier Stackelberg game-based hierarchical optimization framework was proposed.
• A bi-level mixed-integer linear programming (MILP) formulation was developed, incorporating backward-induction-based Subgame Perfect Nash Equilibrium (SPNE) analysis.
• The framework was validated using real-world EV charging data (ACN-Data corpus) and electricity market prices (Italian GME).

Main Results:

• The proposed framework reduced operational costs by 14.2-26.5% and peak grid demand by 26-28% compared to unoptimized systems.
• Service reliability was maintained at 96.8%, with effective load shifting to low-price periods and enhanced battery utilization.
• The framework captured 15-22% additional value over decentralized strategies, approaching near-optimal centralized social welfare.

Conclusions:

• The hierarchical optimization framework effectively coordinates EV BSS and CPO operations for improved economic and grid performance.
• The approach demonstrates robustness, scalability, and computational tractability for practical implementation in integrated EV charging ecosystems.
• The study provides valuable insights for EV infrastructure planning, grid-aware energy management, and regulatory policy design.