Game-theoretic energy scheduling for community multi-energy system with electric vehicle aggregator leveraging quantum swarm intelligence

• Construct game-theoretic framework for community multi-energy system stakeholders. • Develop enhanced Quantum Particle Swarm Optimization algorithm for energy scheduling. • Demonstrate cost reduction and renewable energy gains through case study. • Merge quantum swarm intelligence with non-cooperative energy games for sustainability. The global decarbonization toward renewable integration and decarbonization underscores the critical role of community multi-energy systems (CMES) in optimizing localized energy flows across electricity, heating, and transportation vectors. This paper addresses the challenge of strategic interactions among self-interested stakeholders in dynamic energy markets, which include CMES operator, multi-energy prosumer (MEP), and electric vehicle (EV) aggregator. A game-theoretic framework is proposed to model their interdependent decision-making as a non-cooperative Nash game, which can explicitly model the non-cooperative dynamics between self-interested agents. Under this framework, the operator minimizes costs, MEP maximize profits, and EV aggregator optimizes charging expenses. To efficiently solve this high-dimensional equilibrium problem, we develop an enhanced quantum particle swarm optimization (QPSO) algorithm incorporating chaotic perturbation and adaptive quantum behaviors. The proposed approach is validated through a comprehensive 24-hour case study, the result show that the framework can effectively reduce operational costs and improve renewable energy utilization via synergies between MEP, PV generation and EV charging. This work facilitates the application of QPSO to non-cooperative energy games in CMES with various decision makers, offering a scalable pathway for cost-effective, low-carbon multi-energy communities under energy transition.