Behavior-aware energy management in microgrids using quantum-classical hybrid algorithms under social and demand dynamics
Liu Minghong, Gaoshan Fu, Wang Pengchao, Xin Yuan, Qing Li, Tengfei Hou, Zhang Shuo · Scientific Reports · 2025
The increasing intricacy of modern microgrids, driven by uncertain consumption patterns, decentralized renewables, and user behavioral dynamics, calls for innovative optimization methodologies. This study introduces a hybrid quantum-classical framework for demand-side energy management, leveraging behavioral modeling to foster resilience and flexibility. By embedding principles from Social Cognitive Theory-such as behavioral imitation, confidence in personal capability, and social reinforcement-into a multi-objective optimization scheme, the model supports distributed decision-making and promotes adaptive prosumer behavior. The proposed approach employs Quantum Annealing in combination with NSGA-III to efficiently navigate the complex solution space, accounting for real-time uncertainties and the stochastic nature of both demand and renewable supply. The framework is tested within a case study of a peer-to-peer microgrid network, showcasing its effectiveness in enhancing energy efficiency, lowering peak demand, and improving operational resilience. Performance comparisons with traditional methods, including Mixed-Integer Programming and conventional metaheuristics, underline the improved scalability and robustness of the quantum-inspired model in handling trade-offs between cost, reliability, and socially-driven demand response. The research highlights the potential of integrating quantum-inspired optimization with behavioral energy modeling to advance intelligent and socially-responsive microgrid control systems.