Minghong Liu, Gaoshan Fu, Pengchao Wang, Xin Yuan, Qing Li, Hou Tengfei, Shuo Zhang
State Grid Xinjiang Economic Research Institute, Ürümqi, Xinjiang, China.
State Grid Xinjiang Electric Power Co. Ltd., Ürümqi, Xinjiang, China.
Sci Rep. 2025 Jul 1;15(1):21326. doi: 10.1038/s41598-025-06199-z.
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.
由不确定的消费模式、分散式可再生能源和用户行为动态驱动的现代微电网日益复杂，这就需要创新的优化方法。本研究引入了一种用于需求侧能源管理的混合量子 - 经典框架，利用行为建模来增强弹性和灵活性。通过将社会认知理论中的原则（如行为模仿、对个人能力的信心和社会强化）嵌入多目标优化方案中，该模型支持分布式决策并促进适应性的产消者行为。所提出的方法将量子退火与NSGA - III相结合，以有效探索复杂的解空间，同时考虑实时不确定性以及需求和可再生能源供应的随机性。该框架在对等微电网网络的案例研究中进行了测试，展示了其在提高能源效率、降低峰值需求和增强运营弹性方面的有效性。与传统方法（包括混合整数规划和传统元启发式算法）的性能比较强调了量子启发模型在处理成本、可靠性和社会驱动的需求响应之间权衡时具有更好的可扩展性和鲁棒性。该研究突出了将量子启发优化与行为能源建模相结合以推进智能且对社会有响应的微电网控制系统的潜力。
