Behaviorally Embedded Multi-Agent Optimization for Urban Microgrid Energy Coordination Under Social Influence Dynamics

Urban microgrids are evolving into socially coupled energy systems in which prosumer decisions are shaped by both market incentives and peer influence. Conventional optimization approaches overlook this behavioral interdependence and offer limited adaptability under environmental disturbances. This study develops a behaviorally embedded multi-agent optimization framework that integrates social influence propagation with physical power network coordination. Each prosumer’s decision process incorporates economic, comfort, and behavioral components, while a community operator enforces system-wide feasibility. The resulting bilevel structure is formulated as an equilibrium problem with equilibrium constraints (EPEC) and solved using an iterative hierarchical algorithm. A modified 33-bus urban microgrid with 40 socially connected agents is assessed under stochastic wildfire ignition and propagation scenarios to evaluate resilience under hazard-driven uncertainty. Incorporating behavioral responses increases welfare by 11.8%, reduces cost variance by 9.1%, and improves voltage stability by 23% compared with conventional models. Under wildfire stress, socially cohesive agents converge more rapidly and maintain more stable dispatch patterns. The findings highlight the critical role of social topology in shaping both equilibrium behavior and resilience. The framework provides a foundation for socially responsive and hazard-adaptive optimization in next-generation human-centric energy systems.