Bio-electrochemical swarm intelligence with quantum constrained graph learning for distributed battery optimization in urban microgrids

The integration of distributed battery storage in urban microgrids is becoming increasingly common. Yet, most existing methods for optimizing urban microgrid operation are incapable of jointly representing battery physics, device-to-device relationships, and the temporal variability of intermittent renewable generation. Conventional swarm and deterministic methods also commonly rely on random initialization, homogeneous agent behavior, or post-hoc constraint handling. Therefore, this research proposes a unifying framework that integrates five key components into a single pipeline: (1) Bio-Electrochemical Swarm Field Initialization (BESFI); (2) Adaptive Multi-Species Co-Evolutionary Swarm Routing (AMSCESR); (3) a Spatio-Temporal Energy Diffusion Graph Transformer (STED-GT); (4) Quantum-Inspired Stability-Constrained Optimization (QISCO); and (5) a Digital Twin Reinforcement Validation Engine (DT-RVE). The novelty lies in the closed coupling of physics-aware initialization, role-diverse swarm search, relational graph learning, in-loop stability filtering, and digital-twin reinforcement validation rather than in any isolated module. On the reported 25-node urban microgrid testbed, the framework achieved 95.8% dispatch accuracy, 94.6% energy efficiency, ± 3.7% voltage deviation, ± 0.019 Hz frequency deviation, 10.6% capacity fade, and 2.3 s supervisory adaptation latency. These results indicate improved load balancing, grid stability, battery-life preservation, and adaptive operation within the evaluated simulation conditions.