Resilient distributed model predictive control for cooperative microgrids under communication loss with demand response integration

This paper introduces a resilient distributed model predictive control (RDMPC) framework for coordinating energy management across networked microgrids with demand response integration. The coordination mechanism employs an alternating direction method of multipliers (ADMM)-based distributed MPC formulation that maintains tie-line reciprocity via a shared consensus schedule; standard ADMM convergence results apply under reliable communication for the convex quadratic-program relaxation. Safe operation under communication impairments and early termination is achieved by executing the reciprocal consensus tie-line setpoints and performing a local feasibility-repair step with physically interpretable slack variables (load shedding and spillage), providing anytime feasibility while (under reliable communication) optimality improves with additional ADMM iterations. Communication failure resilience is achieved by treating tie-line mismatch as bounded disturbances and applying two-sided reserve margins (upward and downward) through constraint tightening, ensuring feasibility for any mismatch within the assumed bounds when sufficient reserve headroom exists. Demand response is incorporated using distinct models for shiftable loads (energy-by-deadline) and curtailable loads (penalized reduction). Evaluation on a five-microgrid benchmark under packet loss, burst outages, and topology changes confirms feasible reciprocal execution under loss; relative performance versus naive distributed MPC (B2) is seed-dependent, and DR ablation shows large degradations (about 5.9 × energy not served (ENS) and 5.8 × cost increases) when flexibility is removed.