Cooperative Multi-Twin Negotiation with Safety-Shielded Reinforcement Learning for Grid-Stress-

Abstract

Digital-twin (DT) technology and deep reinforcement learning (DRL) are converging rapidly in smart-grid research, yet most existing systems couple a single monolithic twin to an unconstrained learning controller. Such designs cannot express the heterogeneous, sometimes conflicting risks that arise from demand volatility, weather hazards, and market dynamics, and they provide no mechanism to weight those risks by their mutual reliability or to guarantee safe actuation. This paper proposes a Cooperative Multi-Digital-Twin framework(CMDT-SRL) governed by a meta-cognitive negotiation layer and executed by a Safety-shielded Reinforcement Learning controller. Four specialized twins, a gradient-boosted demand-forecasting twin, a grid-capacity twin, a weather-hazard twin, and a market-price twin, each emit a normalized risk. A meta-twin fuses these through confidence-aware risk-attention weighting and a disagreement penalty, producing a single negotiated risk and an explicit confidence signal that are handed to a Proximal Policy Optimization agent constrained by a runtime safety shield. Trained and evaluated on 22,513 hours of real PJM interconnection load data, the demand twin attains R² = 0.9940 (MAE = 341 MW). Against random, expert rule-based, and single-twin baselines, CMDT-SRL achieves the highest average reward (2.258) while driving the emergency load-shedding rate to 0.0%, demonstrating that cooperative twin negotiation with shielded control yields the most favorable safety–reliability–cost trade-off. The contribution is not the use of DRL for grid control per se, but the cooperative negotiation among multiple specialized twins prior to a provably guarded control decision.