Dynamic Resource Allocation and Collaborative Scheduling Strategies for the Timeliness of Smart-Grid Sensing Digital-Twin Data

Smart-grid sensing digital twins require dynamic resource allocation and collaborative scheduling to keep status updates from feeder segments, substation equipment areas, distributed-energy-resource access points, and alarmed devices fresh enough for cyber-physical synchronization. The difficulty is not only transmitting more data, but coordinating limited wireless resource blocks, feasible resource-block occupancy, and edge-computing capacity so that critical grid states are delivered and processed before they become stale. This paper studies hierarchical freshness-aware scheduling using Age of Information (AoI) as the main timeliness metric. Dynamic regional priorities are modeled as inputs supplied by the grid monitoring and event-management system; no external mobility-domain dataset is used to validate smart-grid sensing. The core freshness scheduling method combines value-network-assisted communication-resource budgeting, masked policy-gradient resource-block scheduling, and priority-aware computation offloading, while selective redundancy is treated as an optional enhancement for high-priority tail-risk tasks. The evaluation is conducted using a scenario-based smart-grid simulation covering normal monitoring, localized alarms, concurrent high-priority events, and priority migration. The results show that the core ValueNet-MaskedPG scheduling solver reduces mean priority-weighted AoI by about 12.6–15.9% compared with uniform first-come-first-served scheduling. When selective redundancy is enabled, high-priority-zone freshness is improved in event-driven scenarios, but the benefit for mean and peak AoI is scenario-dependent and comes at the cost of additional computation copies. The results support the usefulness of hierarchical scheduling under the considered scenario-based settings, while field SCADA/PMU or hardware-in-the-loop validation remains necessary before practical deployment.