5G-Advanced Network Slicing for Smart Grid Communications: Intelligent Resource Scheduling Under Energy-Efficiency and Performance Trade-Offs

Abstract

5G-Advanced network slicing is emerging as a promising communication framework for smart-grid services with diverse latency, reliability, bandwidth, and criticality requirements. In smart-grid communication infrastructures, however, slice scheduling must balance service differentiation with energy efficiency, fairness, and resilience under dynamic operating conditions. This paper presents an interpretable intelligent scheduling framework, where intelligence refers to state-aware, service-aware, energy-aware, and resilience-aware adaptation rather than purely black-box learning. The framework jointly considers slice admission, radio-resource allocation, edge-resource allocation, activity-state control, and disturbed-mode adaptation. The problem is formulated as a dynamic multi-objective scheduling problem incorporating delay, reliability, service utility, energy consumption, fairness, and resilience. On this basis, a hierarchical scheduling method is developed for normal, bursty, and degraded operating conditions. Evaluation under representative smart-grid scenarios, including mixed-service operation, demand-response events, distributed energy resource (DER) coordination surges, and degraded-capacity conditions, shows that the proposed method achieves a better overall balance among service-level agreement (SLA) satisfaction, energy efficiency, fairness, and resilience than benchmark strategies. The results indicate that intelligent 5G-Advanced slice scheduling is a promising standards-aligned approach for service-differentiated and dependable smart-grid communications.