Adaptive Edge–Cloud Framework for Real-Time Smart Grid Optimization with IIoT Analytics

Abstract

The large-scale integration of Distributed Energy Resources (DERs) in smart grids creates challenges related to real-time optimization, system scalability, and operational security. This paper presents GridOpt, a hybrid edge–cloud framework designed to address these challenges through distributed intelligence and coordinated control. In GridOpt, edge nodes handle latency-sensitive tasks, while cloud resources support the processing of large-scale grid data. Security is addressed through the integration of homomorphic encryption and blockchain-based consensus, together with an interoperability layer that enables coordination among heterogeneous grid components. Simulation results show that GridOpt achieves an average latency of 76 ms and an energy consumption of 25 Joules under high-throughput conditions. The framework further maintains scalability beyond 10 requests per second with a resource utilization of 54% in dense deployment scenarios. Comparative analysis indicates that GridOpt outperforms ECCGrid, JOintCS, and EdgeApp across key performance metrics.