Energy efficient unified computing framework for smart grids with AI-driven communication, supercomputing, and energy perception orchestration

Abstract

The next-generation smart grid requires a unified computing framework that seamlessly integrates communication, high-performance computing (HPC), and AI to enable real-time energy perception, forecasting, and decision-making. Conventional architectures, which treat communication, computation, and control as independent modules, often suffer from latency, scalability limitations, and weak coordination across heterogeneous infrastructures. To overcome these constraints, this work proposes an energy-efficient unified computing framework where communication networks, HPC clusters, and AI orchestration operate as a tightly coupled ecosystem. AI modules handle deep learning–based perception of multi-source energy data and employ reinforcement learning to optimize dynamic load allocation and demand-side flexibility. Superscale HPC resources accelerate renewable forecasting, grid stability assessment, and large-scale optimization tasks. In parallel, adaptive communication units with edge-level compression and intelligent routing ensure low latency and resilience under varying network loads. The framework is evaluated through MATLAB/Simulink and Python co-simulation using HPC-enabled TensorFlow clusters and blockchain-secured IoT gateways. Experimental results demonstrate a System Orchestration Index (SOI) of 98.3 %, a Computational Efficiency Ratio (CER) of 37.5 %, a Demand Flexibility Index (DFI) of 33.8 %, and an end-to-end decision latency of 18 ms. Compared with conventional grid computing approaches, the proposed architecture achieves improvements of 9.4 % in orchestration efficiency, 7.8 % in computational efficiency, and 6.2 % in demand flexibility. These outcomes highlight the potential of an AI-driven, HPC-accelerated, and communication-adaptive unified computing paradigm for scalable and resilient smart grid operations.