Edge computing resource optimization allocation method for virtual power plants
TL;DR
The results indicate that edge-side resource allocation can improve computational reliability and operational scalability for automated VPP dispatch as well as improve computational reliability and operational scalability for automated VPP dispatch.
Abstract
Virtual power plants (VPPs) aggregate photovoltaic units, battery storage, electric-vehicle chargers, flexible loads, and optical/electrical sensing devices through cloud-edge-terminal platforms. Centralized cloud scheduling is often too slow for dispatch verification, emergency frequency-control, and burst telemetry processing. This paper proposes a priority- aware edge computing resource allocation method for VPPs. A cloud-edge-terminal model is established, heterogeneous VPP computing tasks are described by data size, CPU cycles, deadline, and operational priority, and a rolling-window allocator jointly optimizes offloading decision, CPU frequency, bandwidth quota, utilization balance, and emergency reserve. The method combines a deadline-priority score, feasibility repair, and dynamic reserve guidance so that routine forecasting and settlement tasks do not occupy capacity required by high-priority control tasks. A simulated IEEE 33-bus VPP with 280 distributed energy-resource endpoints is used for validation. Compared with cloud-only scheduling, the proposed method reduces average latency from 168 ms to 64 ms and decreases deadline violations from 18.6% to 4.9%. Ablation tests further show that priority scoring, feasibility repair, and reserve protection are all necessary for stable real- time operation. The results indicate that edge-side resource allocation can improve computational reliability and operational scalability for automated VPP dispatch.
