Research on Edge Calculation Strategy for Power System Energy Storage Operation Optimization Under Dual-Carbon Goals

Abstract

Energy storage optimization is critical for improving the efficiency, stability, and low-carbon performance of modern power systems with high renewable-energy penetration. In smart grids, rapid storage dispatch must also account for power-electronic switching behavior, communication latency, and electromagnetic transient constraints that affect system reliability. This study proposes an edge-computing-based energy storage optimization framework under dual-carbon goals. The framework uses real-time information on electricity demand, renewable generation, battery state, market price, and carbon-emission factors to support local charging and discharging decisions. By combining edge-based optimization with carbon-aware modeling, the system jointly minimizes electricity cost, carbon emissions, and load fluctuations while maintaining battery operational constraints. Simulations based on the Electricity Market Dataset show that the proposed method reduces electricity cost from 3418 to 3293, decreases carbon emissions from 586.2 to 559.8 kg CO, and improves response time to 26 ms compared with 118 ms for cloud-based control. The results demonstrate that decentralized edge optimization can enhance energy-storage utilization, improve grid stability, and support low-carbon transformation in renewable-rich power systems.