Multi-Temporal Optimization Scheduling of Zero-Carbon Distribution Networks Based on Fuzzy Chance Constraint

Abstract

With the continuous grid connection of renewable energy sources on the distribution network side, their volatility and uncertainty pose significant challenges to the operation of the distribution grid. This paper proposes a multi-temporal optimization scheduling strategy for a zero-carbon distribution system. The focus is on wind, solar, and hydropower components, and the coordination of flexible resources across different time scales. The fuzzy change constraint is employed to handle uncertainties associated with generation and demand, offering a more realistic representation of intermittent power source outputs and load uncertainties. The proposed two-stage optimization model, utilizing fuzzy chance constraints, establishes a balance between economic efficiency and renewable energy integration rates. The methodology is validated using the IEEE 33-node test system, demonstrating improvements in economic performance and renewable energy integration.