基于通信拓扑优化的大规模光伏动态预测调峰策略OA

To address key challenges in large-scale residential distributed photovoltaic(DPV)integration into distribution networks for peak shaving,where traditional distributed model predictive control(DMPC)suffers from convergence difficulties,high computational complexity,and low communication coordination efficiency under high-dimensional coupled constraints,this paper proposes an adaptive projection-based DMPC(AP-DMPC)method.First,a two-stage solution mechanism of"unconstrained optimization+projection"is constructed,decomposing the original constrained distributed optimization problem into two independent steps:unconstrained collaborative optimization and feasible-region geometric projection.This effectively decouples the strongly coupled constraints among subsystems,significantly improving per-iteration computational efficiency and enhancing convergence robustness in ultra-large-scale scenarios.Then,an adaptive scaling factor based on the unconstrained solution magnitude is introduced into the iterative process,dynamically adjusting the step size according to the real-time solving status of each subsystem,thereby significantly accelerating convergence toward the global optimum.Finally,combined with a streaming computing architecture,a real-time collaborative optimization framework tailored to the radial(tree-like)topology of distribution networks is designed.Through theoretical analysis,the optimal backbone node configuration is determined to minimize aggregated communication delay and support high-concurrency,low-latency online dispatching of massive DPV resources,effectively overcoming communication and computation bottlenecks in large-scale distributed resource coordination.Experimental results demonstrate that,under real-time peak-shaving scenarios requiring millisecond-level response,the proposed method can stably maintain the peak-shaving error within 0.1%while fully satisfying real-time operational requirements.