| 引用本文: | 李强,蒲炬屹,李剑,等.基于通信拓扑优化的大规模光伏动态预测调峰策略[J].电力系统保护与控制,2026,54(09):102-111. |
| LI Qiang,PU Juyi,LI Jian,et al.Large‑scale photovoltaic dynamic prediction peak shaving strategy based on communication topology optimization[J].Power System Protection and Control,2026,54(09):102-111 |
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| 摘要: |
| 针对大规模户用分布式光伏(distributed photovoltaic, DPV)接入配电网参与调峰时，传统分布式模型预测控
制(distributed model predictive control, DMPC)在高维耦合约束下面临收敛困难、计算复杂度高以及通信协调效率低
下等关键问题，提出一种基于自适应投影策略的分布式模型预测控制(adaptive projection-based DMPC, AP-DMPC)
方法。首先，构建“无约束优化 + 投影”的两阶段求解机制，将原含约束的分布式优化问题分解为无约束协同优
化与可行域几何投影两个独立步骤，有效解耦子系统间的强耦合约束，在大幅提升单步迭代效率的同时显著增强
算法在超大规模场景下的收敛鲁棒性。然后，在迭代流程中引入基于无约束幅值解自适应缩放因子，根据子系统
实时求解状态动态调整步长，显著加速系统向全局最优解的收敛进程。最后，结合流式计算架构设计面向配电网
树状拓扑的实时协同优化模式，通过理论推导，确定最优主干节点配置，以最小化聚合通信延迟并支持高并发、
低时延的海量 DPV 资源在线调度，突破大规模分布式资源协同控制中通信与计算的瓶颈。实验结果表明，该方
法在满足毫秒级响应需求的实时调峰场景中，可将调峰误差稳定控制在0.1%以内，且满足调峰实时性要求。 |
| 关键词: 分布式光伏 调峰 分布式模型预测控制 流计算 |
| DOI：10.19783/j.cnki.pspc.251150 |
| 分类号: |
| 基金项目:国家自然科学基金项目资助(52577084)；重庆市科技创新重大研发项目资助(CSTB2024TIAD-STX0024) |
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| Large‑scale photovoltaic dynamic prediction peak shaving strategy based on communication topology optimization |
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LI Qiang, PU Juyi, LI Jian, CUI Qiushi, ZHANG Yigong, DI Jian, CHEN Haojia, WANG Yu
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School of Electrical Engineering, Chongqing University, Chongqing 400044, China
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| Abstract: |
| 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 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. |
| Key words: distributed photovoltaic peak shaving distributed model predictive control stream computing |
