Multi-objective partitioning and cooperative peak shaving for distributed photovoltaic clusters via multi-agent reinforcement learning

Introduction To address the urgent need for enhanced grid flexibility in high-penetration distributed photovoltaic (PV) systems, this paper proposes a novel two-stage framework integrating multi-objective cluster partitioning and multi-agent reinforcement learning (MARL) for cooperative peak shaving. Methods First, a comprehensive multi-objective optimization model defines PV cluster partitioning using three domain-specific metrics: available peak shaving margin (APSM), peak shaving synergy (PSS), and node correlation degree (NCD). This approach replaces conventional clustering algorithms, ensuring partitions balance electrical compactness, regulation capacity, and operational coordination. Subsequently, each partitioned cluster acts as an autonomous agent in a hierarchical MARL framework. Leveraging the multi-agent deep deterministic policy gradient (MADDPG) algorithm, agents collaboratively optimize active/reactive power allocation among clusters while coordinating unit-level control within clusters. Results Validated on the IEEE-33 node system, the strategy achieves a 29.39% reduction in peak-to-valley difference and 54.44% improvement in power balance deviation, significantly smoothing net load curves and enhancing voltage stability. Discussion This work demonstrates how MARL-driven coordination unlocks the inherent flexibility of distributed PV clusters, providing a scalable solution for grid-edge resource participation in system-level peak shaving.