TY  - EJOU
AU  - Zhang, Ximing 
AU  - Li, Zhuohuan 
AU  - Quan, Xuexia 
AU  - Cheng, Kai 
AU  - Yu, Yang 

TI  - Curriculum-Learning-Guided Multi-Agent Deep Reinforcement Learning for N-1 Static Security Prevention and Control
T2  - Energy Engineering

PY  - 
VL  - 
IS  - 
SN  - 1546-0118

AB  - The “N-1” criterion represents a fundamental principle for assessing the reliability of power systems in static security analysis. Existing studies mainly rely on centralized single-agent reinforcement learning frameworks, where centralized control is difficult to cope with regional autonomy and communication delays. In high-dimensional state–action spaces, these approaches often suffer from low efficiency and unstable policies, limiting their applicability to large-scale grids. To address these issues, this paper proposes a Multi-Agent Deep Reinforcement Learning (MADRL) method enhanced with Curriculum Learning (CL) and Prioritized Experience Replay (PER). The proposed framework adopts a Centralized Training with Decentralized Execution (CTDE) paradigm, where independent agents are assigned to different system regions to enable autonomous decision-making and interregional coordination. In addition, the Actor–Critic (AC) architecture is refined with optimized value update rules to mitigate Q-value overestimation. A curriculum learning mechanism based on source–load fluctuation intensity further guides agents from simple to complex operating conditions, enhancing convergence and policy robustness. Simulation results on the IEEE 39-bus system demonstrate that the proposed method efficiently generates coordinated multi-region control strategies, eliminates voltage and current violations under N-1 contingencies, and consistently outperforms the baseline MADRL approach in terms of decision performance and robustness under fluctuating source–load scenarios.
KW  - Multi-agent deep reinforcement learning; static security analysis; preventive control; curriculum learning; N-1 guidelines

DO  - 10.32604/ee.2025.073912