TY - JOUR
T1 - Dynamic Security Margin Management via Deep Reinforcement Learning
AU - Samuel Boateng
AU - Kwesi Mensah
AU - Kojo Asante
AU - Linda Owusu
JF - Journal of Smart Grids, Energy Analytics and Optimization
JO - J Smart Grids Energy Anal Optim
Y1 - 2025
VL - 4
IS - 2
SP - 108
N2 - This paper presents a real-time preventive control approach utilizing deep reinforcement learning (DRL) to identify optimal control actions that maintain an adequate secure operating limit (SOL) in power systems. The SOL represents the boundary of the most stressed pre-contingency operating condition that the system can endure following a specified set of credible contingencies while satisfying predefined stability requirements. The proposed DRL technique employs a hybrid control framework that enables simultaneous management of both discrete and continuous control variables. Its effectiveness is assessed using a modified Nordic32 test system. Findings indicate that the DRL method rapidly acquires a robust control policy capable of securing sufficient SOL across various operating conditions. The approach is benchmarked against a rule-based lookup table method and a DRL variant restricted to discrete actions. Results demonstrate that the hybrid DRL controller consistently outperforms the alternatives across all evaluated test cases, highlighting the advantages of jointly optimizing discrete and continuous variables for greater flexibility and operational efficiency.
UR - https://isespub.com/v245855384
ER -