Active power security correction strategy for power grids based on spatiotemporal graph deep reinforcement learning
Year: 2026
Type: article
Abstract: With the growing penetration of renewable energy sources, modern power systems are increasingly challenged in maintaining active power security. Conventional optimization-based correction methods often lack real-time adaptability and robustness, particularly under the uncertainty and volatility of renewable generation. Deep reinforcement learning (DRL) has emerged as a promising alternative, however, most existing DRL approaches overlook the graph-structured characteristics of power networks and the temporal dynamics induced by renewable fluctuations, which limits their correction performance. To address these challenges, this paper proposes a spatiotemporal graph-based DRL framework that integrates graph convolutional networks (GCNs) for topological feature representation and temporal convolutional networks (TCNs) for capturing dynamic system evolution. The correction problem is formulated as a Markov decision process (MDP), and the agent is trained using the soft actor-critic (SAC) algorithm, which enhances robustness through entropy-regularized exploration. By jointly learning spatial and temporal patterns, the proposed method improves the agent’s situational awareness and decision-making capability under uncertainty. Experiments on a modified IEEE 118-bus system demonstrate that the proposed approach achieves more effective corrective actions, faster convergence, and higher renewable utilization compared to state-of-the-art baselines. • Formulates active power security correction as an MDP with spatiotemporal states. • Proposes a spatiotemporal DRL agent using GCN for topology and TCN for dynamics. • Develops a GCN–TCN-enhanced SAC framework with improved robustness and convergence. • Demonstrates superior performance on the IEEE 118-bus system over DRL baselines.
Institutions: Xi'an Jiaotong University, Xi'an University of Technology
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,920
Awards: G2880741797, G6042602324, G7687820343
OpenAlex ID: W7119192240