@Article{ee.2024.047680,
AUTHOR = {Fuju Zhou, Li Li, Tengfei Jia, Yongchang Yin, Aixiang Shi, Shengrong Xu},
TITLE = {Intelligent Power Grid Load Transferring Based on Safe Action-Correction Reinforcement Learning},
JOURNAL = {Energy Engineering},
VOLUME = {121},
YEAR = {2024},
NUMBER = {6},
PAGES = {1697--1711},
URL = {http://www.techscience.com/energy/v121n6/56586},
ISSN = {1546-0118},
ABSTRACT = {When a line failure occurs in a power grid, a load transfer is implemented to reconfigure the network by changing the states of tie-switches and load demands. Computation speed is one of the major performance indicators in power grid load transfer, as a fast load transfer model can greatly reduce the economic loss of post-fault power grids. In this study, a reinforcement learning method is developed based on a deep deterministic policy gradient. The tedious training process of the reinforcement learning model can be conducted offline, so the model shows satisfactory performance in real-time operation, indicating that it is suitable for fast load transfer. Considering that the reinforcement learning model performs poorly in satisfying safety constraints, a safe action-correction framework is proposed to modify the learning model. In the framework, the action of load shedding is corrected according to sensitivity analysis results under a small discrete increment so as to match the constraints of line flow limits. The results of case studies indicate that the proposed method is practical for fast and safe power grid load transfer.},
DOI = {10.32604/ee.2024.047680}
}