Modeling of Integrated Energy Distribution Network and Power System Security and Stability Control Based on Perception System

Abstract

In response to the problems of lagging data collection and monitoring, lack of dynamism in system models, and long fault handling and recovery time in traditional integrated energy distribution network modeling, this study constructed a perception system-based integrated energy distribution network model and power system safety and stability control method. Firstly, by deploying multiple sensors at key nodes in the distribution network, real-time monitoring of power load, thermal energy flow, and gas transmission was achieved. The collected data was then fused using the Kalman filter and data fusion algorithms (such as Bayesian fusion) to eliminate noise and redundant information, resulting in high-quality system state estimation.
Then, the DreamerV2 algorithm was used to establish a dynamic model of the integrated energy distribution network, which simulated the energy flow and control strategies in the environment for long-term system optimization prediction. Finally, based on the data results after the DreamerV2 model prediction, the MILP (Mixed-integer Linear Planning) method was applied for optimization. Experimental results showed that the dynamic model had good processing capacity, high system stability. The transition response time was only 2.5 seconds, energy efficiency was about 85.5%, MTTR (Mean Time To Repair) was 1.69 hours, the average load tracking accuracy was 1.89%, the data proved the method in improving the security and stability of the power system.

Keywords: Electric Power System; Distribution Network Modeling; Deep Reinforcement Learning Algorithm; DreamerV2 Model; Data Acquisition

Cite As

G. Wang, X. Wang, Z. Chen, Y. Li, Z. Peng, "Modeling of Integrated Energy Distribution Network and Power System Security and Stability Control Based on Perception System", Engineering Intelligent Systems, vol. 34 no. 3, pp. 385-297, 2026.