Multi-agent deep reinforcement learning for resilient recovery of electric–gas coupled energy systems under extreme disasters
Year: 2026
Type: article
Abstract: Under extreme disasters, there are still significant gaps in the cascading failure propagation and collaborative recovery decision-making of electric-gas coupled integrated energy systems, making it difficult to form effective dynamic recovery paths under high uncertainty conditions. This paper constructs a Markov decision process environment under disaster conditions, setting multi-dimensional state variables including node fault states, pressure and voltage distribution, and recovery resource constraints. It employs Multi-Agent Deep Reinforcement Learning (MA-DRL) combined with a centralized training and distributed execution architecture. Policy coordination between the power grid subsystem and the gas grid subsystem is achieved through joint action value evaluation and local policy execution. A cross-round reward structure is designed to cover factors such as energy availability, recovery speed, and recovery stability. A curriculum learning mechanism is introduced to address the exploration failure and reward signal sparsity that arise when policies are directly trained under high-disturbance conditions: by organizing training scenarios in order of ascending disaster intensity, the policy first acquires stable recovery behavior in low-disturbance environments and progressively transfers this structure to extreme disturbance conditions, yielding accelerated convergence and robust generalization across disaster scenarios. Delay effect modeling is incorporated to propagate the long-term consequences of repair decisions back to the learning signal. Experimental results show that, under disaster intensity level IV, the proposed MA-DRL collaborative recovery strategy achieves a system energy availability rate of 82.4% within 0–3 h post-disaster; during the post-disaster collaborative recovery process, the overall recovery completion time is 5.7 h, and the proportion of stable operation after recovery reaches 93.1%. This research demonstrates the effective path and technological potential of deep reinforcement learning in enhancing the resilience of coupled energy systems.
Source: Scientific Reports
Authors: Pei Zhang, Chao Yang, Jinghui He
Institution: Guangdong University of Technology
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W7220980048