What is it about?
This paper introduces a novel method to manage integrated energy systems—systems that combine electricity, gas, and heat—in a way that withstands cyber-attacks. It uses deep reinforcement learning (DRL) enhanced with a state-adversarial approach, which means the system is trained to recognize and counteract malicious data or disruptions as if they were adversarial inputs. In addition, the method integrates demand response strategies and dynamic pricing to adapt energy use in real time. Simulation results demonstrate that this approach not only mitigates the impact of cyber threats but also improves economic performance by about 10% compared to conventional methods.
Featured Image
Photo by Frédéric Paulussen on Unsplash
Why is it important?
As energy systems become more interconnected and reliant on digital communications, they are increasingly vulnerable to cyber-attacks. These attacks can disrupt energy supply and lead to significant financial losses. The proposed method is crucial because it enhances the resilience of integrated energy systems by ensuring that even if cyber-attacks occur, the scheduling and operation of the system remain stable and efficient. This advancement is key to supporting the integration of renewable energy sources, maintaining grid stability, and securing the economic operation of modern energy infrastructures.
Perspectives
From my perspective, this research represents an exciting convergence of artificial intelligence and energy system management. The innovative use of state-adversarial deep reinforcement learning not only strengthens the system’s defense against cyber threats but also optimizes energy scheduling and economic performance. I find it particularly impressive how the authors integrate demand response and dynamic pricing into the model, addressing both technical and economic challenges. This multidisciplinary approach is a promising step toward making our energy systems more secure, resilient, and efficient—a critical need in today’s increasingly digital and renewable-driven energy landscape.
Professor/Clarivate Highly Cited Researcher/Associate Editor of IEEE TSG/TII/TSTE Yang Li
Northeast Electric Power University
Read the Original
This page is a summary of: Enhancing cyber-resilience in integrated energy system scheduling with demand response using deep reinforcement learning, Applied Energy, February 2025, Elsevier,
DOI: 10.1016/j.apenergy.2024.124831.
You can read the full text:
Contributors
The following have contributed to this page
