Deep reinforcement learning for power system applications: An overview

Z Zhang, D Zhang, RC Qiu - CSEE Journal of Power and …, 2019 - ieeexplore.ieee.org
Due to increasing complexity, uncertainty and data dimensions in power systems,
conventional methods often meet bottlenecks when attempting to solve decision and control …

Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions

Z Shi, W Yao, Z Li, L Zeng, Y Zhao, R Zhang, Y Tang… - Applied Energy, 2020 - Elsevier
Smart grid is the new trend for clean, sustainable, efficient and reliable energy generation,
delivery and use. To ensure stable and secure operation is essential for the smart grid …

Review on the research and practice of deep learning and reinforcement learning in smart grids

D Zhang, X Han, C Deng - CSEE Journal of Power and Energy …, 2018 - ieeexplore.ieee.org
Smart grids are the developmental trend of power systems and they have attracted much
attention all over the world. Due to their complexities, and the uncertainty of the smart grid …

Adaptive power system emergency control using deep reinforcement learning

Q Huang, R Huang, W Hao, J Tan… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Power system emergency control is generally regarded as the last safety net for grid security
and resiliency. Existing emergency control schemes are usually designed offline based on …

A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems

L Cheng, T Yu - International Journal of Energy Research, 2019 - Wiley Online Library
The new generation of artificial intelligence (AI), called AI 2.0, has recently become a
research focus. Data‐driven AI 2.0 will accelerate the development of smart energy and …

Reinforcement learning in sustainable energy and electric systems: A survey

T Yang, L Zhao, W Li, AY Zomaya - Annual Reviews in Control, 2020 - Elsevier
The dynamic nature of sustainable energy and electric systems can vary significantly along
with the environment and load change, and they represent the features of multivariate, high …

Explainable AI in deep reinforcement learning models for power system emergency control

K Zhang, J Zhang, PD Xu, T Gao… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Artificial intelligence (AI) technology has become an important trend to support the analysis
and control of complex and time-varying power systems. Although deep reinforcement …

[HTML][HTML] Designing an optimal microgrid control system using deep reinforcement learning: A systematic review

NFP Dinata, MAM Ramli, MI Jambak, MAB Sidik… - … Science and Technology …, 2024 - Elsevier
Microgrid systems play a pivotal role in the integration of renewable energy sources and
enhancing electrical grid resilience. Deep Reinforcement Learning (DRL), a subset of …

Deep reinforcement learning for economic dispatch of virtual power plant in internet of energy

L Lin, X Guan, Y Peng, N Wang… - IEEE Internet of …, 2020 - ieeexplore.ieee.org
With the high penetration of large-scale distributed renewable energy generation, the power
system is facing enormous challenges in terms of the inherent uncertainty of power …

Explainable AI in deep reinforcement learning models: A shap method applied in power system emergency control

K Zhang, P Xu, J Zhang - 2020 IEEE 4th conference on energy …, 2020 - ieeexplore.ieee.org
The application of artificial intelligence (AI) system is more and more extensive, using the
explainable AI (XAI) technology to explain why machine learning (ML) models make certain …