[HTML][HTML] Towards a framework for measurements of power systems resiliency: Comprehensive review and development of graph and vector-based resilience metrics

MHN Amiri, F Guéniat - Sustainable Cities and Society, 2024 - Elsevier
Integration of smart grids into conventional power systems has introduced new challenges
and opportunities. Greater dependence on communication and measurement infrastructure …

Deep reinforcement learning for intelligent risk optimization of buildings under hazard

GA Anwar, X Zhang - Reliability Engineering & System Safety, 2024 - Elsevier
Risk management often involves retrofit optimization to enhance the performance of
buildings against extreme events but may result in huge upfront mitigation costs. Existing …

Adaptive robust load restoration via coordinating distribution network reconfiguration and mobile energy storage

R Xu, C Zhang, D Zhang, ZY Dong… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
In recent years, the power outages caused by catastrophic weather events have become an
imperative issue in power system research. Mutual impacts of pre-and post-event operation …

Multi-stage volt/var support in distribution grids: Risk-aware scheduling with real-time reinforcement learning control

M Mansourlakouraj, M Gautam, H Livani… - IEEE Access, 2023 - ieeexplore.ieee.org
The ever-increasing penetration of intermittent renewable resources in low-voltage power
grids necessitates efficient operational strategies for voltage regulation as well as power …

[HTML][HTML] Adaptive parameterized model predictive control based on reinforcement learning: A synthesis framework

D Sun, A Jamshidnejad, B De Schutter - Engineering Applications of …, 2024 - Elsevier
Parameterized model predictive control (PMPC) is one of the many approaches that have
been developed to alleviate the high computational requirement of model predictive control …

Review of planning and optimization of the renewable-energy-based micro-grid for rural electrification

NM Maletsie, S Krishnamurthy - 2024 32nd Southern African …, 2024 - ieeexplore.ieee.org
The research work provides a comprehensive overview of the challenges and opportunities
in the current energy landscape, particularly focusing on the role of microgrids in addressing …

A hierarchical deep reinforcement learning method for coupled transportation and power distribution system dispatching

Q Han, X Li, L He - Engineering Applications of Artificial Intelligence, 2025 - Elsevier
The randomness and dimensionality growth of variables in the Coupled transportation and
power distribution systems (CTPS) pose challenges for effectively solving CTPS dispatching …

A Memory-Based Graph Reinforcement Learning Method for Critical Load Restoration With Uncertainties of Distributed Energy Resource

B Fan, X Liu, G **ao, Y Xu, X Yang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
The integration of distributed energy resources into distribution networks, marked by its
inherent uncertainties, presents a substantial challenge for devising load restoration …

Integrated model and automatically designed solver for power system restoration

X Zhao, X Li, Q Zhao, B Yan, Y Shi, J Kang - Applied Soft Computing, 2025 - Elsevier
Power system restoration strategies schedule the power plants to re-energize the power
network and loads after a blackout. These strategies are of great significance to ensure a …

An Uncertainty Involved Two-Stage Energy Optimization Framework Based on Deep Reinforcement Learning for Steel Enterprise

Z Wang, Z Han, J Zhao, W Wang - IEEE Transactions on …, 2025 - ieeexplore.ieee.org
The optimization of energy systems within the steel industry faces challenges due to the
complexity of nonconvex models as well as supply and demand uncertainty. To address …