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[HTML][HTML] Towards a framework for measurements of power systems resiliency: Comprehensive review and development of graph and vector-based resilience metrics
Integration of smart grids into conventional power systems has introduced new challenges
and opportunities. Greater dependence on communication and measurement infrastructure …
and opportunities. Greater dependence on communication and measurement infrastructure …
Deep reinforcement learning for intelligent risk optimization of buildings under hazard
Risk management often involves retrofit optimization to enhance the performance of
buildings against extreme events but may result in huge upfront mitigation costs. Existing …
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
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 …
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
The ever-increasing penetration of intermittent renewable resources in low-voltage power
grids necessitates efficient operational strategies for voltage regulation as well as 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
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 …
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 …
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 …
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
The integration of distributed energy resources into distribution networks, marked by its
inherent uncertainties, presents a substantial challenge for devising load restoration …
inherent uncertainties, presents a substantial challenge for devising load restoration …
Integrated model and automatically designed solver for power system restoration
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 …
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
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 …
complexity of nonconvex models as well as supply and demand uncertainty. To address …