[HTML][HTML] A comprehensive review of artificial intelligence approaches for smart grid integration and optimization
Technological advancements, urbanization, high energy demand, and global requirements
to mitigate carbon footprints have led to the adoption of innovative green technologies for …
to mitigate carbon footprints have led to the adoption of innovative green technologies for …
Reinforcement learning-based optimization for power scheduling in a renewable energy connected grid
AS Ebrie, YJ Kim - Renewable Energy, 2024 - Elsevier
Power scheduling is an NP-hard optimization problem that demands a delicate equilibrium
between economic costs and environmental emissions. In response to the growing concern …
between economic costs and environmental emissions. In response to the growing concern …
Reinforcement Learning for Efficient Power Systems Planning: A Review of Operational and Expansion Strategies
The efficient planning of electric power systems is essential to meet both the current and
future energy demands. In this context, reinforcement learning (RL) has emerged as a …
future energy demands. In this context, reinforcement learning (RL) has emerged as a …
[HTML][HTML] Reinforcement learning and A* search for the unit commitment problem
P de Mars, A O'Sullivan - Energy and AI, 2022 - Elsevier
Previous research has combined model-free reinforcement learning with model-based tree
search methods to solve the unit commitment problem with stochastic demand and …
search methods to solve the unit commitment problem with stochastic demand and …
Spatio-Temporal Deep Learning-Assisted Reduced Security-Constrained Unit Commitment
Security-constrained unit commitment (SCUC) is a computationally complex process utilized
in power system day-ahead scheduling and market clearing. SCUC is run daily and requires …
in power system day-ahead scheduling and market clearing. SCUC is run daily and requires …
[HTML][HTML] Deep reinforcement learning based model-free optimization for unit commitment against wind power uncertainty
Solving the unit commitment (UC) problem in a computationally efficient manner has
become increasingly crucial, especially in the context of high renewable energy penetration …
become increasingly crucial, especially in the context of high renewable energy penetration …
Feasibility layer aided machine learning approach for day-ahead operations
Day-ahead operation involves a complex and computationally intensive optimization
process to determine the generator commitment schedule and dispatch. The optimization …
process to determine the generator commitment schedule and dispatch. The optimization …
Reinforcement learning and mixed-integer programming for power plant scheduling in low carbon systems: Comparison and hybridisation
Decarbonisation is driving dramatic growth in renewable power generation. This increases
uncertainty in the load to be served by power plants and makes their efficient scheduling …
uncertainty in the load to be served by power plants and makes their efficient scheduling …
Rolling horizon wind-thermal unit commitment optimization based on deep reinforcement learning
The growing penetration of renewable energy has brought significant challenges for modern
power system operation. Academic research and industrial practice show that adjusting unit …
power system operation. Academic research and industrial practice show that adjusting unit …
Environment-friendly power scheduling based on deep contextual reinforcement learning
A novel approach to power scheduling is introduced, focusing on minimizing both economic
and environmental impacts. This method utilizes deep contextual reinforcement learning …
and environmental impacts. This method utilizes deep contextual reinforcement learning …