[PDF][PDF] 新能源电力系统不确定优化调度方法研究现状及展望
林舜江， 冯祥勇， 梁炜焜， 杨悦荣， 刘明波 - 电力系统自动化, 2024 - epjournal.csee.org.cn
风电场和光伏电站出力的不确定性给电力系统优化调度带来很大技术挑战.
文中主要介绍了考虑新能源不确定性的电力系统优化调度方法的研究现状及后续研究方向展望 …
文中主要介绍了考虑新能源不确定性的电力系统优化调度方法的研究现状及后续研究方向展望 …
Navigating the landscape of deep reinforcement learning for power system stability control: A review
The widespread penetration of inverter-based resources has profoundly impacted the
electrical stability of power systems (PSs). Deepening grid integration of photovoltaic and …
electrical stability of power systems (PSs). Deepening grid integration of photovoltaic and …
Deep reinforcement learning for smart grid operations: algorithms, applications, and prospects
With the increasing penetration of renewable energy and flexible loads in smart grids, a
more complicated power system with high uncertainty is gradually formed, which brings …
more complicated power system with high uncertainty is gradually formed, which brings …
A review of safe reinforcement learning methods for modern power systems
Due to the availability of more comprehensive measurement data in modern power systems,
there has been significant interest in developing and applying reinforcement learning (RL) …
there has been significant interest in developing and applying reinforcement learning (RL) …
Online operational decision-making for integrated electric-gas systems with safe reinforcement learning
Increasing interdependencies between power and gas systems and integrating large-scale
intermittent renewable energy increase the complexity of energy management problems …
intermittent renewable energy increase the complexity of energy management problems …
Optimal operable power flow: Sample-efficient holomorphic embedding-based reinforcement learning
The nonlinearity of physical power flow equations divides the decision-making space into
operable and non-operable regions. Therefore, existing control techniques could be …
operable and non-operable regions. Therefore, existing control techniques could be …
Physics-Informed Reinforcement Learning for Real-Time Optimal Power Flow with Renewable Energy Resources
The serious uncertainties from the extensive integration of renewable energy generations
put forward a higher real-time requirement for power system dispatching. To provide …
put forward a higher real-time requirement for power system dispatching. To provide …
Reduced policy optimization for continuous control with hard constraints
Recent advances in constrained reinforcement learning (RL) have endowed reinforcement
learning with certain safety guarantees. However, deploying existing constrained RL …
learning with certain safety guarantees. However, deploying existing constrained RL …
[PDF][PDF] Review of machine learning techniques for optimal power flow
H Khaloie, M Dolanyi, JF Toubeau… - Available at SSRN …, 2024 - researchgate.net
ABSTRACT The Optimal Power Flow (OPF) problem is the cornerstone of power systems
operations, providing generators' most economical dispatch for power demands by fulfilling …
operations, providing generators' most economical dispatch for power demands by fulfilling …
Rethinking Safe Policy Learning for Complex Constraints Satisfaction: A Glimpse in Real-Time Security Constrained Economic Dispatch Integrating Energy Storage …
Reinforcement learning (RL) for real-time security constrained economic dispatch (RT-
SCED) problems have been the subject of significant research interest in recent years …
SCED) problems have been the subject of significant research interest in recent years …