A review of deep reinforcement learning for smart building energy management
Global buildings account for about 30% of the total energy consumption and carbon
emission, raising severe energy and environmental concerns. Therefore, it is significant and …
emission, raising severe energy and environmental concerns. Therefore, it is significant and …
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 …
Deeponet-grid-uq: A trustworthy deep operator framework for predicting the power grid's post-fault trajectories
This paper proposes a novel data-driven method for the reliable prediction of the power
grid's post-fault trajectories, ie, the power grid's dynamic response after a disturbance or …
grid's post-fault trajectories, ie, the power grid's dynamic response after a disturbance or …
电力系统优化控制中强化学习方法应用及挑战
毕聪博， 唐聿劼， 罗永红， 陆超 - 中国电机工程学报, 2023 - epjournal.csee.org.cn
强化学习(reinforcement learning, RL) 方法目前已应用于电力系统的多个领域,
在电力系统优化与控制领域的一些应用展现出良好的结果. 但在强化学习方法落地于实际电力 …
在电力系统优化与控制领域的一些应用展现出良好的结果. 但在强化学习方法落地于实际电力 …
Meta-reinforcement learning-based transferable scheduling strategy for energy management
In Home Energy Management System (HEMS), the scheduling of energy storage equipment
and shiftable loads has been widely studied to reduce home energy costs. However …
and shiftable loads has been widely studied to reduce home energy costs. However …
Rapid adaptation for active pantograph control in high-speed railway via deep meta reinforcement learning
Active pantograph control is the most promising technique for reducing contact force (CF)
fluctuation and improving the train's current collection quality. Existing solutions, however …
fluctuation and improving the train's current collection quality. Existing solutions, however …
Data-driven joint voltage stability assessment considering load uncertainty: A variational Bayes inference integrated with multi-CNNs
Few studies have focused on assessing the transient and steady-state voltage stability
status of dynamic systems simultaneously. This motivated us to propose a new concept …
status of dynamic systems simultaneously. This motivated us to propose a new concept …
Efficient learning of power grid voltage control strategies via model-based deep reinforcement learning
This article proposes a model-based deep reinforcement learning (DRL) method to design
emergency control strategies for short-term voltage stability problems in power systems …
emergency control strategies for short-term voltage stability problems in power systems …
[PDF][PDF] 基于知识融合和深度强化学习的智能紧急切机决策
李舟平， 曾令康， 姚伟， 胡泽， 帅航， 汤涌， 文劲宇 - 中国电机工程学报, 2024 - csee.org.cn
紧急控制是在严重故障后维持电力系统暂态安全稳定的重要手段. 目前常用的“人在环路”
离线紧急控制决策制定方式存在效率不高, 严重依赖专家经验等问题, 该文提出一种基于知识 …
离线紧急控制决策制定方式存在效率不高, 严重依赖专家经验等问题, 该文提出一种基于知识 …