Adaptive power system emergency control using deep reinforcement learning

Q Huang, R Huang, W Hao, J Tan… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Power system emergency control is generally regarded as the last safety net for grid security
and resiliency. Existing emergency control schemes are usually designed offline based on …

Gridlearn: Multiagent reinforcement learning for grid-aware building energy management

A Pigott, C Crozier, K Baker, Z Nagy - Electric power systems research, 2022 - Elsevier
Increasing amounts of distributed generation in distribution networks can provide both
challenges and opportunities for voltage regulation across the network. Intelligent control of …

Multi-agent deep reinforcement learning for voltage control with coordinated active and reactive power optimization

D Hu, Z Ye, Y Gao, Z Ye, Y Peng… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
The increasing penetration of distributed renewable energy resources causes voltage
fluctuations in distribution networks. The controllable active and reactive power resources …

Deep-reinforcement-learning-based autonomous voltage control for power grid operations

J Duan, D Shi, R Diao, H Li, Z Wang… - … on Power Systems, 2019 - ieeexplore.ieee.org
In this letter, a novel autonomous control framework “Grid Mind” is proposed for the secure
operation of power grids based on cutting-edge artificial intelligence (AI) technologies. The …

A graph policy network approach for volt-var control in power distribution systems

XY Lee, S Sarkar, Y Wang - Applied Energy, 2022 - Elsevier
Volt-var control (VVC) is the problem of operating power distribution systems within healthy
regimes by controlling actuators in power systems. Existing works have mostly adopted the …

Reinforcement learning applied to an electric water heater: From theory to practice

F Ruelens, BJ Claessens, S Quaiyum… - … on Smart Grid, 2016 - ieeexplore.ieee.org
Electric water heaters have the ability to store energy in their water buffer without impacting
the comfort of the end user. This feature makes them a prime candidate for residential …

Powergridworld: A framework for multi-agent reinforcement learning in power systems

D Biagioni, X Zhang, D Wald, D Vaidhynathan… - Proceedings of the …, 2022 - dl.acm.org
We present the PowerGridworld open source software package to provide users with a
lightweight, modular, and customizable framework for creating power-systems-focused, multi …

Controlling distributed energy resources via deep reinforcement learning for load flexibility and energy efficiency

S Touzani, AK Prakash, Z Wang, S Agarwal, M Pritoni… - Applied Energy, 2021 - Elsevier
Behind-the-meter distributed energy resources (DERs), including building solar photovoltaic
(PV) technology and electric battery storage, are increasingly being considered as solutions …

A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems

L Cheng, T Yu - International Journal of Energy Research, 2019 - Wiley Online Library
The new generation of artificial intelligence (AI), called AI 2.0, has recently become a
research focus. Data‐driven AI 2.0 will accelerate the development of smart energy and …

(Deep) reinforcement learning for electric power system control and related problems: A short review and perspectives

M Glavic - Annual Reviews in Control, 2019 - Elsevier
This paper reviews existing works on (deep) reinforcement learning considerations in
electric power system control. The works are reviewed as they relate to electric power …