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PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems [article]

David Biagioni, Xiangyu Zhang, Dylan Wald, Deepthi Vaidhynathan, Rohit Chintala, Jennifer King, Ahmed S. Zamzam
2021 arXiv   pre-print
with existing training frameworks for reinforcement learning (RL).  ...  ) power systems where power flow solutions are required to define grid-level variables and costs.  ...  Reinforcement learning (RL) approaches have shown great potential in several power systems control and load management tasks [1] - [4] .  ... 
arXiv:2111.05969v1 fatcat:jisj34ccbnaghldi2oiobllfk4

A Reinforcement Learning-Based Framework for the Exploitation of Multiple RATs in the IoT

Ruben M. Sandoval, Sebastian Canovas-Carrasco, Antonio-Javier Garcia-Sanchez, Joan Garcia-Haro
2019 IEEE Access  
By making use of the mathematical framework of Reinforcement Learning, we have allowed IoT nodes to learn from previous real world data in order to derive optimal RAT-selection policies.  ...  in a real IoT device.  ...  To solve the MDP, a set of classic tools from Reinforcement Learning (RL) are proposed and applied.  ... 
doi:10.1109/access.2019.2938084 fatcat:vn5j6sff2vcghlmwcg3hf7ucgq

A Q-Cube Framework of Reinforcement Learning Algorithm for Continuous Double Auction among Microgrids

Ning Wang, Weisheng Xu, Weihui Shao, Zhiyu Xu
2019 Energies  
The emerging application of reinforcement learning algorithms in energy markets provides solutions to this problem.  ...  In this paper, we investigate the potential of applying a Q-learning algorithm into a continuous double auction mechanism.  ...  Introduction The power system has experienced the evolution from a traditional power grid to the smart grid and then to the Energy Internet (EI), driven by economic, technological and environment incentives  ... 
doi:10.3390/en12152891 fatcat:z3adfrb7ivgiljavsm7puxsbie

2021 Index IEEE Transactions on Smart Grid Vol. 12

2021 IEEE Transactions on Smart Grid  
Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name.  ...  -that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021.  ...  ., +, TSG March 2021 1135-1148 A Reinforcement Learning-Based Decision System for Electricity Pricing Plan Selection by Smart Grid End Users.  ... 
doi:10.1109/tsg.2021.3137570 fatcat:xjssgbcfnrcvzf4qqqwifu6e3u

Intelligent User-Centric Network Selection: A Model-Driven Reinforcement Learning Framework

Xinwei Wang, Jiandong Li, Lingxia Wang, Chungang Yang, Zhu Han
2019 IEEE Access  
In this paper, we propose a model-driven framework with a joint off-line and on-line way, which is able to achieve fast and optimal network selection through an alliance of machine learning and game theory  ...  Ultra-dense heterogeneous networks, as a novel network architecture in the fifth-generation mobile communication system (5G), promise ubiquitous connectivity and smooth experience, which take advantage  ...  The system using reinforcement learning can update itself continuously, while the systems using supervised and unsupervised learning, in general, are static.  ... 
doi:10.1109/access.2019.2898205 fatcat:nwykye6mjbbmdg7k6m2thrlxui

2020 Index IEEE Transactions on Smart Grid Vol. 11

2020 IEEE Transactions on Smart Grid  
Algorithm for Volt-VAR Control in Power Distribution Systems; TSG July 2020 3008-3018 Wang, W., see Gao, Y., TSG Nov. 2020 5357-5369 Wang, X., see Sheng, H., TSG Jan. 2020 95-105 Wang, X., Zhang,  ...  . , TSG Sept. 2020 4390-4402 Hosseinipour, A., and Hojabri, H., Small-Signal Stability Analysis and Active Damping Control of DC Microgrids Integrated With Distributed Electric Springs; 3737-3747 Hou  ...  ., +, TSG July 2020 3302-3312 Two-Timescale Voltage Control in Distribution Grids Using Deep Reinforcement Learning.  ... 
doi:10.1109/tsg.2020.3044227 fatcat:qp5iogfnrnambc3qzuwvj4aega

Smart Grids

Peter Palensky, Friederich Kupzog
2013 Annual Review Environment and Resources  
Smart grids are expected to make our power system more resilient, "green," and efficient; a challenge that the automotive industry could only manage by introducing digital controls in engines.  ...  For personal use only.  ...  A cost-benefit analysis and business models for EV management in a smart grid are given in Reference 131.  ... 
doi:10.1146/annurev-environ-031312-102947 fatcat:pvwm4gxukzfh3e6zg65ykdf7k4

Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons

Nicolas Frémaux, Henning Sprekeler, Wulfram Gerstner, Lyle J. Graham
2013 PLoS Computational Biology  
On the other hand, the theory of reinforcement learning provides a framework for reward-based learning.  ...  First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations.  ...  Conceived and designed the experiments: NF HS. Performed the experiments: NF. Analyzed the data: NF. Wrote the paper: NF WG. Contributed to the analytical derivations: NF HS WG.  ... 
doi:10.1371/journal.pcbi.1003024 pmid:23592970 pmcid:PMC3623741 fatcat:opuecl3nybaq3jtrt5xrdbt7di

GridLearn: Multiagent Reinforcement Learning for Grid-Aware Building Energy Management [article]

Aisling Pigott, Constance Crozier, Kyri Baker, Zoltan Nagy
2021 arXiv   pre-print
Intelligent control of smart inverters and other smart building energy management systems can be leveraged to alleviate these issues.  ...  GridLearn is a multiagent reinforcement learning platform that incorporates both building energy models and power flow models to achieve grid level goals, by controlling behind-the-meter resources.  ...  To overcome these issues, we develop a model-free multiagent reinforcement learning (MARL) framework that addresses both building-level and grid-level objectives, building upon the powerful CityLearn framework  ... 
arXiv:2110.06396v1 fatcat:z6ayrv3vd5d5bp5vjqkoq4l5zq

How Smart is the Grid? [article]

Ermanno Lo Cascio, Zhenjun Ma, François Maréchal
2020 arXiv   pre-print
Further, a taxonomic framework for assessing the level of sustainability of the grid is proposed.  ...  To this end, in this article, a systematic review of the emerging paradigms is presented, identifying intersectoral synergies and limitations with respect to the 'smart grid' concept.  ...  , building level e.g. electronic 'smart' devices for HVAC control, etc.  ... 
arXiv:2006.04943v2 fatcat:j4msv64kwzdr5dgrrzzvayvola

Smart grids as distributed learning control

D. J. Hill, T. Liu, G. Verbic
2012 2012 IEEE Power and Energy Society General Meeting  
in power system control.  ...  The topic of smart grids has received a lot of attention but from a scientific point of view it is a highly imprecise concept.  ...  The authors gratefully acknowledge discussions with many people including Zhaoyang Dong, Ian Hiskens, Ron Hui, Paul de Martini, Lucas Millmore and Felix Wu, and people associated with the SGSC Project  ... 
doi:10.1109/pesgm.2012.6344726 fatcat:kcljzj4kobfipdcaggbwndoqfy

Empowering Prosumer Communities in Smart Grid with Wireless Communications and Federated Edge Learning [article]

Afaf Taik and Boubakr Nour and Soumaya Cherkaoui
2021 arXiv   pre-print
Yet, the integration of prosumers in the energy market imposes new considerations in designing unified and sustainable frameworks for efficient use of the power and communication infrastructure.  ...  Accordingly, we propose a multi-level pro-decision framework for prosumer communities to achieve collective goals.  ...  ACKNOWLEDGMENTS The authors would like to thank the Natural Sciences and Engineering Research Council of Canada, for the financial support of this research.  ... 
arXiv:2104.03169v1 fatcat:6ng6xzw4nzc2fctvvq627c2viu

IoT-enabled Smart Energy Grid: Applications and Challenges

S. M. Abu Adnan Abir, Adnan Anwar, Jinho Choi, A. S. M. Kayes
2021 IEEE Access  
possible to transmit a huge amount of energy data and information in a faster, dependable and effective way for the management purpose of the smart grid system.  ...  In a smart Building Management System (BMS), a computer-oriented mechanism used to automatically monitor, control and regulate the electrical and mechanical apparatuses and elements of power systems, lighting  ... 
doi:10.1109/access.2021.3067331 fatcat:kfwpgepvqfd2xgat5r3rrp75yy

Exploring Smart Grid Possibilities: A Complex Systems Modelling ApproachExploring Smart Grid Possibilities: A Complex Systems Modelling Approach

R. Mark Rylatt, J. Richard Snape, Peter Allen, Babak M. Ardestani, Peter Boait, Ekkehard Boggasch, Denis Fan, Graham Fletcher, Rupert Gammon, Mark Lemon, Vijay Pakka, Christophe Rynikiewicz (+4 others)
2015 Smart GridSmart Grid  
However, there is an acknowledged and growing need for an integrated systems approach to the evaluation of smart grid initiatives.  ...  The architecture is described and related to realised examples of its use, both to model the electricity system as it is today and to model futures that have been envisioned in the literature.  ...  This scenario demonstrates the framework being used to produce a proof of concept model for a particular smart grid strategy at local household level.  ... 
doi:10.1515/sgrid-2015-0001 fatcat:6avcvguxojhvnlzkpix3qbxigm

Multi-Layer Cyber-Physical Security and Resilience for Smart Grid [article]

Quanyan Zhu
2018 arXiv   pre-print
The chapter will discuss the future directions of using game-theoretic tools in addressing multi-layer security issues in the smart grid.  ...  The smart grid is a large-scale complex system that integrates communication technologies with the physical layer operation of the energy systems.  ...  In Section 3, we focus on the cyber and physical layers of the smart grid and propose a general cross-layer framework for robust and resilient controller design.  ... 
arXiv:1810.00282v1 fatcat:dyh7dy3jizgvfd4xwg7ij46eu4
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