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Dynamic Matching Markets in Power Grid: Concepts and Solution using Deep Reinforcement Learning [article]

Majid Majidi, Deepan Muthirayan, Masood Parvania, Pramod P. Khargonekar
2021 arXiv   pre-print
This paper proposes an alternative to bulk load flexibility options for managing uncertainty in power markets: a reinforcement learning based dynamic matching market.  ...  We propose a novel hybrid learning-based model for maximizing social welfare in the dynamic matching market.  ...  Matching Markets in Power Systems: Concepts and Solution using Deep Reinforcement Learning  ... 
arXiv:2104.05654v4 fatcat:rsggsbuqmje6dl5usgyd26hewe

2020 Index IEEE Transactions on Smart Grid Vol. 11

2020 IEEE Transactions on Smart Grid  
Expansion Planning for Active Distribution Network in Deregulated Retail Power Market; 1476-1488 Wang, J., see Arif, A., TSG Jan. 2020 673-685 Wang, J., see Cao, X., TSG Jan. 2020 696-709 Wang,  ...  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 July 2020 3008-3018 Two-Timescale Voltage Control in Distribution Grids Using Deep Reinforcement Learning.  ... 
doi:10.1109/tsg.2020.3044227 fatcat:qp5iogfnrnambc3qzuwvj4aega

A Survey on Energy Trading in the Smart Grid: Taxonomy, Research Challenges and Solutions

Shubhani Aggarwal, Neeraj Kumar, Sudeep Tanwar, Mamoun Alazab
2021 IEEE Access  
[69] described the learning module based on deep reinforcement learning in a holistic market model design as shown in Figure 5 .  ...  × ✓ × ✓ × [76] Deep reinforcement learning, Q-learning algorithm, Deep double Q-learning Data set ✓ ✓ × ✓ ✓ [77] Reinforcement learning model + Stackelberg game theory Among micro-grids  ... 
doi:10.1109/access.2021.3104354 fatcat:yoagfu6ebfeshjmz6c4wiakkaq

What blockchain can do for power grids?

Magda Foti, Manolis Vavalis
2018 Figshare  
An up-to-date comprehensive review for power engineers and practitioners.  ...  Acknowledgment This research has been financially supported by the General Secretariat for Research and Technology (GSRT) and the Hellenic Foundation for Research and Innovation (HFRI) (Scholarship Code  ...  Concepts and Background In recent years we have been monitoring the evolution of power grids in intelligent networks and lastly what is called the grid edge.  ... 
doi:10.6084/m9.figshare.6840656.v1 fatcat:3avr6lb3gna6bcstgqoqlgrkzy

How Smart is the Grid? [article]

Ermanno Lo Cascio, Zhenjun Ma, François Maréchal
2020 arXiv   pre-print
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.  ...  The findings are likely to suggest the urgent need for multidisciplinary cooperation to wisely address engineering and ontological challenges gravitating around the smart grid concept.  ...  This will enable complex deep learning networks functioning that would help to solve -in a prompt fashion -complex problems related to the smart grid control.  ... 
arXiv:2006.04943v2 fatcat:j4msv64kwzdr5dgrrzzvayvola

State-of-the-Art Artificial Intelligence Techniques for Distributed Smart Grids: A Review

Syed Saqib Ali, Bong Jun Choi
2020 Electronics  
As the smart grid involves various actors, such as energy produces, markets, and consumers, we also discuss how artificial intelligence and market liberalization can potentially help to increase the overall  ...  , distributed energy resources, and electric vehicles, to improve the reliability, energy efficiency, management, and security of the future power system.  ...  Through the virtual power plant (VPP) concept, DERs can get access and exposure across all energy markets.  ... 
doi:10.3390/electronics9061030 fatcat:ltpceutt5ngyvmg67aw7tm3zty

Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects

Mohamed Massaoudi, Haitham Abu-Rub, Shady S. Refaat, Ines Chihi, Fakhreddine S. Oueslati
2021 IEEE Access  
TAILORING QUANTUM DEEP LEARNING IN SMART GRID Quantum Deep Learning (QDL) can provide a huge breakthroughs to power systems [182] .  ...  FIGURE 1: Frequency of use of terms deep learning and smart grid in books from 1980 to 2020 (Google Books Ngram Viewer, 2020). E.  ...  His research interests include machine learning and deep learning techniques for energy management and Big data analytics in smart grid systems.  ... 
doi:10.1109/access.2021.3071269 fatcat:77gyjqaj2zeznc57r4n7kfbu7e

A Review on Communication Aspects of Demand Response Management for Future 5G IoT- Based Smart Grids

Sahar Ahmadzadeh, Gerard Parr, Wanqing Zhao
2021 IEEE Access  
A broad discussion on various DRM programmes in different layers of enhanced 5G IoT based SGs is given, paying particular attention to advances in machine learning (ML) and deep learning (DL) algorithms  ...  In recent power grids, the need for having a two-way flow of information and electricity is crucial.  ...  and progresses in advanced Machine Learning (ML) and Deep Learning (DL) algorithms.  ... 
doi:10.1109/access.2021.3082430 fatcat:qdsnwyvzerc63kxayds2hojuba

Smart Grid Big Data Analytics: Survey of Technologies, Techniques, and Applications

Dabeeruddin Syed, Ameema Zainab, Shady S. Refaat, Haitham Abu-Rub, Othmane Bouhali
2020 IEEE Access  
Smart grids have been gradually replacing the traditional power grids since the last decade.  ...  This includes the effective acquisition, transmission, processing, visualization, interpretation, and utilization of big data.  ...  The concept of a smart grid and the use of big data analytics will help to manage the power systems better and also to increase resilience.  ... 
doi:10.1109/access.2020.3041178 fatcat:awgtqx6nordadbtjn2a4v4nxe4

Smart Grid Security and Privacy: From Conventional to Machine Learning Issues (Threats and Countermeasures)

Parya Haji Mirzaee, Mohammad Shojafar, Haitham Cruickshank, Rahim Tafazolli
2022 IEEE Access  
In addition, recently, there has been an ever-increasing use of machine intelligence and Machine Learning (ML) algorithms in different components of SG.  ...  In order to prevent potential attacks and vulnerabilities in evolving power networks, the need for additional studying security and privacy mechanisms is reinforced.  ...  Also, in [27] , the general ML and Deep Learning (DL) techniques and security applications in the concept of SG were surveyed.  ... 
doi:10.1109/access.2022.3174259 fatcat:txuebjhpnre73cq5lbx77ugmhq

Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges [article]

Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Veličković
2021 arXiv   pre-print
The last decade has witnessed an experimental revolution in data science and machine learning, epitomised by deep learning methods.  ...  While learning generic functions in high dimensions is a cursed estimation problem, most tasks of interest are not generic, and come with essential pre-defined regularities arising from the underlying  ...  Acknowledgements This text represents a humble attempt to summarise and synthesise decades of existing knowledge in deep learning architectures, through the geometric lens of invariance and symmetry.  ... 
arXiv:2104.13478v2 fatcat:odbzfsau6bbwbhulc233cfsrom

Grid Integration Challenges of Wind Energy: A Review

Shakir D. Ahmed, Fahad S. M. Al-Ismail, Md Shafiullah, Fahad A. Al-suliman, Ibrahim M. El-amin
2020 IEEE Access  
Many of the solutions used and proposed to mitigate the impact of these challenges, such as energy storage systems, wind energy policy, and grid codes, are also reviewed and discussed.  ...  Besides, socioeconomic, environmental, and electricity market challenges due to the grid integration of wind power are also investigated.  ...  ACKNOWLEDGMENT The authors would like to thank the Center of Research Excellence in Renewable Energy (CoRE-RE) and the Electrical Engineering Department at both King Fahd University of Petroleum & Minerals  ... 
doi:10.1109/access.2020.2964896 fatcat:cluy4udksvfklcsl253qee22au

Modeling and Analysis of Energy Harvesting and Smart Grid-Powered Wireless Communication Networks: A Contemporary Survey [article]

Shuyan Hu, Xiaojing Chen, Wei Ni, Xin Wang, Ekram Hossain
2019 arXiv   pre-print
The advancements in smart power grid and the advocation of "green communications" have inspired the wireless communication networks to harness energy from ambient environments and operate in an energy-efficient  ...  A significant part of the article is devoted to the redistribution of redundant (unused) energy harvested within cellular networks, the energy planning under dynamic pricing when smart grids are in place  ...  Deep (Reinforcement) Learning Deep (reinforcement) learning has been widely employed in image processing and natural language processing [212] , [213] .  ... 
arXiv:1912.13203v1 fatcat:rhnrbxe5wbd3banrfksuxq4oyy

A Comprehensive Review on Sustainable Aspects of Big Data Analytics for Smart Grid

Vinoth Kumar Ponnusamy, Padmanathan Kasinathan, Rajvikram Madurai Elavarasan, Vinoth Ramanathan, Ranjith Kumar Anandan, Umashankar Subramaniam, Aritra Ghosh, Eklas Hossain
2021 Sustainability  
The current research work deals with the achievement of sustainability in the smart grid and efficient data management using big data analytics, that has social, economic, technical and political impacts  ...  Big data analytics, in association with the smart grid, enable better grid visualization and contribute toward the attainment of sustainability.  ...  Energy sectors and consumers can use artificial intelligence technology in descriptive, predictive and prescriptive analytics along with the machine learning concept.  ... 
doi:10.3390/su132313322 fatcat:sb2v2q32nnc3fklnd3354tusta

Smart Buildings in the Smart Grid: Contract-Based Design of an Integrated Energy Management System [chapter]

Mehdi Maasoumy, Pierluigi Nuzzo, Alberto Sangiovanni-Vincentelli
2015 Cyber Physical Systems Approach to Smart Electric Power Grid  
We use the concept of assume-guarantee contracts to formalize the requirements of the grid and the building subsystem as well as their interface.  ...  In a supply-following "smart" grid scenario, buildings can exploit remotely controllable thermostats and "smart" meters to communicate with energy providers, trade energy in real-time and offer frequency  ...  In practice, the grid operator predicts both the longterm power demand and its short-term deviations from historical data (e.g. weather patterns) by using machine learning algorithms.  ... 
doi:10.1007/978-3-662-45928-7_5 fatcat:cqrc5u2igvch5c47ikkkgdfhzm
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