Towards a Reference Architecture for Large-Scale Smart Grids System of Systems

2015 ◽  
Author(s):  
Jennifer Perez ◽  
Jessica Diaz ◽  
Juan Garbajosa ◽  
Agustin Yague ◽  
Eloy Gonzalez ◽  
...  
Keyword(s):  
Smart Grids ◽  
Large Scale ◽  
System Of Systems ◽  
Reference Architecture

scholarly journals Large-scale smart grids as system of systems

2013 ◽  
Author(s):  
Jennifer Pérez ◽  
Jessica Díaz ◽  
Juan Garbajosa ◽  
Agustín Yagüe ◽  
Eloy Gonzalez ◽  
...  
Keyword(s):  
Smart Grids ◽  
Large Scale ◽  
System Of Systems

scholarly journals An integral control formulation of mean field game based large scale coordination of loads in smart grids

Automatica ◽  
2019 ◽  
Vol 100 ◽  
pp. 312-322 ◽  
Author(s):  
Arman C. Kizilkale ◽  
Rabih Salhab ◽  
Roland P. Malhamé
Keyword(s):  
Smart Grids ◽  
Large Scale ◽  
Mean Field ◽  
Integral Control ◽  
Mean Field Game

scholarly journals Short Time Solar Power Forecasting Using Persistence Extreme Learning Machine Approach

2021 ◽  
Vol 294 ◽  
pp. 01002
Author(s):  
Xiaoyan Xiang ◽  
Yao Sun ◽  
Xiaofei Deng
Keyword(s):  
Solar Energy ◽  
Extreme Learning Machine ◽  
Power Output ◽  
Smart Grids ◽  
Large Scale ◽  
Solar Power ◽  
Learning Algorithm ◽  
Power Performance ◽  
Energy Prediction ◽  
Learning Machine

Solar energy in nature is irregular, so photovoltaic (PV) power performance is intermittent, and highly dependent on solar radiation, temperature and other meteorological parameters. Accurately predicting solar power to ensure the economic operation of micro-grids (MG) and smart grids is an important challenge to improve the large-scale application of PV to traditional power systems. In this paper, a hybrid machine learning algorithm is proposed to predict solar power accurately, and Persistence Extreme Learning Machine(P-ELM) algorithm is used to train the system. The input parameters are the temperature, sunshine and solar power output at the time of i, and the output parameters are the temperature, sunshine and solar power output at the time i+1. The proposed method can realize the prediction of solar power output 20 minutes in advance. Mean absolute error (MAE) and root-mean-square error (RMSE) are used to characterize the performance of P-ELM algorithm, and compared with ELM algorithm. The results show that the accuracy of P-ELM algorithm is better in short-term prediction, and P-ELM algorithm is very suitable for real-time solar energy prediction accuracy and reliability.

Object-Oriented Applications in Marine Hydrodynamics

2012 ◽  
Author(s):  
Piotr J. Bandyk ◽  
Justin Freimuth ◽  
George Hazen
Keyword(s):  
Large Scale ◽  
Object Oriented ◽  
System Of Systems ◽  
Object Oriented Programming ◽  
Marine Hydrodynamics ◽  
Natural Approach ◽  
Complex Problems ◽  
Systems Model

Object-oriented programming offers a natural approach to solving complex problems by focusing on individual aspects, or objects, and describing the ways in which they interact using interfaces. Modularity, extensibility, and code re-use often make OOP more appealing than its procedural counterpart. Code can be implemented in a more intuitive way and often mirrors the theory it derives from. Two examples are given in the form of real programs: a 3D panel code solver and a system-of-systems model for seabasing and environment sensing. Both are examples of large-scale frameworks and leverage the benefits offered by the object-oriented paradigm.

Target Evaluation and Correlation Method (TECM) as an Assessment Approach to Global Earth Observation System of Systems (GEOSS)

2011 ◽  
Vol 2 (1) ◽  
pp. 36-62 ◽  
Author(s):  
Samuel Epelbaum ◽  
Mo Mansouri ◽  
Alex Gorod ◽  
Brian Sauser ◽  
Alexander Fridman
Keyword(s):  
Large Scale ◽  
Correlation Method ◽  
System Of Systems ◽  
Earth Observation ◽  
Observation System ◽  
Societal Benefit ◽  
Large Scale Network ◽  
Assessment Approach ◽  
Earth Observation System ◽  
Scale Network

The Global Earth Observation System of Systems (GEOSS) is a ten-year-long Implementation Plan, which commenced in 2005 as a group effort by numerous participating countries and organizations to build a large-scale network to effectively monitor and respond to the increasingly complex web of global environmental and socio-economic issues. This paper proposes the Target Evaluation and Correlation Method (TECM) as an assessment approach to GEOSS with its 241 Targets across the nine “Societal Benefit Areas,” along with a method to identify Target Correlation Levels (TCL). Applying TECM allows concluding whether the chosen targets within the GEOSS fall into the domain of System of Systems (SoS), while using TCLs delineates the extent of inclusion for these targets in the form of a system. Furthermore, this research investigates the possible ways of raising the correlation levels of the targets for the cases in which TCLs are low.

Link importance-based network recovery for large-scale failures in smart grids

2020 ◽  
Author(s):  
Huibin Jia ◽  
Yonghe Gai ◽  
Dongfang Xu ◽  
Yincheng Qi ◽  
Hongda Zheng
Keyword(s):  
Smart Grids ◽  
Large Scale ◽  
Network Recovery

scholarly journals A Deep and Scalable Unsupervised Machine Learning System for Cyber-Attack Detection in Large-Scale Smart Grids

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 80778-80788 ◽  
Author(s):  
Hadis Karimipour ◽  
Ali Dehghantanha ◽  
Reza M. Parizi ◽  
Kim-Kwang Raymond Choo ◽  
Henry Leung
Keyword(s):  
Machine Learning ◽  
Smart Grids ◽  
Large Scale ◽  
Attack Detection ◽  
Learning System ◽  
Cyber Attack ◽  
Unsupervised Machine Learning

scholarly journals Future Generation 5G Wireless Networks for Smart Grid: A Comprehensive Review

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2140 ◽  
Author(s):  
Sofana Reka. S ◽  
Tomislav Dragičević ◽  
Pierluigi Siano ◽  
S.R. Sahaya Prabaharan
Keyword(s):  
Smart Grid ◽  
Electricity Markets ◽  
Smart Grids ◽  
Large Scale ◽  
Business Models ◽  
Future Generation ◽  
Current Status ◽  
Complete Analysis ◽  
5G Networks ◽  
5G Network

Wireless cellular networks are emerging to take a strong stand in attempts to achieve pervasive large scale obtainment, communication, and processing with the evolution of the fifth generation (5G) network. Both the present day cellular technologies and the evolving new age 5G are considered to be advantageous for the smart grid. The 5G networks exhibit relevant services for critical and timely applications for greater aspects in the smart grid. In the present day electricity markets, 5G provides new business models to the energy providers and improves the way the utility communicates with the grid systems. In this work, a complete analysis and a review of the 5G network and its vision regarding the smart grid is exhibited. The work discusses the present day wireless technologies, and the architectural changes for the past years are shown. Furthermore, to understand the user-based analyses in a smart grid, a detailed analysis of 5G architecture with the grid perspectives is exhibited. The current status of 5G networks in a smart grid with a different analysis for energy efficiency is vividly explained in this work. Furthermore, focus is emphasized on future reliable smart grid communication with future roadmaps and challenges to be faced. The complete work gives an in-depth understanding of 5G networks as they pertain to future smart grids as a comprehensive analysis.

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