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Title: Big data analytics in smart grids: state-of-the-art, challenges, opportunities, and future directions

Abstract

Big data has a potential to unlock novel groundbreaking opportunities in the power grid sector that enhances a multitude of technical, social, and economic gains. The currently untapped potential of applying the science of big data for better planning and operation of the power grid is a very challenging task and needs significant efforts all-around. As power grid technologies evolve in conjunction with measurement and communication technologies, this results in unprecedented amount of heterogeneous big data sets from diverse sources. In particular, computational complexity, data security, and operational integration of big data into utility decision frameworks are the key challenges to transform the heterogeneous large dataset into actionable outcomes. Moreover, due to the complex nature of power grids along with the need to balance power in real time, seamless integration of big data into utility operations is very critical. In this context, big data analytics combined with grid visualization can lead to better situational awareness and predictive decisions. This paper presents a comprehensive state-of-the-art review of big data analytics and its applications in power grids, and also identifies challenges and opportunities from utility, industry, and research perspectives. The paper analyzes research gaps and presents insights on future research directions tomore » integrate big data analytics into electric utility decision framework. Detailed information for utilities looking to apply big data analytics and details insights on how utilities can enhance revenue streams and bring disruptive innovation in the industry are discussed. More importantly, general guidelines for utilities to make the right investment in the adoption of big data analytics by unveiling interdependencies among critical infrastructures and operations are also provided.« less

Authors:
 [1];  [2]; ORCiD logo [3]; ORCiD logo [3];  [4];  [5]; ORCiD logo [3]; ORCiD logo [3];  [6];  [7];  [8];  [9];  [10]
+ Show Author Affiliations
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  2. Michigan Technological Univ., Houghton, MI (United States)
  3. Idaho National Lab. (INL), Idaho Falls, ID (United States)
  4. GE Grid Solutions, Redmond, WA (United States)
  5. South Dakota State Univ., Brookings, SD (United States)
  6. IBM Research, Almaden, CA (United States)
  7. Oncor Electric Delivery, Dallas, TX (United States)
  8. Virginia Commonwealth Univ., Richmond, VA (United States)
  9. Independent System Operator New England, Holyoke, MA (United States)
  10. California Independent System Operator, Folsom, CA (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Idaho National Lab. (INL), Idaho Falls, ID (United States)
Sponsoring Org.:
USDOE Office of Nuclear Energy (NE)
OSTI Identifier:
1639296
Alternate Identifier(s):
OSTI ID: 1559939
Report Number(s):
PNNL-SA-140724; INL/JOU-17-42462-Rev000
Journal ID: ISSN 2515-2947
Grant/Contract Number:  
AC05-76RL01830; AC07-05ID14517
Resource Type:
Accepted Manuscript
Journal Name:
IET Smart Grid
Additional Journal Information:
Journal Volume: 2; Journal Issue: 2; Journal ID: ISSN 2515-2947
Publisher:
The Institution of Engineering and Technology
Country of Publication:
United States
Language:
English
Subject:
13 HYDRO ENERGY; 24 POWER TRANSMISSION AND DISTRIBUTION; 14 SOLAR ENERGY; big data; data analysis; smart power grids; power system planning; power engineering computing; big data analytics; operational decision framework; power grid sector; power grid technologies; heterogeneous big data sets; computational complexity; data security; data integration; data analytics; grid modernization; high performance computing; Smart Grid; visualization

Citation Formats

Bhattarai, Bishnu P., Paudyal, Sumit, Luo, Yusheng, Mohanpurkar, Manish, Cheung, Kwok, Tonkoski, Reinaldo, Hovsapian, Rob, Myers, Kurt S., Zhang, Rui, Zhao, Power, Manic, Milos, Zhang, Song, and Zhang, Xiaping. Big data analytics in smart grids: state-of-the-art, challenges, opportunities, and future directions. United States: N. p., 2019. Web. doi:10.1049/iet-stg.2018.0261.
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Bhattarai, Bishnu P., Paudyal, Sumit, Luo, Yusheng, Mohanpurkar, Manish, Cheung, Kwok, Tonkoski, Reinaldo, Hovsapian, Rob, Myers, Kurt S., Zhang, Rui, Zhao, Power, Manic, Milos, Zhang, Song, & Zhang, Xiaping. Big data analytics in smart grids: state-of-the-art, challenges, opportunities, and future directions. United States. https://doi.org/10.1049/iet-stg.2018.0261
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Bhattarai, Bishnu P., Paudyal, Sumit, Luo, Yusheng, Mohanpurkar, Manish, Cheung, Kwok, Tonkoski, Reinaldo, Hovsapian, Rob, Myers, Kurt S., Zhang, Rui, Zhao, Power, Manic, Milos, Zhang, Song, and Zhang, Xiaping. Thu . "Big data analytics in smart grids: state-of-the-art, challenges, opportunities, and future directions". United States. https://doi.org/10.1049/iet-stg.2018.0261. https://www.osti.gov/servlets/purl/1639296.
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@article{osti_1639296,
title = {Big data analytics in smart grids: state-of-the-art, challenges, opportunities, and future directions},
author = {Bhattarai, Bishnu P. and Paudyal, Sumit and Luo, Yusheng and Mohanpurkar, Manish and Cheung, Kwok and Tonkoski, Reinaldo and Hovsapian, Rob and Myers, Kurt S. and Zhang, Rui and Zhao, Power and Manic, Milos and Zhang, Song and Zhang, Xiaping},
abstractNote = {Big data has a potential to unlock novel groundbreaking opportunities in the power grid sector that enhances a multitude of technical, social, and economic gains. The currently untapped potential of applying the science of big data for better planning and operation of the power grid is a very challenging task and needs significant efforts all-around. As power grid technologies evolve in conjunction with measurement and communication technologies, this results in unprecedented amount of heterogeneous big data sets from diverse sources. In particular, computational complexity, data security, and operational integration of big data into utility decision frameworks are the key challenges to transform the heterogeneous large dataset into actionable outcomes. Moreover, due to the complex nature of power grids along with the need to balance power in real time, seamless integration of big data into utility operations is very critical. In this context, big data analytics combined with grid visualization can lead to better situational awareness and predictive decisions. This paper presents a comprehensive state-of-the-art review of big data analytics and its applications in power grids, and also identifies challenges and opportunities from utility, industry, and research perspectives. The paper analyzes research gaps and presents insights on future research directions to integrate big data analytics into electric utility decision framework. Detailed information for utilities looking to apply big data analytics and details insights on how utilities can enhance revenue streams and bring disruptive innovation in the industry are discussed. More importantly, general guidelines for utilities to make the right investment in the adoption of big data analytics by unveiling interdependencies among critical infrastructures and operations are also provided.},
doi = {10.1049/iet-stg.2018.0261},
journal = {IET Smart Grid},
number = 2,
volume = 2,
place = {United States},
year = {2019},
month = {7}
}
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Publisher's Version of Record
https://doi.org/10.1049/iet-stg.2018.0261
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Figures / Tables:

Fig. 1 Fig. 1: Sources of non-electrical and electrical big dataset in smart grids
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  • DOI: 10.1109/AINA.2014.32

A reliable data aggregation mechanism with Homomorphic Encryption in Smart Grid AMI networks
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  • 2016 13th IEEE Annual Consumer Communications & Networking Conference (CCNC)
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  • 2012 9th Web Information Systems and Applications Conference (WISA), 2012 Ninth Web Information Systems and Applications Conference
  • DOI: 10.1109/WISA.2012.27

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journal, January 2018

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  • IET Generation, Transmission & Distribution, Vol. 12, Issue 4
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Correlation Analysis of Big Data to Support Machine Learning
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  • 2015 Fifth International Conference on Communication Systems and Network Technologies (CSNT)
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  • 2012 IEEE/PES Transmission and Distribution Conference and Exposition (T&D), PES T&D 2012
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  • 2011 IEEE First International Workshop on Smart Grid Modeling and Simulation (SGMS)
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Addressing the challenges for integrating micro-synchrophasor data with operational system applications
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Distributed data management in energy sector using Hadoop
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An experimental approach towards big data for analyzing memory utilization on a hadoop cluster using HDFS and MapReduce
conference, August 2014

  • Pal, Amrit; Agrawal, Sanjay
  • 2014 International Conference on Networks & Soft Computing (ICNSC), 2014 First International Conference on Networks & Soft Computing (ICNSC2014)
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Benchmarking Streaming Computation Engines: Storm, Flink and Spark Streaming
conference, May 2016

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Demand response targeting using big data analytics
conference, October 2013

Real Time Dynamics Monitoring System (RTDMS®) for use with SynchroPhasor technology in power systems
conference, July 2011

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Big data analytics for demand response: Clustering over space and time
conference, October 2015

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Big Data Analytics for Discovering Electricity Consumption Patterns in Smart Cities
journal, March 2018

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The role of big data in improving power system operation and protection
conference, August 2013

  • Kezunovic, Mladen; Xie, Le; Grijalva, Santiago
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Cyber Physical Security Analytics for Anomalies in Transmission Protection Systems
conference, September 2018

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A cloud-based architecture for Big-Data analytics in smart grid: A proposal
conference, December 2013

  • Mayilvaganan, M.; Sabitha, M.
  • 2013 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)
  • DOI: 10.1109/ICCIC.2013.6724168
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    A Novel on Transmission Line Tower Big Data Analysis Model Using Altered K-means and ADQL
    journal, June 2019

    Design of Experiments in the Methodology for Interoperability Testing: Evaluating AMI Message Exchange
    journal, March 2019

    • Andreadou, Nikoleta; Lucas, Alexandre; Tarantola, Stefano
    • Applied Sciences, Vol. 9, Issue 6
    • DOI: 10.3390/app9061221
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