Power systems big data analytics: An assessment of paradigm shift barriers and prospects

Akhavan-Hejazi, Hossein; Mohsenian-Rad, Hamed

doi:10.1016/j.egyr.2017.11.002

Title: Power systems big data analytics: An assessment of paradigm shift barriers and prospects

Abstract

Electric power systems are taking drastic advances in deployment of information and communication technologies; numerous new measurement devices are installed in forms of advanced metering infrastructure, distributed energy resources (DER) monitoring systems, high frequency synchronized wide-area awareness systems that with great speed are generating immense volume of energy data. However, it is still questioned that whether the today’s power system data, the structures and the tools being developed are indeed aligned with the pillars of the big data science. Further, several requirements and especial features of power systems and energy big data call for customized methods and platforms. This paper provides an assessment of the distinguished aspects in big data analytics developments in the domain of power systems. We perform several taxonomy of the existing and the missing elements in the structures and methods associated with big data analytics in power systems. We also provide a holistic outline, classifications, and concise discussions on the technical approaches, research opportunities, and application areas for energy big data analytics.

Authors:: Akhavan-Hejazi, Hossein; Mohsenian-Rad, Hamed

Publication Date:: 2018-11-01

Research Org.:: Univ. of California, Riverside, CA (United States)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office

OSTI Identifier:: 1633483

Alternate Identifier(s):: OSTI ID: 1613482

Grant/Contract Number:: EE0008001; 1405330; 1462530

Resource Type:: Published Article

Journal Name:: Energy Reports

Additional Journal Information:: Journal Name: Energy Reports Journal Volume: 4 Journal Issue: C; Journal ID: ISSN 2352-4847

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; energy; big data analytics; internet of energy; smart grid

Citation Formats


                    Akhavan-Hejazi, Hossein, and Mohsenian-Rad, Hamed. Power systems big data analytics: An assessment of paradigm shift barriers and prospects.  United States: N. p., 2018. 
Web.  doi:10.1016/j.egyr.2017.11.002.

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                    Akhavan-Hejazi, Hossein, & Mohsenian-Rad, Hamed. Power systems big data analytics: An assessment of paradigm shift barriers and prospects.  United States.  https://doi.org/10.1016/j.egyr.2017.11.002

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                    Akhavan-Hejazi, Hossein, and Mohsenian-Rad, Hamed. Thu .  
"Power systems big data analytics: An assessment of paradigm shift barriers and prospects".  United States.  https://doi.org/10.1016/j.egyr.2017.11.002.

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@article{osti_1633483,

  title        = {Power systems big data analytics: An assessment of paradigm shift barriers and prospects},

  author       = {Akhavan-Hejazi, Hossein and Mohsenian-Rad, Hamed},

  abstractNote = {Electric power systems are taking drastic advances in deployment of information and communication technologies; numerous new measurement devices are installed in forms of advanced metering infrastructure, distributed energy resources (DER) monitoring systems, high frequency synchronized wide-area awareness systems that with great speed are generating immense volume of energy data. However, it is still questioned that whether the today’s power system data, the structures and the tools being developed are indeed aligned with the pillars of the big data science. Further, several requirements and especial features of power systems and energy big data call for customized methods and platforms. This paper provides an assessment of the distinguished aspects in big data analytics developments in the domain of power systems. We perform several taxonomy of the existing and the missing elements in the structures and methods associated with big data analytics in power systems. We also provide a holistic outline, classifications, and concise discussions on the technical approaches, research opportunities, and application areas for energy big data analytics.},

  doi          = {10.1016/j.egyr.2017.11.002},

  journal      = {Energy Reports},

  number       = C,

  volume       = 4,

  place        = {United States},

  year         = {2018},

  month        = {11}

}

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Title: Power systems big data analytics: An assessment of paradigm shift barriers and prospects

Abstract

Citation Formats

Chance-Constrained AC Optimal Power Flow for Distribution Systems With Renewables journal, September 2017

Power System State Estimation and Bad Data Detection by Means of Conic Relaxation conference, January 2017

Business Intelligence and Analytics: From Big Data to Big Impact journal, January 2012

Unsupervised Disaggregation of Photovoltaic Production From Composite Power Flow Measurements of Heterogeneous Prosumers journal, September 2018

Forecasting of preprocessed daily solar radiation time series using neural networks journal, December 2010

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An unsupervised training method for non-intrusive appliance load monitoring journal, December 2014

A data-driven analysis of lightning-initiated contingencies at a distribution grid with a PV farm using Micro-PMU data conference, September 2017

Zero Duality Gap in Optimal Power Flow Problem journal, February 2012

MAD skills: new analysis practices for big data journal, August 2009

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Mobile social media for smart grids customer engagement: Emerging trends and challenges journal, January 2016

Exploiting massive PMU data analysis for LV distribution network model validation conference, September 2015

A formal definition of Big Data based on its essential features journal, April 2016

Modeling and Optimization for Big Data Analytics: (Statistical) learning tools for our era of data deluge journal, September 2014

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Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed journal, November 2011

Optimal Dispatch of Residential Photovoltaic Inverters Under Forecasting Uncertainties journal, January 2015

Accelerating the Benders decomposition for network-constrained unit commitment problems journal, June 2010

Event detection and localization in distribution grids with phasor measurement units conference, July 2017

Fully Distributed State Estimation for Wide-Area Monitoring Systems journal, September 2012

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Applying thermophysics for wind turbine drivetrain fault diagnosis using SCADA data journal, May 2016

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Addressing the challenges for integrating micro-synchrophasor data with operational system applications conference, July 2014

E-Sketch: Gathering large-scale energy consumption data based on consumption patterns conference, October 2014

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Social networking for Smart Grid users conference, April 2015

The US National Lightning Detection Network/sup TM/ and applications of cloud-to-ground lightning data by electric power utilities journal, January 1998

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Fuzzy ART Neural Network Algorithm for Classifying the Power System Faults journal, April 2005

A decomposition method for network-constrained unit commitment with AC power flow constraints journal, August 2015

Data-Driven Wind Turbine Power Generation Performance Monitoring journal, October 2015

Distribution Grid Reliability Versus Regulation Market Efficiency: An Analysis Based on Micro-PMU Data journal, November 2017

Chance-Constrained AC Optimal Power Flow for Distribution Systems With Renewables
journal, September 2017

Power System State Estimation and Bad Data Detection by Means of Conic Relaxation
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Business Intelligence and Analytics: From Big Data to Big Impact
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Unsupervised Disaggregation of Photovoltaic Production From Composite Power Flow Measurements of Heterogeneous Prosumers
journal, September 2018

Forecasting of preprocessed daily solar radiation time series using neural networks
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Wind turbine SCADA alarm analysis for improving reliability: Improving wind turbine reliability
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The internet of energy: a web-enabled smart grid system
journal, January 2012

Monitoring wind turbine gearboxes: Monitoring wind turbine gearboxes
journal, July 2012

Data-Driven Targeting of Customers for Demand Response
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An unsupervised training method for non-intrusive appliance load monitoring
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A data-driven analysis of lightning-initiated contingencies at a distribution grid with a PV farm using Micro-PMU data
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Zero Duality Gap in Optimal Power Flow Problem
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MAD skills: new analysis practices for big data
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The Smart Grid's Data Generating Potentials
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ScaLAPACK: a portable linear algebra library for distributed memory computers — design issues and performance
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Mobile social media for smart grids customer engagement: Emerging trends and challenges
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Exploiting massive PMU data analysis for LV distribution network model validation
conference, September 2015

A formal definition of Big Data based on its essential features
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Modeling and Optimization for Big Data Analytics: (Statistical) learning tools for our era of data deluge
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Accelerating the Benders decomposition for network-constrained unit commitment problems
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Event detection and localization in distribution grids with phasor measurement units
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The US National Lightning Detection Network/sup TM/ and applications of cloud-to-ground lightning data by electric power utilities
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