Big data analytics in smart grids: state‐of‐the‐art, challenges, opportunities, and future directions. Issue 2 (1st March 2019)

• Record Type: Journal Article
• Authors: 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
• Is Part Of: IET smart grid. Volume 2:Issue 2(2019)
• Page Start: 141
• Page End: 154
• Publication Date: 2019-03-01
• Subjects: Big Data -- data analysis -- smart power grids -- power system planning -- power engineering computing; big data analytics -- power system planning -- operational decision framework -- power grid sector -- power grid technologies -- heterogeneous big data sets -- computational complexity -- data security -- data integration; B8110D Power system planning and layout -- C6130 Data handling techniques -- C6160Z Other DBMS -- C7410B Power engineering computing; Smart power grids -- Periodicals; Computer science -- Periodicals; Energy industries -- Periodicals; Broadcasting -- Periodicals; 333.79110285
• DOI: 10.1049/iet-stg.2018.0261
• Languages: English
• ISSNs: 2515-2947

Abstract

Abstract : Big data has potential to unlock novel groundbreaking opportunities in power grid that enhances a multitude of technical, social, and economic gains. As power grid technologies evolve in conjunction with measurement and communication technologies, this results in unprecedented amount of heterogeneous big data. In particular, computational complexity, data security, and operational integration of big data into power system planning and operational frameworks are the key challenges to transform the heterogeneous large dataset into actionable outcomes. In this context, suitable big data analytics combined with 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 power system planning and operational frameworks. Detailed information for utilities looking to apply big data analytics and insights on how utilities can enhance revenue streams and bring disruptive innovation are discussed. 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.