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

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 11/11/2018, Accepted 04/02/2019, Revised 23/01/2019, Published 05/02/2019

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.

Inspec keywords: smart power grids; data analysis; power engineering computing; power system planning; Big Data

Other keywords: operational decision framework; power system planning; heterogeneous big data sets; power grid technologies; computational complexity; data integration; power grid sector; big data analytics; data security

Subjects: Power system planning and layout; Other DBMS; Power engineering computing; Data handling techniques

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