Adapting big data standards, maturity models to smart grid distributed generation: critical review

• Published in: IET Smart Grid Vol. 3; no. 4; pp. 508 - 519
• Main Authors: Sundararajan, Aditya, Hernandez, Alexander S, Sarwat, Arif I
• Format: Journal Article
• Language: English
• Published: Durham The Institution of Engineering and Technology 01.08.2020; John Wiley & Sons, Inc; Wiley
• Subjects: B8120K Distributed power generation; Big Data; big data standards; C6110B Software engineering techniques; C6130 Data handling techniques; C6160Z Other DBMS; C7410B Power engineering computing; Capability Maturity Model; capability maturity models; CMMs; Cybersecurity; Data management; data management techniques; Decision making; Distributed generation; distributed power generation; Electric utilities; Energy industry; grid-wide integration; Industrial development; Infrastructure; Power; power engineering computing; Relational data bases; Review Article; Sensors; Smart grid; smart grid DG; smart grid distributed generation; smart grid standards; Smart meters; smart power grids; Capability Maturity Model; big data standards; smart grid DG; Big Data; smart power grids; distributed power generation; data management techniques; power engineering computing; CMMs; smart grid distributed generation; grid-wide integration; capability maturity models; smart grid standards
• ISSN: 2515-2947; 2515-2947
• DOI: 10.1049/iet-stg.2019.0298

Big data standards and capability maturity models (CMMs) help developers build applications with reduced coupling and increased breadth of deployment. In smart grids, stakeholders currently work with data management techniques that are unique and customised to their own goals, thereby posing challenges for grid-wide integration and deployment. Although big data standards and CMMs exist for other domains, no work in the literature considers adapting them to smart grids, which will benefit from both. Further, existing smart grid standards and CMMs do not fully account for big data challenges. This study bridges the gap by analysing the role of big data in smart grids, and explores if and how big data standards and CMMs can be adapted specifically to ten distributed generation (DG) use-cases that use big data. In doing so, this work provides a useful starting point for researchers and industry members developing standards and CMM assessments for smart grid DG.