IET Smart Grid | |
Big data analytics in smart grids: state-of-the-art, challenges, opportunities, and future directions | |
Xiaping Zhang1  Kwok Cheung2  Rui Zhang3  Yusheng Luo4  Rob Hovsapian4  Manish Mohanpurkar4  Kurt S. Myers4  Song Zhang5  Sumit Paudyal6  Power Zhao7  Bishnu P. Bhattarai8  Reinaldo Tonkoski9  Milos Manic1,10  | |
[1] California Independent System Operator;GE Grid Solutions;IBM Research;Idaho National Laboratory;Independent System Operator New England;Michigan Technological University;Oncor Electric Delivery;Pacific Northwest National Laboratory;South Dakota State University;Virginia Commonwealth University; | |
关键词: 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; | |
DOI : 10.1049/iet-stg.2018.0261 | |
来源: DOAJ |
【 摘 要 】
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.
【 授权许可】
Unknown