Data Management in Smart Grid

The smart grid (SG) allows integration of renewable energy sources, distributed generation (DG) and storage systems. This chapter builds on the concepts of data management and analytics in SG to build the foundation needed for data analytics to transform Big Data for high‐value action. Big data sources in SG generally fall into two main categories; electric utility data sources and supplementary data sources. The Big Data system will store, process, and mine information in an efficient manner to enhance different SG services. The chapter presents a list of the most common big data tools utilized by successful analytics developers to store, manage, and analyze big data. Big Data needs additional convincing methods to handle the massive amount of information in a limited time span. Any data exchange in SG must be effectively protected through specific “Privacy Concerns” which have potential privacy impacts of SG and smart meter systems.

[1]  Haitham Abu-Rub,et al.  Big data, better energy management and control decisions for distribution systems in smart grid , 2016, 2016 IEEE International Conference on Big Data (Big Data).

[2]  Nor Badrul Anuar,et al.  Blending Big Data Analytics: Review on Challenges and a Recent Study , 2020, IEEE Access.

[3]  Amardeep Singh,et al.  Big Data: Hadoop framework vulnerabilities, security issues and attacks , 2019, Array.

[4]  K. P. Soman,et al.  Apache Spark a Big Data Analytics Platform for Smart Grid , 2015 .

[5]  Sherali Zeadally,et al.  Performance analysis of data intensive cloud systems based on data management and replication: a survey , 2016, Distributed and Parallel Databases.

[6]  Feng Gao,et al.  Real-time Complex Event Processing and Analytics for Smart Grid , 2015, Complex Adaptive Systems.

[7]  Zoran Obradovic,et al.  Big data analytics for future electricity grids , 2020, Electric Power Systems Research.

[8]  Milos Manic,et al.  Big data analytics in smart grids: state-of-the-art, challenges, opportunities, and future directions , 2019 .

[9]  Feroz Zahid,et al.  Future Cloud Systems Design: Challenges and Research Directions , 2018, IEEE Access.

[10]  Xiaoming Li,et al.  Big Data Analytics and Deep Learning in Bioinformatics With Hadoop , 2019 .

[11]  Marco Aiello,et al.  The smart grid's data generating potentials , 2014, 2014 Federated Conference on Computer Science and Information Systems.

[12]  Nima Jafari Navimipour,et al.  The role of structured and unstructured data managing mechanisms in the Internet of things , 2019, Cluster Computing.

[13]  Tao Huang,et al.  Big data analytics in smart grids: a review , 2018, Energy Informatics.

[14]  Lukasz Golab,et al.  SMAS: A smart meter data analytics system , 2015, 2015 IEEE 31st International Conference on Data Engineering.

[15]  Maozhen Li,et al.  Hadoop-based framework for big data analysis of synchronised harmonics in active distribution network , 2017 .

[16]  Mladen Kezunovic.  Translational Knowledge: From Collecting Data to Making Decisions in a Smart Grid , 2011, Proceedings of the IEEE.

[17]  Wilson Rivera,et al.  Big Data Analysis and Visualization for the Smart Grid , 2017, 2017 IEEE International Congress on Big Data (BigData Congress).

[18]  Vangelis Marinakis,et al.  Big Data for Energy Management and Energy-Efficient Buildings , 2020, Energies.

[19]  Yonggang Wen,et al.  Toward Scalable Systems for Big Data Analytics: A Technology Tutorial , 2014, IEEE Access.

[20]  R. Safavi-Naini,et al.  Security and privacy in the smart grid , 2012 .

[21]  Jorge-Arnulfo Quiané-Ruiz,et al.  Efficient Big Data Processing in Hadoop MapReduce , 2012, Proc. VLDB Endow..

[22]  Eduardo Caicedo Bravo,et al.  The Power of Big Data and Data Analytics for AMI Data: A Case Study , 2020, Sensors.

[23]  Han Liu,et al.  Challenges of Big Data Analysis. , 2013, National science review.

[24]  Haitham Abu-Rub,et al.  A Review on Big Data Management and Decision-Making in Smart Grid , 2019, Power Electronics and Drives.

[25]  Sneha Kumari,et al.  Role of Big Data in Decision Making , 2018 .

[26]  Philip S. Yu,et al.  Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.

[27]  Z. Irani,et al.  Critical analysis of Big Data challenges and analytical methods , 2017 .