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Big data issues in smart grid – A review

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  • Tu, Chunming
  • He, Xi
  • Shuai, Zhikang
  • Jiang, Fei

Abstract

There are both economic and environmental urges for transition from the current outdated power grid to a sensor-embedded smart grid that monitors system stability, integrates distributed energy and schedules energy consumption for household users. Especially with the proliferation of intelligent measurement devices, exponential growth of data empowers this transition and brings new tools for the development of different applications in power system. Under this context, this paper presents a holistically overview on the state-of-the-art of big data technology in smart grid integration. First, the features of smart grid and the multisource of energy data are discussed. Then, this paper comprehensive summarizes the applications leveraged by big data in smart grid, which also contains some brand new applications with the latest big data technologies. Furthermore, some mainstream platforms and knowledge extraction techniques are looked to promote the big data insights. Finally, challenges and opportunities are pointed out in this paper as well.

Suggested Citation

  • Tu, Chunming & He, Xi & Shuai, Zhikang & Jiang, Fei, 2017. "Big data issues in smart grid – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1099-1107.
    • Handle: RePEc:eee:rensus:v:79:y:2017:i:c:p:1099-1107
    DOI: 10.1016/j.rser.2017.05.134
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    5. George C. Konstantopoulos & Antonio T. Alexandridis & Panos C. Papageorgiou, 2020. "Towards the Integration of Modern Power Systems into a Cyber–Physical Framework," Energies, MDPI, vol. 13(9), pages 1-20, May.
    6. Patnaik, Bhaskar & Mishra, Manohar & Bansal, Ramesh C. & Jena, Ranjan Kumar, 2020. "AC microgrid protection – A review: Current and future prospective," Applied Energy, Elsevier, vol. 271(C).
    7. Scapino, Luca & Zondag, Herbert A. & Diriken, Jan & Rindt, Camilo C.M. & Van Bael, Johan & Sciacovelli, Adriano, 2019. "Modeling the performance of a sorption thermal energy storage reactor using artificial neural networks," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    8. Borna Franović & Sandi Baressi Šegota & Nikola Anđelić & Zlatan Car, 2023. "Decentralized Smart Grid Stability Modeling with Machine Learning," Energies, MDPI, vol. 16(22), pages 1-18, November.
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