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Big Data issues and opportunities for electric utilities

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  • Schuelke-Leech, Beth-Anne
  • Barry, Betsy
  • Muratori, Matteo
  • Yurkovich, B.J.

Abstract

Advances and innovations are crucial for a sustainable electricity system that includes smart grid technologies, renewable energy sources, and greater energy efficiency. These technologies are often layered on top of the existing infrastructure and legacy information systems. The management and utilization of the data generated from the different components of the electrical system are critical for the successful deployment and operation of this system. This paper reviews the issues and opportunities of the use of Big Data for electric utilities. Big Data provides the opportunity to better monitor, correct, and integrate smart grid technologies and renewable energy. At the same time, data management and utilization must be integrated into organizational operations if the potentials are to be realized. Electric utilities are conservative, heavily-regulated, and concerned with both system reliability and overall profitability. Thus, technological, economic, institutional, and policy constraints must all be addressed. After reviewing these issues and opportunities, we empirically analyze whether these are part of the discussions about electric utilities with federal policymakers. The results show that while conversations about electric utilities overall are plentiful, conversations about data in the context of electric utilities are relatively rare.

Suggested Citation

  • Schuelke-Leech, Beth-Anne & Barry, Betsy & Muratori, Matteo & Yurkovich, B.J., 2015. "Big Data issues and opportunities for electric utilities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 937-947.
    • Handle: RePEc:eee:rensus:v:52:y:2015:i:c:p:937-947
    DOI: 10.1016/j.rser.2015.07.128
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    3. Isaac Machorro-Cano & Giner Alor-Hernández & Mario Andrés Paredes-Valverde & Lisbeth Rodríguez-Mazahua & José Luis Sánchez-Cervantes & José Oscar Olmedo-Aguirre, 2020. "HEMS-IoT: A Big Data and Machine Learning-Based Smart Home System for Energy Saving," Energies, MDPI, vol. 13(5), pages 1-24, March.
    4. Dong, Qingli & Sun, Yuhuan & Li, Peizhi, 2017. "A novel forecasting model based on a hybrid processing strategy and an optimized local linear fuzzy neural network to make wind power forecasting: A case study of wind farms in China," Renewable Energy, Elsevier, vol. 102(PA), pages 241-257.
    5. Schuelke-Leech, Beth-Anne, 2018. "A model for understanding the orders of magnitude of disruptive technologies," Technological Forecasting and Social Change, Elsevier, vol. 129(C), pages 261-274.
    6. Gustavo Cattelan Nobre & Elaine Tavares, 2017. "Scientific literature analysis on big data and internet of things applications on circular economy: a bibliometric study," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 463-492, April.
    7. Purna Prakash Kasaraneni & Venkata Pavan Kumar Yellapragada & Ganesh Lakshmana Kumar Moganti & Aymen Flah, 2022. "Analytical Enumeration of Redundant Data Anomalies in Energy Consumption Readings of Smart Buildings with a Case Study of Darmstadt Smart City in Germany," Sustainability, MDPI, vol. 14(17), pages 1-24, August.
    8. Pablo David Necoechea-Porras & Asunción López & Juan Carlos Salazar-Elena, 2021. "Deregulation in the Energy Sector and Its Economic Effects on the Power Sector: A Literature Review," Sustainability, MDPI, vol. 13(6), pages 1-23, March.
    9. Matti Grosse & Hendrik Send & Thomas Schildhauer, 2019. "Lessons Learned from Establishing the Energy-Informatics Business Model: Case of a German Energy Company," Sustainability, MDPI, vol. 11(3), pages 1-18, February.
    10. Colak, Ilhami & Sagiroglu, Seref & Fulli, Gianluca & Yesilbudak, Mehmet & Covrig, Catalin-Felix, 2016. "A survey on the critical issues in smart grid technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 396-405.
    11. Adriana Mar & Pedro Pereira & João F. Martins, 2019. "A Survey on Power Grid Faults and Their Origins: A Contribution to Improving Power Grid Resilience," Energies, MDPI, vol. 12(24), pages 1-21, December.
    12. Li, Francis G.N. & Bataille, Chris & Pye, Steve & O'Sullivan, Aidan, 2019. "Prospects for energy economy modelling with big data: Hype, eliminating blind spots, or revolutionising the state of the art?," Applied Energy, Elsevier, vol. 239(C), pages 991-1002.
    13. Olga Pilipczuk, 2020. "Sustainable Smart Cities and Energy Management: The Labor Market Perspective," Energies, MDPI, vol. 13(22), pages 1-24, November.

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