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Economic Efficiency of the Internet of Things Solution in the Energy Industry: A Very High Voltage Frosting Case Study

Author

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  • Milos Maryska

    (Department of Information Technologies, University of Economics, 130 67 Prague, Czech Republic)

  • Petr Doucek

    (Department of System Analysis, University of Economics, 130 67 Prague, Czech Republic)

  • Pavel Sladek

    (Department of Information Technologies, University of Economics, 130 67 Prague, Czech Republic)

  • Lea Nedomova

    (Department of System Analysis, University of Economics, 130 67 Prague, Czech Republic)

Abstract

This article deals with the deployment of an Internet of Things (IoT) technology within the energy industry (energy distribution) in the Czech Republic. The first part of the article is devoted to an assessment of the perspectives for developing IoT applications and implementing them within the economy, and then examines how the principles of multi-criteria decision-making are used to select IoT technologies for deployment in the energy industry. The selection of technology is also followed by the selection of the specific application with the highest potential benefit for the company using such a method to select the technology. The selection solution is demonstrated and further discussed from the technological and financial standpoints and illustrated via the example of choosing among two alternatives for a real-world application, very high voltage (VHV) frosting (in electric power transmission engineering, which is usually considered as any voltage between 52,000 and 300,000 V). The application solution is analyzed by how it relates to the direct vs indirect measurement of glaze ice. The result of this technical and financial analysis was that the direct glaze ice measurement variant is clearly the more advantageous one. The direct-measurement variant has a three-year payoff period, compared to six years for indirect measurement. Further, the benefits from the direct-measurement variant are 2.25 times larger than the other variant, and the five-year net profit value amounts to a profit for the direct-measurement variant while it results in a financial loss for the indirect-measurement variant. The recommended variant is to measure the icing of VHV lines directly.

Suggested Citation

  • Milos Maryska & Petr Doucek & Pavel Sladek & Lea Nedomova, 2019. "Economic Efficiency of the Internet of Things Solution in the Energy Industry: A Very High Voltage Frosting Case Study," Energies, MDPI, vol. 12(4), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:4:p:585-:d:205397
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    References listed on IDEAS

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    1. Rehman Abdul & Anand Paul & Junaid Gul M. & Won-Hwa Hong & Hyuncheol Seo, 2018. "Exploiting Small World Problems in a SIoT Environment," Energies, MDPI, vol. 11(8), pages 1-18, August.
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    3. Vishal Sharma & Ilsun You & Giovanni Pau & Mario Collotta & Jae Deok Lim & Jeong Nyeo Kim, 2018. "LoRaWAN-Based Energy-Efficient Surveillance by Drones for Intelligent Transportation Systems," Energies, MDPI, vol. 11(3), pages 1-26, March.
    4. Andrew Whitmore & Anurag Agarwal & Li Xu, 2015. "The Internet of Things—A survey of topics and trends," Information Systems Frontiers, Springer, vol. 17(2), pages 261-274, April.
    5. Tae Yeong Kim & Dong In Kim, 2018. "Novel Sparse-Coded Ambient Backscatter Communication for Massive IoT Connectivity," Energies, MDPI, vol. 11(7), pages 1-25, July.
    6. Milos Maryska & Petr Doucek & Lea Nedomova & Pavel Sladek, 2018. "The Energy Industry in the Czech Republic: On the Way to the Internet of Things," Economies, MDPI, vol. 6(2), pages 1-13, June.
    7. Bahram Shakerighadi & Amjad Anvari-Moghaddam & Juan C. Vasquez & Josep M. Guerrero, 2018. "Internet of Things for Modern Energy Systems: State-of-the-Art, Challenges, and Open Issues," Energies, MDPI, vol. 11(5), pages 1-23, May.
    8. Muhammad Babar & Jakub Grela & Andrzej Ożadowicz & Phuong H. Nguyen & Zbigniew Hanzelka & I. G. Kamphuis, 2018. "Energy Flexometer: Transactive Energy-Based Internet of Things Technology," Energies, MDPI, vol. 11(3), pages 1-20, March.
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    Cited by:

    1. Mussawir Ul Mehmood & Abasin Ulasyar & Abraiz Khattak & Kashif Imran & Haris Sheh Zad & Shibli Nisar, 2020. "Cloud Based IoT Solution for Fault Detection and Localization in Power Distribution Systems," Energies, MDPI, vol. 13(11), pages 1-19, May.

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