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Introduction of Smart Grid Station Configuration and Application in Guri Branch Office of KEPCO

Author

Listed:
  • Jaehong Whang

    (KU-KIST GreenSchool, Graduate School of Energy and Environment, Korea University, Seoul 02841, Korea)

  • Woohyun Hwang

    (KEPCO Academy, Seoul 01793, Korea)

  • Yeuntae Yoo

    (School of Electrical Engineering, Korea University, Seoul 02841, Korea)

  • Gilsoo Jang

    (School of Electrical Engineering, Korea University, Seoul 02841, Korea)

Abstract

Climate change and global warming are becoming important problems around the globe. To prevent these environmental problems, many countries try to reduce their emissions of greenhouse gases (GHGs) and manage the consumption of energy. The Korea Electric Power Corporation (KEPCO) introduced smart grid (SG) technologies to its branch office in 2014. This was the first demonstration of a smart grid on a building, called the Smart Grid Station (SGS). However, the smart grid industry is stagnant despite of the efforts of KEPCO. The authors analyzed the achievements to date, and proved the effects of the SGS by comparing its early targets to its performance. To evaluate the performance, we analyzed the data of 2015 with the data of 2014 in three aspects: peak reduction, power consumption reduction, and electricity fee savings. Furthermore, we studied the economic analysis including photovoltaic (PV) and energy storage system (ESS) electricity fee savings, as well as running cost savings by electric vehicles. Through the evaluation, the authors proved that the performance surpassed the early targets and that the system is economical. With the advantages of the SGS, we suggested directions to expand the system.

Suggested Citation

  • Jaehong Whang & Woohyun Hwang & Yeuntae Yoo & Gilsoo Jang, 2018. "Introduction of Smart Grid Station Configuration and Application in Guri Branch Office of KEPCO," Sustainability, MDPI, vol. 10(10), pages 1-18, September.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:10:p:3512-:d:172963
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    References listed on IDEAS

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    2. Sunyong Kim & Hyuk Lim, 2018. "Reinforcement Learning Based Energy Management Algorithm for Smart Energy Buildings," Energies, MDPI, vol. 11(8), pages 1-19, August.
    3. Antimo Barbato & Cristiana Bolchini & Angela Geronazzo & Elisa Quintarelli & Andrei Palamarciuc & Alessandro Pitì & Cristina Rottondi & Giacomo Verticale, 2016. "Energy Optimization and Management of Demand Response Interactions in a Smart Campus," Energies, MDPI, vol. 9(6), pages 1-20, May.
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