IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i10p3512-d172963.html

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/10/3512/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/10/10/3512/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tan, Kang Miao & Ramachandaramurthy, Vigna K. & Yong, Jia Ying, 2016. "Integration of electric vehicles in smart grid: A review on vehicle to grid technologies and optimization techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 720-732.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Eid, Cherrelle & Codani, Paul & Perez, Yannick & Reneses, Javier & Hakvoort, Rudi, 2016. "Managing electric flexibility from Distributed Energy Resources: A review of incentives for market design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 237-247.
    2. Alqahtani, Mohammed & Hu, Mengqi, 2022. "Dynamic energy scheduling and routing of multiple electric vehicles using deep reinforcement learning," Energy, Elsevier, vol. 244(PA).
    3. Natascia Andrenacci & Roberto Ragona & Antonino Genovese, 2020. "Evaluation of the Instantaneous Power Demand of an Electric Charging Station in an Urban Scenario," Energies, MDPI, vol. 13(11), pages 1-19, May.
    4. Lucio Ciabattoni & Stefano Cardarelli & Marialaura Di Somma & Giorgio Graditi & Gabriele Comodi, 2021. "A Novel Open-Source Simulator Of Electric Vehicles in a Demand-Side Management Scenario," Energies, MDPI, vol. 14(6), pages 1-16, March.
    5. Lefeng, Shi & Shengnan, Lv & Chunxiu, Liu & Yue, Zhou & Cipcigan, Liana & Acker, Thomas L., 2020. "A framework for electric vehicle power supply chain development," Utilities Policy, Elsevier, vol. 64(C).
    6. Tan, Kang Miao & Yong, Jia Ying & Ramachandaramurthy, Vigna K. & Mansor, Muhamad & Teh, Jiashen & Guerrero, Josep M., 2023. "Factors influencing global transportation electrification: Comparative analysis of electric and internal combustion engine vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    7. Ghosh, Sanchita & Roy, Tanushree, 2025. "Detection and isolation of battery charging cyberattacks via Koopman operator," Applied Energy, Elsevier, vol. 401(PB).
    8. Chengxiang Zhuge & Chunfu Shao & Xia Li, 2019. "Empirical Analysis of Parking Behaviour of Conventional and Electric Vehicles for Parking Modelling: A Case Study of Beijing, China," Energies, MDPI, vol. 12(16), pages 1-21, August.
    9. Arroyo, Javier & Manna, Carlo & Spiessens, Fred & Helsen, Lieve, 2022. "Reinforced model predictive control (RL-MPC) for building energy management," Applied Energy, Elsevier, vol. 309(C).
    10. Dreidy, Mohammad & Mokhlis, H. & Mekhilef, Saad, 2017. "Inertia response and frequency control techniques for renewable energy sources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 144-155.
    11. Weitzel, Timm & Glock, Christoph H., 2018. "Energy management for stationary electric energy storage systems: A systematic literature review," European Journal of Operational Research, Elsevier, vol. 264(2), pages 582-606.
    12. Kaiss, Mateus & Wan, Yihao & Gebbran, Daniel & Vila, Clodomiro Unsihuay & Dragičević, Tomislav, 2025. "Review on Virtual Power Plants/Virtual Aggregators: Concepts, applications, prospects and operation strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 211(C).
    13. Das, H.S. & Rahman, M.M. & Li, S. & Tan, C.W., 2020. "Electric vehicles standards, charging infrastructure, and impact on grid integration: A technological review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
    14. Georg Göhler & Anna-Lena Klingler & Florian Klausmann & Dieter Spath, 2021. "Integrated Modelling of Decentralised Energy Supply in Combination with Electric Vehicle Charging in a Real-Life Case Study," Energies, MDPI, vol. 14(21), pages 1-19, October.
    15. Woo, Soomin & Strobel, Leo & Yuan, Yuhao & Pruckner, Marco & Lipman, Timothy E., 2025. "Exploring bidirectional charging strategies for an electric vehicle population," Applied Energy, Elsevier, vol. 397(C).
    16. Ashique, Ratil H. & Salam, Zainal & Bin Abdul Aziz, Mohd Junaidi & Bhatti, Abdul Rauf, 2017. "Integrated photovoltaic-grid dc fast charging system for electric vehicle: A review of the architecture and control," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1243-1257.
    17. Syed Ali Abbas Kazmi & Muhammad Khuram Shahzad & Akif Zia Khan & Dong Ryeol Shin, 2017. "Smart Distribution Networks: A Review of Modern Distribution Concepts from a Planning Perspective," Energies, MDPI, vol. 10(4), pages 1-47, April.
    18. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    19. Park, Keonwoo & Moon, Ilkyeong, 2022. "Multi-agent deep reinforcement learning approach for EV charging scheduling in a smart grid," Applied Energy, Elsevier, vol. 328(C).
    20. Bio Gassi, Karim & Baysal, Mustafa, 2023. "Improving real-time energy decision-making model with an actor-critic agent in modern microgrids with energy storage devices," Energy, Elsevier, vol. 263(PE).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:10:y:2018:i:10:p:3512-:d:172963. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.