IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i20p5504-d431995.html
   My bibliography  Save this article

Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin

Author

Listed:
  • Hyang-A Park

    (Digital Energy System Research Center, Korea Electrotechnology Research Institute, Changwon 51543, Korea
    The School of Electrical Engineering, Pusan National University, Pusan 46241, Korea)

  • Gilsung Byeon

    (Digital Energy System Research Center, Korea Electrotechnology Research Institute, Changwon 51543, Korea)

  • Wanbin Son

    (Digital Energy System Research Center, Korea Electrotechnology Research Institute, Changwon 51543, Korea)

  • Hyung-Chul Jo

    (Digital Energy System Research Center, Korea Electrotechnology Research Institute, Changwon 51543, Korea)

  • Jongyul Kim

    (Digital Energy System Research Center, Korea Electrotechnology Research Institute, Changwon 51543, Korea)

  • Sungshin Kim

    (The School of Electrical Engineering, Pusan National University, Pusan 46241, Korea)

Abstract

Due to the recent development of information and communication technology (ICT), various studies using real-time data are now being conducted. The microgrid research field is also evolving to enable intelligent operation of energy management through digitalization. Problems occur when operating the actual microgrid, causing issues such as difficulty in decision making and system abnormalities. Using digital twin technology, which is one of the technologies representing the fourth industrial revolution, it is possible to overcome these problems by changing the microgrid configuration and operating algorithms of virtual space in various ways and testing them in real time. In this study, we proposed an energy storage system (ESS) operation scheduling model to be applied to virtual space when constructing a microgrid using digital twin technology. An ESS optimal charging/discharging scheduling was established to minimize electricity bills and was implemented using supervised learning techniques such as the decision tree, NARX, and MARS models instead of existing optimization techniques. NARX and decision trees are machine learning techniques. MARS is a nonparametric regression model, and its application has been increasing. Its performance was analyzed by deriving performance evaluation indicators for each model. Using the proposed model, it was found in a case study that the amount of electricity bill savings when operating the ESS is greater than that incurred in the actual ESS operation. The suitability of the model was evaluated by a comparative analysis with the optimization-based ESS charging/discharging scheduling pattern.

Suggested Citation

  • Hyang-A Park & Gilsung Byeon & Wanbin Son & Hyung-Chul Jo & Jongyul Kim & Sungshin Kim, 2020. "Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin," Energies, MDPI, vol. 13(20), pages 1-15, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:20:p:5504-:d:431995
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/20/5504/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/20/5504/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kim, S.K. & Cho, K.H. & Kim, J.Y. & Byeon, G., 2019. "Field study on operational performance and economics of lithium-polymer and lead-acid battery systems for consumer load management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    2. Óscar Gonzales-Zurita & Jean-Michel Clairand & Elisa Peñalvo-López & Guillermo Escrivá-Escrivá, 2020. "Review on Multi-Objective Control Strategies for Distributed Generation on Inverter-Based Microgrids," Energies, MDPI, vol. 13(13), pages 1-29, July.
    3. Ahmed Saad & Samy Faddel & Osama Mohammed, 2020. "IoT-Based Digital Twin for Energy Cyber-Physical Systems: Design and Implementation," Energies, MDPI, vol. 13(18), pages 1-21, September.
    4. Morais, Hugo & Kádár, Péter & Faria, Pedro & Vale, Zita A. & Khodr, H.M., 2010. "Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming," Renewable Energy, Elsevier, vol. 35(1), pages 151-156.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Eduardo Gómez-Luna & John E. Candelo-Becerra & Juan C. Vasquez, 2023. "A New Digital Twins-Based Overcurrent Protection Scheme for Distributed Energy Resources Integrated Distribution Networks," Energies, MDPI, vol. 16(14), pages 1-23, July.
    2. Semeraro, Concetta & Aljaghoub, Haya & Abdelkareem, Mohammad Ali & Alami, Abdul Hai & Olabi, A.G., 2023. "Digital twin in battery energy storage systems: Trends and gaps detection through association rule mining," Energy, Elsevier, vol. 273(C).
    3. Rachid Darbali-Zamora & Jay Johnson & Adam Summers & C. Birk Jones & Clifford Hansen & Chad Showalter, 2021. "State Estimation-Based Distributed Energy Resource Optimization for Distribution Voltage Regulation in Telemetry-Sparse Environments Using a Real-Time Digital Twin," Energies, MDPI, vol. 14(3), pages 1-21, February.
    4. do Amaral, J.V.S. & dos Santos, C.H. & Montevechi, J.A.B. & de Queiroz, A.R., 2023. "Energy Digital Twin applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    5. Danny Espín-Sarzosa & Rodrigo Palma-Behnke & Felipe Valencia-Arroyave, 2023. "Towards Digital Twins of Small Productive Processes in Microgrids," Energies, MDPI, vol. 16(11), pages 1-17, May.
    6. Namita Kumari & Ankush Sharma & Binh Tran & Naveen Chilamkurti & Damminda Alahakoon, 2023. "A Comprehensive Review of Digital Twin Technology for Grid-Connected Microgrid Systems: State of the Art, Potential and Challenges Faced," Energies, MDPI, vol. 16(14), pages 1-19, July.
    7. Juan R. Lopez & Jose de Jesus Camacho & Pedro Ponce & Brian MacCleery & Arturo Molina, 2022. "A Real-Time Digital Twin and Neural Net Cluster-Based Framework for Faults Identification in Power Converters of Microgrids, Self Organized Map Neural Network," Energies, MDPI, vol. 15(19), pages 1-25, October.
    8. Zeli Ye & Wentao Huang & Jinfeng Huang & Jun He & Chengxi Li & Yan Feng, 2023. "Optimal Scheduling of Integrated Community Energy Systems Based on Twin Data Considering Equipment Efficiency Correction Models," Energies, MDPI, vol. 16(3), pages 1-22, January.
    9. Bianca Goia & Tudor Cioara & Ionut Anghel, 2022. "Virtual Power Plant Optimization in Smart Grids: A Narrative Review," Future Internet, MDPI, vol. 14(5), pages 1-22, April.
    10. Sri Nikhil Gupta Gourisetti & Sraddhanjoli Bhadra & David Jonathan Sebastian-Cardenas & Md Touhiduzzaman & Osman Ahmed, 2023. "A Theoretical Open Architecture Framework and Technology Stack for Digital Twins in Energy Sector Applications," Energies, MDPI, vol. 16(13), pages 1-58, June.
    11. Zhen Huang & Xuechun Xiao & Yuan Gao & Yonghong Xia & Tomislav Dragičević & Pat Wheeler, 2023. "Emerging Information Technologies for the Energy Management of Onboard Microgrids in Transportation Applications," Energies, MDPI, vol. 16(17), pages 1-26, August.
    12. Wang, Hui & Zheng, Junkang & Xiang, Jiawei, 2023. "Online bearing fault diagnosis using numerical simulation models and machine learning classifications," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    13. Sofia Agostinelli & Fabrizio Cumo & Giambattista Guidi & Claudio Tomazzoli, 2021. "Cyber-Physical Systems Improving Building Energy Management: Digital Twin and Artificial Intelligence," Energies, MDPI, vol. 14(8), pages 1-25, April.

    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. Abbaspour, M. & Satkin, M. & Mohammadi-Ivatloo, B. & Hoseinzadeh Lotfi, F. & Noorollahi, Y., 2013. "Optimal operation scheduling of wind power integrated with compressed air energy storage (CAES)," Renewable Energy, Elsevier, vol. 51(C), pages 53-59.
    2. Chen, Yen-Haw & Lu, Su-Ying & Chang, Yung-Ruei & Lee, Ta-Tung & Hu, Ming-Che, 2013. "Economic analysis and optimal energy management models for microgrid systems: A case study in Taiwan," Applied Energy, Elsevier, vol. 103(C), pages 145-154.
    3. Keon Baek & Woong Ko & Jinho Kim, 2019. "Optimal Scheduling of Distributed Energy Resources in Residential Building under the Demand Response Commitment Contract," Energies, MDPI, vol. 12(14), pages 1-19, July.
    4. Saher Javaid & Mineo Kaneko & Yasuo Tan, 2021. "Safe Operation Conditions of Electrical Power System Considering Power Balanceability among Power Generators, Loads, and Storage Devices," Energies, MDPI, vol. 14(15), pages 1-27, July.
    5. Llaria, Alvaro & Curea, Octavian & Jiménez, Jaime & Camblong, Haritza, 2011. "Survey on microgrids: Unplanned islanding and related inverter control techniques," Renewable Energy, Elsevier, vol. 36(8), pages 2052-2061.
    6. Soha, Tamás & Munkácsy, Béla & Harmat, Ádám & Csontos, Csaba & Horváth, Gergely & Tamás, László & Csüllög, Gábor & Daróczi, Henriett & Sáfián, Fanni & Szabó, Mária, 2017. "GIS-based assessment of the opportunities for small-scale pumped hydro energy storage in middle-mountain areas focusing on artificial landscape features," Energy, Elsevier, vol. 141(C), pages 1363-1373.
    7. Yong Zeng & Yanpeng Cai & Guohe Huang & Jing Dai, 2011. "A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty," Energies, MDPI, vol. 4(10), pages 1-33, October.
    8. Mazzola, Simone & Astolfi, Marco & Macchi, Ennio, 2015. "A detailed model for the optimal management of a multigood microgrid," Applied Energy, Elsevier, vol. 154(C), pages 862-873.
    9. Hirotaka Takano & Ryosuke Hayashi & Hiroshi Asano & Tadahiro Goda, 2021. "Optimal Sizing of Battery Energy Storage Systems Considering Cooperative Operation with Microgrid Components," Energies, MDPI, vol. 14(21), pages 1-13, November.
    10. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    11. Naraharisetti, Pavan Kumar & Karimi, I.A. & Anand, Abhay & Lee, Dong-Yup, 2011. "A linear diversity constraint – Application to scheduling in microgrids," Energy, Elsevier, vol. 36(7), pages 4235-4243.
    12. Zhao, Bo & Zhang, Xuesong & Li, Peng & Wang, Ke & Xue, Meidong & Wang, Caisheng, 2014. "Optimal sizing, operating strategy and operational experience of a stand-alone microgrid on Dongfushan Island," Applied Energy, Elsevier, vol. 113(C), pages 1656-1666.
    13. Lambert, Mathieu & Hassani, Rachid, 2023. "Diesel genset optimization in remote microgrids," Applied Energy, Elsevier, vol. 340(C).
    14. Bahramara, Salah & Sheikhahmadi, Pouria & Golpîra, Hêmin, 2019. "Co-optimization of energy and reserve in standalone micro-grid considering uncertainties," Energy, Elsevier, vol. 176(C), pages 792-804.
    15. Zhang, Di & Samsatli, Nouri J. & Hawkes, Adam D. & Brett, Dan J.L. & Shah, Nilay & Papageorgiou, Lazaros G., 2013. "Fair electricity transfer price and unit capacity selection for microgrids," Energy Economics, Elsevier, vol. 36(C), pages 581-593.
    16. Lombardi, P. & Sokolnikova, T. & Suslov, K. & Voropai, N. & Styczynski, Z.A., 2016. "Isolated power system in Russia: A chance for renewable energies?," Renewable Energy, Elsevier, vol. 90(C), pages 532-541.
    17. Archana Saxena & Rajesh Singh & Anita Gehlot & Shaik Vaseem Akram & Bhekisipho Twala & Aman Singh & Elisabeth Caro Montero & Neeraj Priyadarshi, 2022. "Technologies Empowered Environmental, Social, and Governance (ESG): An Industry 4.0 Landscape," Sustainability, MDPI, vol. 15(1), pages 1-17, December.
    18. Khodr, H.M. & El Halabi, N. & García-Gracia, M., 2012. "Intelligent renewable microgrid scheduling controlled by a virtual power producer: A laboratory experience," Renewable Energy, Elsevier, vol. 48(C), pages 269-275.
    19. Bourbon, R. & Ngueveu, S.U. & Roboam, X. & Sareni, B. & Turpin, C. & Hernandez-Torres, D., 2019. "Energy management optimization of a smart wind power plant comparing heuristic and linear programming methods," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 158(C), pages 418-431.
    20. Faria, P. & Vale, Z., 2011. "Demand response in electrical energy supply: An optimal real time pricing approach," Energy, Elsevier, vol. 36(8), pages 5374-5384.

    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:jeners:v:13:y:2020:i:20:p:5504-:d:431995. 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.