IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2022i1p73-d1010352.html
   My bibliography  Save this article

Optimizing Energy Management in Microgrids Based on Different Load Types in Smart Buildings

Author

Listed:
  • Mohammad Zareein

    (Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran 1631714191, Iran)

  • Jalal Sahebkar Farkhani

    (Department of Energy, Aalborg University, 9220 Aalborg, Denmark)

  • Amirhossein Nikoofard

    (Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran 1631714191, Iran)

  • Turaj Amraee

    (Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran 1631714191, Iran)

Abstract

This paper presents an energy management strategy (EMS) based on the Stackelberg game theory for the microgrid community. Three agents or layers are considered in the proposed framework. The microgrid cluster (MGC) refers to the agent that coordinates the interactions between the microgrids and the utility grid. The microgrid agent manages the energy scheduling of its own consumers. The third agent represents the consumers inside the microgrids. The game equilibrium point is solved between different layers and each layer will benefit the most. First, an algorithm performs demand response in each microgrid according to load models in smart buildings and determines the load consumption for each consumer. Then, each microgrid determines its selling price to the consumers and the amount of energy required to purchase from the utility grid to achieve the maximum profit. Finally, the balance point will be obtained between microgrids by the microgrid cluster agent. Moreover, the proposed method uses various load types at different times based on real-life models. The result shows that considering these different load models with demand response increased the profit of the user agent by an average of 22%. The demand response is implemented by the time of use (TOU) model and real-time pricing (RTP) in the microgrid.

Suggested Citation

  • Mohammad Zareein & Jalal Sahebkar Farkhani & Amirhossein Nikoofard & Turaj Amraee, 2022. "Optimizing Energy Management in Microgrids Based on Different Load Types in Smart Buildings," Energies, MDPI, vol. 16(1), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:73-:d:1010352
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/1/73/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/1/73/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kaijun Lin & Junyong Wu & Di Liu & Dezhi Li & Taorong Gong, 2018. "Energy Management of Combined Cooling, Heating and Power Micro Energy Grid Based on Leader-Follower Game Theory," Energies, MDPI, vol. 11(3), pages 1-21, March.
    2. Liu, Yixin & Guo, Li & Wang, Chengshan, 2018. "A robust operation-based scheduling optimization for smart distribution networks with multi-microgrids," Applied Energy, Elsevier, vol. 228(C), pages 130-140.
    3. Hafiz Abdul Muqeet & Hafiz Mudassir Munir & Haseeb Javed & Muhammad Shahzad & Mohsin Jamil & Josep M. Guerrero, 2021. "An Energy Management System of Campus Microgrids: State-of-the-Art and Future Challenges," Energies, MDPI, vol. 14(20), pages 1-34, October.
    4. Tostado-Véliz, Marcos & Kamel, Salah & Aymen, Flah & Rezaee Jordehi, Ahmad & Jurado, Francisco, 2022. "A Stochastic-IGDT model for energy management in isolated microgrids considering failures and demand response," Applied Energy, Elsevier, vol. 317(C).
    5. Seyedeh Narjes Fallah & Ravinesh Chand Deo & Mohammad Shojafar & Mauro Conti & Shahaboddin Shamshirband, 2018. "Computational Intelligence Approaches for Energy Load Forecasting in Smart Energy Management Grids: State of the Art, Future Challenges, and Research Directions," Energies, MDPI, vol. 11(3), pages 1-31, March.
    6. Wunvisa Tipasri & Amnart Suksri & Karthikeyan Velmurugan & Tanakorn Wongwuttanasatian, 2022. "Energy Management for an Air Conditioning System Using a Storage Device to Reduce the On-Peak Power Consumption," Energies, MDPI, vol. 15(23), pages 1-19, November.
    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. Hafiz Abdul Muqeet & Rehan Liaqat & Mohsin Jamil & Asharf Ali Khan, 2023. "A State-of-the-Art Review of Smart Energy Systems and Their Management in a Smart Grid Environment," Energies, MDPI, vol. 16(1), pages 1-23, January.
    2. Tostado-Véliz, Marcos & Hasanien, Hany M. & Rezaee Jordehi, Ahmad & Turky, Rania A. & Gómez-González, Manuel & Jurado, Francisco, 2023. "An Interval-based privacy – Aware optimization framework for electricity price setting in isolated microgrid clusters," Applied Energy, Elsevier, vol. 340(C).
    3. Konstantina Peloriadi & Petros Iliadis & Panagiotis Boutikos & Konstantinos Atsonios & Panagiotis Grammelis & Aristeidis Nikolopoulos, 2022. "Technoeconomic Assessment of LNG-Fueled Solid Oxide Fuel Cells in Small Island Systems: The Patmos Island Case Study," Energies, MDPI, vol. 15(11), pages 1-20, May.
    4. Rodriguez, Mauricio & Arcos-Aviles, Diego & Guinjoan, Francesc, 2024. "Simple fuzzy logic-based energy management for power exchange in isolated multi-microgrid systems: A case study in a remote community in the Amazon region of Ecuador," Applied Energy, Elsevier, vol. 357(C).
    5. Ijaz Ul Haq & Amin Ullah & Samee Ullah Khan & Noman Khan & Mi Young Lee & Seungmin Rho & Sung Wook Baik, 2021. "Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors," Mathematics, MDPI, vol. 9(6), pages 1-17, March.
    6. Alfredo Alcayde & Raul Baños & Francisco M. Arrabal-Campos & Francisco G. Montoya, 2019. "Optimization of the Contracted Electric Power by Means of Genetic Algorithms," Energies, MDPI, vol. 12(7), pages 1-13, April.
    7. Tan, Bifei & Chen, Simin & Liang, Zipeng & Zheng, Xiaodong & Zhu, Yanjin & Chen, Haoyong, 2024. "An iteration-free hierarchical method for the energy management of multiple-microgrid systems with renewable energy sources and electric vehicles," Applied Energy, Elsevier, vol. 356(C).
    8. Heekwon Yang & Byeol Kim & Joosung Lee & Yonghan Ahn & Chankil Lee, 2018. "Advanced Wireless Sensor Networks for Sustainable Buildings Using Building Ducts," Sustainability, MDPI, vol. 10(8), pages 1-13, July.
    9. Jani, Ali & Jadid, Shahram, 2023. "Two-stage energy scheduling framework for multi-microgrid system in market environment," Applied Energy, Elsevier, vol. 336(C).
    10. Tostado-Véliz, Marcos & Rezaee Jordehi, Ahmad & Fernández-Lobato, Lázuli & Jurado, Francisco, 2023. "Robust energy management in isolated microgrids with hydrogen storage and demand response," Applied Energy, Elsevier, vol. 345(C).
    11. Ji, Haoran & Wang, Chengshan & Li, Peng & Song, Guanyu & Yu, Hao & Wu, Jianzhong, 2019. "Quantified analysis method for operational flexibility of active distribution networks with high penetration of distributed generators," Applied Energy, Elsevier, vol. 239(C), pages 706-714.
    12. Paweł Pijarski & Piotr Kacejko & Piotr Miller, 2023. "Advanced Optimisation and Forecasting Methods in Power Engineering—Introduction to the Special Issue," Energies, MDPI, vol. 16(6), pages 1-20, March.
    13. Xuewei Pan & Fan Yang & Peiwen Ma & Yijin Xing & Jinye Zhang & Lingling Cao, 2022. "A Game-Theoretic Approach of Optimized Operation of AC/DC Hybrid Microgrid Clusters," Energies, MDPI, vol. 15(15), pages 1-22, July.
    14. Huy, Truong Hoang Bao & Duy, Nguyen Thanh Minh & Phu, Pham Van & Le, Tien-Dat & Park, Seongkeun & Kim, Daehee, 2024. "Robust real-time energy management for a hydrogen refueling station using generative adversarial imitation learning," Applied Energy, Elsevier, vol. 373(C).
    15. Nebiyu Kedir & Phuong H. D. Nguyen & Citlaly Pérez & Pedro Ponce & Aminah Robinson Fayek, 2023. "Systematic Literature Review on Fuzzy Hybrid Methods in Photovoltaic Solar Energy: Opportunities, Challenges, and Guidance for Implementation," Energies, MDPI, vol. 16(9), pages 1-38, April.
    16. Emilio Ghiani & Alessandro Serpi & Virginia Pilloni & Giuliana Sias & Marco Simone & Gianluca Marcialis & Giuliano Armano & Paolo Attilio Pegoraro, 2018. "A Multidisciplinary Approach for the Development of Smart Distribution Networks," Energies, MDPI, vol. 11(10), pages 1-29, September.
    17. Piotr Bórawski & Aneta Bełdycka-Bórawska & Lisa Holden & Tomasz Rokicki, 2022. "The Role of Renewable Energy Sources in Electricity Production in Poland and the Background of Energy Policy of the European Union at the Beginning of the COVID-19 Crisis," Energies, MDPI, vol. 15(22), pages 1-17, November.
    18. Gaetano Perone, 2022. "Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(6), pages 917-940, August.
    19. Shen, Haotian & Zhang, Hualiang & Xu, Yujie & Chen, Haisheng & Zhang, Zhilai & Li, Wenkai & Su, Xu & Xu, Yalin & Zhu, Yilin, 2024. "Two stage robust economic dispatching of microgrid considering uncertainty of wind, solar and electricity load along with carbon emission predicted by neural network model," Energy, Elsevier, vol. 300(C).
    20. Ivo Araújo & Leonel J. R. Nunes & António Curado, 2023. "Preliminary Approach for the Development of Sustainable University Campuses: A Case Study Based on the Mitigation of Greenhouse Gas Emissions," Sustainability, MDPI, vol. 15(6), pages 1-18, March.

    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:16:y:2022:i:1:p:73-:d:1010352. 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.