IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i4p942-d141259.html
   My bibliography  Save this article

Optimal Scheduling of Residential Microgrids Considering Virtual Energy Storage System

Author

Listed:
  • Weiliang Liu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China)

  • Changliang Liu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China)

  • Yongjun Lin

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China)

  • Liangyu Ma

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China)

  • Kang Bai

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China)

  • Yanqun Wu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China)

Abstract

The increasingly complex residential microgrids (r-microgrid) consisting of renewable generation, energy storage systems, and residential buildings require a more intelligent scheduling method. Firstly, aiming at the radiant floor heating/cooling system widely utilized in residential buildings, the mathematical relationship between the operative temperature and heating/cooling demand is established based on the equivalent thermodynamic parameters (ETP) model, by which the thermal storage capacity is analyzed. Secondly, the radiant floor heating/cooling system is treated as virtual energy storage system (VESS), and an optimization model based on mixed-integer nonlinear programming (MINLP) for r-microgrid scheduling is established which takes thermal comfort level and economy as the optimization objectives. Finally, the optimal scheduling results of two typical r-microgrids are analyzed. Case studies demonstrate that the proposed scheduling method can effectively employ the thermal storage capacity of radiant floor heating/cooling system, thus lowering the operating cost of the r-microgrid effectively while ensuring the thermal comfort level of users.

Suggested Citation

  • Weiliang Liu & Changliang Liu & Yongjun Lin & Liangyu Ma & Kang Bai & Yanqun Wu, 2018. "Optimal Scheduling of Residential Microgrids Considering Virtual Energy Storage System," Energies, MDPI, vol. 11(4), pages 1-23, April.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:942-:d:141259
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/4/942/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/4/942/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sergio Saponara, 2016. "Distributed Measuring System for Predictive Diagnosis of Uninterruptible Power Supplies in Safety-Critical Applications," Energies, MDPI, vol. 9(5), pages 1-18, April.
    2. Lu, Yuehong & Wang, Shengwei & Sun, Yongjun & Yan, Chengchu, 2015. "Optimal scheduling of buildings with energy generation and thermal energy storage under dynamic electricity pricing using mixed-integer nonlinear programming," Applied Energy, Elsevier, vol. 147(C), pages 49-58.
    3. Xu, Xiandong & Jia, Hongjie & Wang, Dan & Yu, David C. & Chiang, Hsiao-Dong, 2015. "Hierarchical energy management system for multi-source multi-product microgrids," Renewable Energy, Elsevier, vol. 78(C), pages 621-630.
    4. Keirstead, James & Jennings, Mark & Sivakumar, Aruna, 2012. "A review of urban energy system models: Approaches, challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3847-3866.
    5. Bolívar Jaramillo, Lucas & Weidlich, Anke, 2016. "Optimal microgrid scheduling with peak load reduction involving an electrolyzer and flexible loads," Applied Energy, Elsevier, vol. 169(C), pages 857-865.
    6. Rabiee, Abdorreza & Sadeghi, Mohammad & Aghaeic, Jamshid & Heidari, Alireza, 2016. "Optimal operation of microgrids through simultaneous scheduling of electrical vehicles and responsive loads considering wind and PV units uncertainties," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 721-739.
    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. Marco Badami & Gabriele Fambri & Salvatore Mancò & Mariapia Martino & Ioannis G. Damousis & Dimitrios Agtzidis & Dimitrios Tzovaras, 2019. "A Decision Support System Tool to Manage the Flexibility in Renewable Energy-Based Power Systems," Energies, MDPI, vol. 13(1), pages 1-16, December.

    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. Jin, Xiaolong & Mu, Yunfei & Jia, Hongjie & Wu, Jianzhong & Jiang, Tao & Yu, Xiaodan, 2017. "Dynamic economic dispatch of a hybrid energy microgrid considering building based virtual energy storage system," Applied Energy, Elsevier, vol. 194(C), pages 386-398.
    2. Fontenot, Hannah & Dong, Bing, 2019. "Modeling and control of building-integrated microgrids for optimal energy management – A review," Applied Energy, Elsevier, vol. 254(C).
    3. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
    4. Jin, Xiaolong & Mu, Yunfei & Jia, Hongjie & Wu, Jianzhong & Xu, Xiandong & Yu, Xiaodan, 2016. "Optimal day-ahead scheduling of integrated urban energy systems," Applied Energy, Elsevier, vol. 180(C), pages 1-13.
    5. Thomas, Dimitrios & Deblecker, Olivier & Ioakimidis, Christos S., 2018. "Optimal operation of an energy management system for a grid-connected smart building considering photovoltaics’ uncertainty and stochastic electric vehicles’ driving schedule," Applied Energy, Elsevier, vol. 210(C), pages 1188-1206.
    6. Jin, Xiaolong & Wu, Jianzhong & Mu, Yunfei & Wang, Mingshen & Xu, Xiandong & Jia, Hongjie, 2017. "Hierarchical microgrid energy management in an office building," Applied Energy, Elsevier, vol. 208(C), pages 480-494.
    7. Zhang, Jingrui & Wu, Yihong & Guo, Yiran & Wang, Bo & Wang, Hengyue & Liu, Houde, 2016. "A hybrid harmony search algorithm with differential evolution for day-ahead scheduling problem of a microgrid with consideration of power flow constraints," Applied Energy, Elsevier, vol. 183(C), pages 791-804.
    8. Tang, Rui & Li, Hangxin & Wang, Shengwei, 2019. "A game theory-based decentralized control strategy for power demand management of building cluster using thermal mass and energy storage," Applied Energy, Elsevier, vol. 242(C), pages 809-820.
    9. Avilés A., Camilo & Oliva H., Sebastian & Watts, David, 2019. "Single-dwelling and community renewable microgrids: Optimal sizing and energy management for new business models," Applied Energy, Elsevier, vol. 254(C).
    10. David Drysdale & Brian Vad Mathiesen & Henrik Lund, 2019. "From Carbon Calculators to Energy System Analysis in Cities," Energies, MDPI, vol. 12(12), pages 1-21, June.
    11. Eissa, M.M., 2018. "First time real time incentive demand response program in smart grid with “i-Energy” management system with different resources," Applied Energy, Elsevier, vol. 212(C), pages 607-621.
    12. Solène Goy & François Maréchal & Donal Finn, 2020. "Data for Urban Scale Building Energy Modelling: Assessing Impacts and Overcoming Availability Challenges," Energies, MDPI, vol. 13(16), pages 1-23, August.
    13. Xiaoyu Xu & Chun Chang & Xinxin Guo & Mingzhi Zhao, 2023. "Experimental and Numerical Study of the Ice Storage Process and Material Properties of Ice Storage Coils," Energies, MDPI, vol. 16(14), pages 1-18, July.
    14. McKenna, R. & Bertsch, V. & Mainzer, K. & Fichtner, W., 2018. "Combining local preferences with multi-criteria decision analysis and linear optimization to develop feasible energy concepts in small communities," European Journal of Operational Research, Elsevier, vol. 268(3), pages 1092-1110.
    15. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    16. Mikovits, Christian & Wetterlund, Elisabeth & Wehrle, Sebastian & Baumgartner, Johann & Schmidt, Johannes, 2021. "Stronger together: Multi-annual variability of hydrogen production supported by wind power in Sweden," Applied Energy, Elsevier, vol. 282(PB).
    17. Martínez-Lao, Juan & Montoya, Francisco G. & Montoya, Maria G. & Manzano-Agugliaro, Francisco, 2017. "Electric vehicles in Spain: An overview of charging systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 970-983.
    18. Behzadi, Amirmohammad & Holmberg, Sture & Duwig, Christophe & Haghighat, Fariborz & Ooka, Ryozo & Sadrizadeh, Sasan, 2022. "Smart design and control of thermal energy storage in low-temperature heating and high-temperature cooling systems: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    19. Luca Brunelli & Emiliano Borri & Anna Laura Pisello & Andrea Nicolini & Carles Mateu & Luisa F. Cabeza, 2024. "Thermal Energy Storage in Energy Communities: A Perspective Overview through a Bibliometric Analysis," Sustainability, MDPI, vol. 16(14), pages 1-27, July.
    20. Huang, Zishuo & Yu, Hang & Chu, Xiangyang & Peng, Zhenwei, 2017. "A goal programming based model system for community energy plan," Energy, Elsevier, vol. 134(C), pages 893-901.

    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:11:y:2018:i:4:p:942-:d:141259. 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.