IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v202y2020ics0360544220308975.html
   My bibliography  Save this article

Stochastic energy management in multi-carrier residential energy systems

Author

Listed:
  • Kazemdehdashti, A.
  • Mohammadi, M.
  • Seifi, A.R.
  • Rastegar, M.

Abstract

This paper proposes a stochastic framework for residential energy management (REM) in a multi-carrier building. The proposed REM program minimizes the purchase cost of electricity and natural gas of a building, considering the operational constraints of energy dispatch and various building components such as micro-combined heat and power (CHP), heat storage system, heater, gas boiler, plug-in electric vehicles (PEVs), and renewable energy resources (RERs). A part of the electrical and thermal load of the building is assumed to be flexible in time and the amount of energy consumption. Uncertainties of renewable generation, the traveling time of PEVs, energy prices, and electricity/thermal loads are addressed in the program. Since the conventional stochastic methods are time-consuming or rely on asymptotic approximation, the proposed stochastic method in this paper has a low computational burden and a high precision level, which make it a suitable solution for large-scale stochastic programming problems. This method is examined on a test building for the stochastic day-ahead REM problem, and the results are compared with the other conventional methods to demonstrate the advantages of the proposed one.

Suggested Citation

  • Kazemdehdashti, A. & Mohammadi, M. & Seifi, A.R. & Rastegar, M., 2020. "Stochastic energy management in multi-carrier residential energy systems," Energy, Elsevier, vol. 202(C).
    • Handle: RePEc:eee:energy:v:202:y:2020:i:c:s0360544220308975
    DOI: 10.1016/j.energy.2020.117790
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220308975
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.117790?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fabrizio, Enrico & Corrado, Vincenzo & Filippi, Marco, 2010. "A model to design and optimize multi-energy systems in buildings at the design concept stage," Renewable Energy, Elsevier, vol. 35(3), pages 644-655.
    2. Hawkes, A.D. & Leach, M.A., 2007. "Cost-effective operating strategy for residential micro-combined heat and power," Energy, Elsevier, vol. 32(5), pages 711-723.
    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. Lyu, Xiangmei & Liu, Tianqi & Liu, Xuan & He, Chuan & Nan, Lu & Zeng, Hong, 2023. "Low-carbon robust economic dispatch of park-level integrated energy system considering price-based demand response and vehicle-to-grid," Energy, Elsevier, vol. 263(PB).
    2. Zhang, Heng & Zhang, Shenxi & Hu, Xiao & Cheng, Haozhong & Gu, Qingfa & Du, Mengke, 2022. "Parametric optimization-based peer-to-peer energy trading among commercial buildings considering multiple energy conversion," Applied Energy, Elsevier, vol. 306(PB).
    3. Li, Peng & Wang, Zixuan & Wang, Jiahao & Yang, Weihong & Guo, Tianyu & Yin, Yunxing, 2021. "Two-stage optimal operation of integrated energy system considering multiple uncertainties and integrated demand response," Energy, Elsevier, vol. 225(C).
    4. Mehrjerdi, Hasan & Mahdavi, Sajad & Hemmati, Reza, 2021. "Resilience maximization through mobile battery storage and diesel DG in integrated electrical and heating networks," Energy, Elsevier, vol. 237(C).

    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. Sayegh, M.A. & Danielewicz, J. & Nannou, T. & Miniewicz, M. & Jadwiszczak, P. & Piekarska, K. & Jouhara, H., 2017. "Trends of European research and development in district heating technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P2), pages 1183-1192.
    2. Abolhosseini, Shahrouz & Heshmati, Almas & Altmann, Jörn, 2014. "A Review of Renewable Energy Supply and Energy Efficiency Technologies," IZA Discussion Papers 8145, Institute of Labor Economics (IZA).
    3. Cheng, Yaohua & Zhang, Ning & Kirschen, Daniel S. & Huang, Wujing & Kang, Chongqing, 2020. "Planning multiple energy systems for low-carbon districts with high penetration of renewable energy: An empirical study in China," Applied Energy, Elsevier, vol. 261(C).
    4. Gao, Datong & Zhao, Bin & Kwan, Trevor Hocksun & Hao, Yong & Pei, Gang, 2022. "The spatial and temporal mismatch phenomenon in solar space heating applications: status and solutions," Applied Energy, Elsevier, vol. 321(C).
    5. Hadžiselimović, Miralem & Srpčič, Gregor & Brinovar, Iztok & Praunseis, Zdravko & Seme, Sebastijan & Štumberger, Bojan, 2019. "A novel concept of linear oscillatory synchronous generator designed for a stirling engine," Energy, Elsevier, vol. 180(C), pages 19-27.
    6. Maroufmashat, Azadeh & Elkamel, Ali & Fowler, Michael & Sattari, Sourena & Roshandel, Ramin & Hajimiragha, Amir & Walker, Sean & Entchev, Evgueniy, 2015. "Modeling and optimization of a network of energy hubs to improve economic and emission considerations," Energy, Elsevier, vol. 93(P2), pages 2546-2558.
    7. Entchev, E. & Yang, L. & Ghorab, M. & Lee, E.J., 2013. "Simulation of hybrid renewable microgeneration systems in load sharing applications," Energy, Elsevier, vol. 50(C), pages 252-261.
    8. Laura Canale & Anna Rita Di Fazio & Mario Russo & Andrea Frattolillo & Marco Dell’Isola, 2021. "An Overview on Functional Integration of Hybrid Renewable Energy Systems in Multi-Energy Buildings," Energies, MDPI, vol. 14(4), pages 1-33, February.
    9. Xiaofeng Liu & Shijun Wang & Jiawen Sun, 2018. "Energy Management for Community Energy Network with CHP Based on Cooperative Game," Energies, MDPI, vol. 11(5), pages 1-18, April.
    10. Pisello, Anna Laura & Goretti, Michele & Cotana, Franco, 2012. "A method for assessing buildings’ energy efficiency by dynamic simulation and experimental activity," Applied Energy, Elsevier, vol. 97(C), pages 419-429.
    11. Jungwon Yoon & Sanghyun Bae, 2020. "Performance Evaluation and Design of Thermo-Responsive SMP Shading Prototypes," Sustainability, MDPI, vol. 12(11), pages 1-35, May.
    12. Poolla, Chaitanya & Ishihara, Abraham K. & Milito, Rodolfo, 2019. "Designing near-optimal policies for energy management in a stochastic environment," Applied Energy, Elsevier, vol. 242(C), pages 1725-1737.
    13. Facci, Andrea L. & Cigolotti, Viviana & Jannelli, Elio & Ubertini, Stefano, 2017. "Technical and economic assessment of a SOFC-based energy system for combined cooling, heating and power," Applied Energy, Elsevier, vol. 192(C), pages 563-574.
    14. Di Somma, M. & Graditi, G. & Heydarian-Forushani, E. & Shafie-khah, M. & Siano, P., 2018. "Stochastic optimal scheduling of distributed energy resources with renewables considering economic and environmental aspects," Renewable Energy, Elsevier, vol. 116(PA), pages 272-287.
    15. Pellegrino, Sandro & Lanzini, Andrea & Leone, Pierluigi, 2015. "Techno-economic and policy requirements for the market-entry of the fuel cell micro-CHP system in the residential sector," Applied Energy, Elsevier, vol. 143(C), pages 370-382.
    16. Brouwer, Anne Sjoerd & Kuramochi, Takeshi & van den Broek, Machteld & Faaij, André, 2013. "Fulfilling the electricity demand of electric vehicles in the long term future: An evaluation of centralized and decentralized power supply systems," Applied Energy, Elsevier, vol. 107(C), pages 33-51.
    17. Garmabdari, R. & Moghimi, M. & Yang, F. & Lu, J., 2020. "Multi-objective optimisation and planning of grid-connected cogeneration systems in presence of grid power fluctuations and energy storage dynamics," Energy, Elsevier, vol. 212(C).
    18. Avinash Vijay & Adam Hawkes, 2017. "The Techno-Economics of Small-Scale Residential Heating in Low Carbon Futures," Energies, MDPI, vol. 10(11), pages 1-23, November.
    19. Mahtab Kaffash & Glenn Ceusters & Geert Deconinck, 2021. "Interval Optimization to Schedule a Multi-Energy System with Data-Driven PV Uncertainty Representation," Energies, MDPI, vol. 14(10), pages 1-20, May.
    20. Capone, Martina & Guelpa, Elisa & Verda, Vittorio, 2021. "Multi-objective optimization of district energy systems with demand response," Energy, Elsevier, vol. 227(C).

    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:eee:energy:v:202:y:2020:i:c:s0360544220308975. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.