IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v253y2019ic93.html
   My bibliography  Save this article

An adaptive optimal monthly peak building demand limiting strategy considering load uncertainty

Author

Listed:
  • Xu, Lei
  • Wang, Shengwei
  • Xiao, Fu

Abstract

Peak demand limiting is an efficient means to reduce the electricity cost during a billing cycle in cases where peak demand charge is applied. Most previous studies focus on the daily peak demand limiting without considering load uncertainty, which is a big challenge in making proper and reliable decisions in applications. This paper presents an adaptive optimal peak building demand limiting strategy in a month considering load uncertainty. The core element and major innovation of the strategy is the optimal threshold resetting scheme, which involves two major functions as follows. The uncertain economic benefits (i.e., gains and losses) of a demand limiting control are quantified on the basis of probabilistic load forecasts. The optimal monthly limiting threshold is identified using the expectation metric based on the quantified economic benefits. The strategy optimizes and updates the monthly limiting threshold by adapting it to the ever-changing weather forecast and actual peak power use. Case studies are conducted and the results show that this strategy can effectively reduce the monthly peak demand cost under load uncertainty in different seasons. In addition, sensitivity analysis on the cost benefits of the developed strategy using different means of demand limiting and under different electricity demand charges is conducted.

Suggested Citation

  • Xu, Lei & Wang, Shengwei & Xiao, Fu, 2019. "An adaptive optimal monthly peak building demand limiting strategy considering load uncertainty," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    • Handle: RePEc:eee:appene:v:253:y:2019:i:c:93
    DOI: 10.1016/j.apenergy.2019.113582
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919312565
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.113582?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. Ürge-Vorsatz, Diana & Cabeza, Luisa F. & Serrano, Susana & Barreneche, Camila & Petrichenko, Ksenia, 2015. "Heating and cooling energy trends and drivers in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 85-98.
    2. Cui, Borui & Wang, Shengwei & Sun, Yongjun, 2014. "Life-cycle cost benefit analysis and optimal design of small scale active storage system for building demand limiting," Energy, Elsevier, vol. 73(C), pages 787-800.
    3. Xu, Lei & Wang, Shengwei & Tang, Rui, 2019. "Probabilistic load forecasting for buildings considering weather forecasting uncertainty and uncertain peak load," Applied Energy, Elsevier, vol. 237(C), pages 180-195.
    4. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "Design of distributed energy systems under uncertainty: A two-stage stochastic programming approach," Applied Energy, Elsevier, vol. 222(C), pages 932-950.
    5. Gao, Dian-ce & Sun, Yongjun & Lu, Yuehong, 2015. "A robust demand response control of commercial buildings for smart grid under load prediction uncertainty," Energy, Elsevier, vol. 93(P1), pages 275-283.
    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. Janne Hirvonen & Juha Jokisalo & Risto Kosonen, 2020. "The Effect of Deep Energy Retrofit on The Hourly Power Demand of Finnish Detached Houses," Energies, MDPI, vol. 13(7), pages 1-26, 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. Gert van Wyk & Vinessa Naidoo & E. Innocents Edoun, 2021. "Guiding Principles for Establishing Energy Consumption Reduction and Increase Production Performance in Manufacturing," International Journal of Energy Economics and Policy, Econjournals, vol. 11(1), pages 502-515.
    2. Huang, Pei & Fan, Cheng & Zhang, Xingxing & Wang, Jiayuan, 2019. "A hierarchical coordinated demand response control for buildings with improved performances at building group," Applied Energy, Elsevier, vol. 242(C), pages 684-694.
    3. Cui, Borui & Gao, Dian-ce & Xiao, Fu & Wang, Shengwei, 2017. "Model-based optimal design of active cool thermal energy storage for maximal life-cycle cost saving from demand management in commercial buildings," Applied Energy, Elsevier, vol. 201(C), pages 382-396.
    4. Shen, Feifei & Zhao, Liang & Wang, Meihong & Du, Wenli & Qian, Feng, 2022. "Data-driven adaptive robust optimization for energy systems in ethylene plant under demand uncertainty," Applied Energy, Elsevier, vol. 307(C).
    5. Florian Knobloch & Hector Pollitt & Unnada Chewpreecha & Vassilis Daioglou & Jean-Francois Mercure, 2017. "Simulating the deep decarbonisation of residential heating for limiting global warming to 1.5C," Papers 1710.11019, arXiv.org, revised May 2018.
    6. Bell, N.O. & Bilbao, J.I. & Kay, M. & Sproul, A.B., 2022. "Future climate scenarios and their impact on heating, ventilation and air-conditioning system design and performance for commercial buildings for 2050," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    7. Meyabadi, A. Fattahi & Deihimi, M.H., 2017. "A review of demand-side management: Reconsidering theoretical framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 367-379.
    8. Zhang, Jiyuan & Tang, Hailong & Chen, Min, 2019. "Linear substitute model-based uncertainty analysis of complicated non-linear energy system performance (case study of an adaptive cycle engine)," Applied Energy, Elsevier, vol. 249(C), pages 87-108.
    9. Jaykumar Joshi & Akhilesh Magal & Vijay S. Limaye & Prima Madan & Anjali Jaiswal & Dileep Mavalankar & Kim Knowlton, 2022. "Climate change and 2030 cooling demand in Ahmedabad, India: opportunities for expansion of renewable energy and cool roofs," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 27(7), pages 1-17, October.
    10. Da Li & Shijie Zhang & Yunhan Xiao, 2020. "Interval Optimization-Based Optimal Design of Distributed Energy Resource Systems under Uncertainties," Energies, MDPI, vol. 13(13), pages 1-18, July.
    11. 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.
    12. Roth, Jonathan & Martin, Amory & Miller, Clayton & Jain, Rishee K., 2020. "SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods," Applied Energy, Elsevier, vol. 280(C).
    13. Cabeza, Luisa F. & Ürge-Vorsatz, Diana & Palacios, Anabel & Ürge, Daniel & Serrano, Susana & Barreneche, Camila, 2018. "Trends in penetration and ownership of household appliances," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 4044-4059.
    14. Yu, Sha & Tan, Qing & Evans, Meredydd & Kyle, Page & Vu, Linh & Patel, Pralit L., 2017. "Improving building energy efficiency in India: State-level analysis of building energy efficiency policies," Energy Policy, Elsevier, vol. 110(C), pages 331-341.
    15. Jiang, Sheng-Long & Wang, Meihong & Bogle, I. David L., 2023. "Plant-wide byproduct gas distribution under uncertainty in iron and steel industry via quantile forecasting and robust optimization," Applied Energy, Elsevier, vol. 350(C).
    16. Stefan Arens & Sunke Schlüters & Benedikt Hanke & Karsten von Maydell & Carsten Agert, 2020. "Sustainable Residential Energy Supply: A Literature Review-Based Morphological Analysis," Energies, MDPI, vol. 13(2), pages 1-28, January.
    17. Ahsan, Syed M. & Khan, Hassan A. & Hassan, Naveed-ul & Arif, Syed M. & Lie, Tek-Tjing, 2020. "Optimized power dispatch for solar photovoltaic-storage system with multiple buildings in bilateral contracts," Applied Energy, Elsevier, vol. 273(C).
    18. Peng, Shiliang & Fan, Lin & Zhang, Li & Su, Huai & He, Yuxuan & He, Qian & Wang, Xiao & Yu, Dejun & Zhang, Jinjun, 2024. "Spatio-temporal prediction of total energy consumption in multiple regions using explainable deep neural network," Energy, Elsevier, vol. 301(C).
    19. Huang, Pei & Wu, Hunjun & Huang, Gongsheng & Sun, Yongjun, 2018. "A top-down control method of nZEBs for performance optimization at nZEB-cluster-level," Energy, Elsevier, vol. 159(C), pages 891-904.
    20. Yuchun Li & Yinghua Han & Jinkuan Wang & Qiang Zhao, 2018. "A MBCRF Algorithm Based on Ensemble Learning for Building Demand Response Considering the Thermal Comfort," Energies, MDPI, vol. 11(12), pages 1-20, December.

    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:appene:v:253:y:2019:i:c:93. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.