IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v346y2023ics0306261923007262.html
   My bibliography  Save this article

A data-driven rolling optimization control approach for building energy systems that integrate virtual energy storage systems

Author

Listed:
  • Mu, Yunfei
  • Xu, Yanze
  • Zhang, Jiarui
  • Wu, Zeqing
  • Jia, Hongjie
  • Jin, Xiaolong
  • Qi, Yan

Abstract

The virtual energy storage system (VESS) is an innovative and cost-effective technique for coupling building envelope thermal storage and release abilities with the electric and heat power conversion characteristics of an air conditioner; this system provides building energy systems (BESs) with adjustable potentials similar to those of conventional battery energy storage systems (BESSs). However, the VESS is a dynamic system, and uncertainties in the outdoor temperature and solar irradiance are difficult to accurately predict, which impacts the quantification accuracy of VESSs; these characteristics challenge the BES control scheme economy and the thermal comfort of occupants. To solve this crucial issue, a data-driven rolling optimization (RO) control approach for a BES that integrates a VESS is proposed. First, a BES state space model integrating the VESS is created to reflect the VESS adjustable potential and dynamic characteristics. Based on the above model, while aiming at a small BES data sample size, a support vector machine (SVM) is combined with RO to correct the day-ahead quantification errors of the VESS adjustable potential and enhance the economical operation and thermal comfort of the BES that integrates the VESS in uncertain environments. Comparative simulations validate the effectiveness of this VESS modelling and data-driven RO control approach.

Suggested Citation

  • Mu, Yunfei & Xu, Yanze & Zhang, Jiarui & Wu, Zeqing & Jia, Hongjie & Jin, Xiaolong & Qi, Yan, 2023. "A data-driven rolling optimization control approach for building energy systems that integrate virtual energy storage systems," Applied Energy, Elsevier, vol. 346(C).
    • Handle: RePEc:eee:appene:v:346:y:2023:i:c:s0306261923007262
    DOI: 10.1016/j.apenergy.2023.121362
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923007262
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.121362?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. Smarra, Francesco & Jain, Achin & de Rubeis, Tullio & Ambrosini, Dario & D’Innocenzo, Alessandro & Mangharam, Rahul, 2018. "Data-driven model predictive control using random forests for building energy optimization and climate control," Applied Energy, Elsevier, vol. 226(C), pages 1252-1272.
    2. Mu, Yunfei & Xu, Yurui & Cao, Yan & Chen, Wanqing & Jia, Hongjie & Yu, Xiaodan & Jin, Xiaolong, 2022. "A two-stage scheduling method for integrated community energy system based on a hybrid mechanism and data-driven model," Applied Energy, Elsevier, vol. 323(C).
    3. Solano, J.C. & Olivieri, L. & Caamaño-Martín, E., 2017. "Assessing the potential of PV hybrid systems to cover HVAC loads in a grid-connected residential building through intelligent control," Applied Energy, Elsevier, vol. 206(C), pages 249-266.
    4. Golpîra, Hêriş & Khan, Syed Abdul Rehman, 2019. "A multi-objective risk-based robust optimization approach to energy management in smart residential buildings under combined demand and supply uncertainty," Energy, Elsevier, vol. 170(C), pages 1113-1129.
    5. C. V. Rao & S. J. Wright & J. B. Rawlings, 1998. "Application of Interior-Point Methods to Model Predictive Control," Journal of Optimization Theory and Applications, Springer, vol. 99(3), pages 723-757, December.
    6. Wang, Ying & Wang, Jianzhou & Li, Zhiwu & Yang, Hufang & Li, Hongmin, 2021. "Design of a combined system based on two-stage data preprocessing and multi-objective optimization for wind speed prediction," Energy, Elsevier, vol. 231(C).
    7. Lopes, Rui Amaral & Martins, João & Aelenei, Daniel & Lima, Celson Pantoja, 2016. "A cooperative net zero energy community to improve load matching," Renewable Energy, Elsevier, vol. 93(C), pages 1-13.
    8. 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.
    9. Ghiaus, Christian & Ahmad, Naveed, 2020. "Thermal circuits assembling and state-space extraction for modelling heat transfer in buildings," Energy, Elsevier, vol. 195(C).
    10. Luthander, Rasmus & Nilsson, Annica M. & Widén, Joakim & Åberg, Magnus, 2019. "Graphical analysis of photovoltaic generation and load matching in buildings: A novel way of studying self-consumption and self-sufficiency," Applied Energy, Elsevier, vol. 250(C), pages 748-759.
    11. Reynders, Glenn & Diriken, Jan & Saelens, Dirk, 2017. "Generic characterization method for energy flexibility: Applied to structural thermal storage in residential buildings," Applied Energy, Elsevier, vol. 198(C), pages 192-202.
    12. Seal, Sayani & Boulet, Benoit & Dehkordi, Vahid R., 2020. "Centralized model predictive control strategy for thermal comfort and residential energy management," Energy, Elsevier, vol. 212(C).
    13. Cox, Sam J. & Kim, Dongsu & Cho, Heejin & Mago, Pedro, 2019. "Real time optimal control of district cooling system with thermal energy storage using neural networks," Applied Energy, Elsevier, vol. 238(C), pages 466-480.
    14. Jung, Seunghoon & Jeoung, Jaewon & Kang, Hyuna & Hong, Taehoon, 2021. "Optimal planning of a rooftop PV system using GIS-based reinforcement learning," Applied Energy, Elsevier, vol. 298(C).
    15. Wang, Shuai & Li, Bin & Li, Guanzheng & Yao, Bin & Wu, Jianzhong, 2021. "Short-term wind power prediction based on multidimensional data cleaning and feature reconfiguration," Applied Energy, Elsevier, vol. 292(C).
    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. Fang, Daohong & Tang, Hao & Hatziargyriou, Nikos & Zhang, Tao & Chen, Wenjuan & Zhang, Qianli, 2024. "Dual-center control scheme and FF-DHRL-based collaborative optimization for charging stations under intra-day peak-shaving demand," Applied Energy, Elsevier, vol. 368(C).
    2. Jiawei Wang & Aidong Zeng & Yaheng Wan, 2023. "Multi-Time-Scale Optimal Scheduling of Integrated Energy System Considering Transmission Delay and Heat Storage of Heating Network," Sustainability, MDPI, vol. 15(19), pages 1-26, September.

    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. Bampoulas, Adamantios & Pallonetto, Fabiano & Mangina, Eleni & Finn, Donal P., 2022. "An ensemble learning-based framework for assessing the energy flexibility of residential buildings with multicomponent energy systems," Applied Energy, Elsevier, vol. 315(C).
    2. Mu, Yunfei & Xu, Yurui & Cao, Yan & Chen, Wanqing & Jia, Hongjie & Yu, Xiaodan & Jin, Xiaolong, 2022. "A two-stage scheduling method for integrated community energy system based on a hybrid mechanism and data-driven model," Applied Energy, Elsevier, vol. 323(C).
    3. Nicola Franzoi & Alessandro Prada & Sara Verones & Paolo Baggio, 2021. "Enhancing PV Self-Consumption through Energy Communities in Heating-Dominated Climates," Energies, MDPI, vol. 14(14), pages 1-17, July.
    4. Xiaoyi Zhang & Weijun Gao & Yanxue Li & Zixuan Wang & Yoshiaki Ushifusa & Yingjun Ruan, 2021. "Operational Performance and Load Flexibility Analysis of Japanese Zero Energy House," IJERPH, MDPI, vol. 18(13), pages 1-19, June.
    5. Guo, Honggang & Wang, Jianzhou & Li, Zhiwu & Jin, Yu, 2022. "A multivariable hybrid prediction system of wind power based on outlier test and innovative multi-objective optimization," Energy, Elsevier, vol. 239(PE).
    6. Liu, Jiangyan & Zhang, Qing & Dong, Zhenxiang & Li, Xin & Li, Guannan & Xie, Yi & Li, Kuining, 2021. "Quantitative evaluation of the building energy performance based on short-term energy predictions," Energy, Elsevier, vol. 223(C).
    7. Chen, Yongbao & Chen, Zhe & Xu, Peng & Li, Weilin & Sha, Huajing & Yang, Zhiwei & Li, Guowen & Hu, Chonghe, 2019. "Quantification of electricity flexibility in demand response: Office building case study," Energy, Elsevier, vol. 188(C).
    8. Guo, Yurun & Wang, Shugang & Wang, Jihong & Zhang, Tengfei & Ma, Zhenjun & Jiang, Shuang, 2024. "Key district heating technologies for building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    9. Vallianos, Charalampos & Candanedo, José & Athienitis, Andreas, 2023. "Application of a large smart thermostat dataset for model calibration and Model Predictive Control implementation in the residential sector," Energy, Elsevier, vol. 278(PA).
    10. He, Zhaoyu & Guo, Weimin & Zhang, Peng, 2022. "Performance prediction, optimal design and operational control of thermal energy storage using artificial intelligence methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    11. Zhou, Yu & Li, Zhengshuo & Wang, Guangrui, 2021. "Study on leveraging wind farms' robust reactive power range for uncertain power system reactive power optimization," Applied Energy, Elsevier, vol. 298(C).
    12. Reza Fachrizal & Joakim Munkhammar, 2020. "Improved Photovoltaic Self-Consumption in Residential Buildings with Distributed and Centralized Smart Charging of Electric Vehicles," Energies, MDPI, vol. 13(5), pages 1-19, March.
    13. Moura, Ricardo & Brito, Miguel Centeno, 2019. "Prosumer aggregation policies, country experience and business models," Energy Policy, Elsevier, vol. 132(C), pages 820-830.
    14. 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).
    15. Xu, Jing & Wang, Xiaoying & Gu, Yujiong & Ma, Suxia, 2023. "A data-based day-ahead scheduling optimization approach for regional integrated energy systems with varying operating conditions," Energy, Elsevier, vol. 283(C).
    16. You, Zhengjie & Lumpp, Sebastian Dirk & Doepfert, Markus & Tzscheutschler, Peter & Goebel, Christoph, 2024. "Leveraging flexibility of residential heat pumps through local energy markets," Applied Energy, Elsevier, vol. 355(C).
    17. Zhao, Jing & Yang, Zilan & Shi, Linyu & Liu, Dehan & Li, Haonan & Mi, Yumiao & Wang, Hongbin & Feng, Meili & Hutagaol, Timothy Joseph, 2024. "Photovoltaic capacity dynamic tracking model predictive control strategy of air-conditioning systems with consideration of flexible loads," Applied Energy, Elsevier, vol. 356(C).
    18. João Tabanêz Patrício & Rui Amaral Lopes & Naim Majdalani & Daniel Aelenei & João Martins, 2023. "Aggregated Use of Energy Flexibility in Office Buildings," Energies, MDPI, vol. 16(2), pages 1-17, January.
    19. Amadeh, Ali & Lee, Zachary E. & Zhang, K. Max, 2022. "Quantifying demand flexibility of building energy systems under uncertainty," Energy, Elsevier, vol. 246(C).
    20. Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(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:appene:v:346:y:2023:i:c:s0306261923007262. 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.