IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v356y2024ics0306261923016768.html
   My bibliography  Save this article

Data-driven surrogate optimization for deploying heterogeneous multi-energy storage to improve demand response performance at building cluster level

Author

Listed:
  • Ren, Haoshan
  • Gao, Dian-ce
  • Ma, Zhenjun
  • Zhang, Sheng
  • Sun, Yongjun

Abstract

Energy storage such as battery and thermal energy storage is an effective approach to shift building peak load and alleviate grid stress at a building cluster level. However, due to the heterogeneous performance of different types of storage (e.g., response speed, charge/discharge efficiency and rate, storage capacity) and highly diversified energy use patterns of individual buildings, the multi-energy storage should be properly selected and optimally designed for individual buildings to achieve effective load shifting. The optimal deployment of multi-energy storage at a cluster level is a challenging optimization problem due to the nonlinear dynamic performance of the multi-energy storage and the high dimensionality as a result of a large number of buildings. To tackle the challenges, this study proposes a data-driven surrogate optimization method that optimally deploys multi-energy storage at a cluster level to minimize the building cluster energy bill under demand response programs. The method utilizes data-driven surrogate models to accurately predict demand response performance of individual buildings with multi-energy storage. An iterative optimization with automated energy-storage-option screening is developed to optimize the multi-energy storage configurations and design parameters. For a case study including 21 buildings, by optimally deploying multi-energy storage including battery, cooling TES tank, and building-integrated TES, the method reduced the building cluster energy bill by 8%–181% as compared to baseline cases. The optimal deployment method effectively identifies the buildings with better potential to adopt demand-side management and balances the pros and cons of the energy storage options, increasing demand response incentives by 12%–31%. The proposed method can be used in practice to facilitate the deployment of energy storage and improve engagement of buildings in demand response.

Suggested Citation

  • Ren, Haoshan & Gao, Dian-ce & Ma, Zhenjun & Zhang, Sheng & Sun, Yongjun, 2024. "Data-driven surrogate optimization for deploying heterogeneous multi-energy storage to improve demand response performance at building cluster level," Applied Energy, Elsevier, vol. 356(C).
    • Handle: RePEc:eee:appene:v:356:y:2024:i:c:s0306261923016768
    DOI: 10.1016/j.apenergy.2023.122312
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923016768
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.122312?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. Chen, Yongbao & Xu, Peng & Chen, Zhe & Wang, Hongxin & Sha, Huajing & Ji, Ying & Zhang, Yongming & Dou, Qiang & Wang, Sheng, 2020. "Experimental investigation of demand response potential of buildings: Combined passive thermal mass and active storage," Applied Energy, Elsevier, vol. 280(C).
    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. Benjamin Schäfer & Dirk Witthaut & Marc Timme & Vito Latora, 2018. "Dynamically induced cascading failures in power grids," Nature Communications, Nature, vol. 9(1), pages 1-13, December.
    4. Duvignau, Romaric & Heinisch, Verena & Göransson, Lisa & Gulisano, Vincenzo & Papatriantafilou, Marina, 2021. "Benefits of small-size communities for continuous cost-optimization in peer-to-peer energy sharing," Applied Energy, Elsevier, vol. 301(C).
    5. Koskela, Juha & Rautiainen, Antti & Järventausta, Pertti, 2019. "Using electrical energy storage in residential buildings – Sizing of battery and photovoltaic panels based on electricity cost optimization," Applied Energy, Elsevier, vol. 239(C), pages 1175-1189.
    6. 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.
    7. Benjamin Schäfer & Dirk Witthaut & Marc Timme & Vito Latora, 2018. "Author Correction: Dynamically induced cascading failures in power grids," Nature Communications, Nature, vol. 9(1), pages 1-1, December.
    8. Amini Toosi, Hashem & Del Pero, Claudio & Leonforte, Fabrizio & Lavagna, Monica & Aste, Niccolò, 2023. "Machine learning for performance prediction in smart buildings: Photovoltaic self-consumption and life cycle cost optimization," Applied Energy, Elsevier, vol. 334(C).
    9. Lizana, Jesús & Chacartegui, Ricardo & Barrios-Padura, Angela & Valverde, José Manuel, 2017. "Advances in thermal energy storage materials and their applications towards zero energy buildings: A critical review," Applied Energy, Elsevier, vol. 203(C), pages 219-239.
    10. Lizana, Jesus & de-Borja-Torrejon, Manuel & Barrios-Padura, Angela & Auer, Thomas & Chacartegui, Ricardo, 2019. "Passive cooling through phase change materials in buildings. A critical study of implementation alternatives," Applied Energy, Elsevier, vol. 254(C).
    11. Hou, Juan & Li, Haoran & Nord, Natasa, 2022. "Nonlinear model predictive control for the space heating system of a university building in Norway," Energy, Elsevier, vol. 253(C).
    12. Zhang, Yelin & Zhang, Xingxing & Huang, Pei & Sun, Yongjun, 2020. "Global sensitivity analysis for key parameters identification of net-zero energy buildings for grid interaction optimization," Applied Energy, Elsevier, vol. 279(C).
    13. Zhang, Yuna & Augenbroe, Godfried, 2018. "Optimal demand charge reduction for commercial buildings through a combination of efficiency and flexibility measures," Applied Energy, Elsevier, vol. 221(C), pages 180-194.
    14. Nolan, Sheila & O’Malley, Mark, 2015. "Challenges and barriers to demand response deployment and evaluation," Applied Energy, Elsevier, vol. 152(C), pages 1-10.
    15. Fu, Yangyang & O'Neill, Zheng & Wen, Jin & Pertzborn, Amanda & Bushby, Steven T., 2022. "Utilizing commercial heating, ventilating, and air conditioning systems to provide grid services: A review," Applied Energy, Elsevier, vol. 307(C).
    16. Gao, Dian-ce & Sun, Yongjun & Zhang, Xingxing & Huang, Pei & Yelin Zhang,, 2022. "A GA-based NZEB-cluster planning and design optimization method for mitigating grid overvoltage risk," Energy, Elsevier, vol. 243(C).
    17. Aghaei, Jamshid & Alizadeh, Mohammad-Iman, 2013. "Demand response in smart electricity grids equipped with renewable energy sources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 64-72.
    18. Li, Han & Johra, Hicham & de Andrade Pereira, Flavia & Hong, Tianzhen & Le Dréau, Jérôme & Maturo, Anthony & Wei, Mingjun & Liu, Yapan & Saberi-Derakhtenjani, Ali & Nagy, Zoltan & Marszal-Pomianowska,, 2023. "Data-driven key performance indicators and datasets for building energy flexibility: A review and perspectives," Applied Energy, Elsevier, vol. 343(C).
    19. 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.
    20. Wang, Jianxiao & Zhong, Haiwang & Ma, Ziming & Xia, Qing & Kang, Chongqing, 2017. "Review and prospect of integrated demand response in the multi-energy system," Applied Energy, Elsevier, vol. 202(C), pages 772-782.
    21. Novoa, Laura & Flores, Robert & Brouwer, Jack, 2019. "Optimal renewable generation and battery storage sizing and siting considering local transformer limits," Applied Energy, Elsevier, vol. 256(C).
    22. Huang, Pei & Sun, Yongjun & Lovati, Marco & Zhang, Xingxing, 2021. "Solar-photovoltaic-power-sharing-based design optimization of distributed energy storage systems for performance improvements," Energy, Elsevier, vol. 222(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. Dan, Zhaohui & Song, Aoye & Yu, Xiaojun & Zhou, Yuekuan, 2024. "Electrification-driven circular economy with machine learning-based multi-scale and cross-scale modelling approach," Energy, Elsevier, vol. 299(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. Xu, Ruoyu & Liu, Xiaochen & Liu, Xiaohua & Zhang, Tao, 2024. "Quantifying the energy flexibility potential of a centralized air-conditioning system: A field test study of hub airports," Energy, Elsevier, vol. 298(C).
    2. Wu, Long & Yin, Xunyuan & Pan, Lei & Liu, Jinfeng, 2023. "Distributed economic predictive control of integrated energy systems for enhanced synergy and grid response: A decomposition and cooperation strategy," Applied Energy, Elsevier, vol. 349(C).
    3. Brunner, L.G. & Peer, R.A.M. & Zorn, C. & Paulik, R. & Logan, T.M., 2024. "Understanding cascading risks through real-world interdependent urban infrastructure," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    4. Hao Wu & Xiangyi Meng & Michael M. Danziger & Sean P. Cornelius & Hui Tian & Albert-László Barabási, 2022. "Fragmentation of outage clusters during the recovery of power distribution grids," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    5. Cai, Qiran & Xu, Qingyang & Qing, Jing & Shi, Gang & Liang, Qiao-Mei, 2022. "Promoting wind and photovoltaics renewable energy integration through demand response: Dynamic pricing mechanism design and economic analysis for smart residential communities," Energy, Elsevier, vol. 261(PB).
    6. Kocaman, Ayse Selin & Ozyoruk, Emin & Taneja, Shantanu & Modi, Vijay, 2020. "A stochastic framework to evaluate the impact of agricultural load flexibility on the sizing of renewable energy systems," Renewable Energy, Elsevier, vol. 152(C), pages 1067-1078.
    7. Benjamin Schäfer & Thiemo Pesch & Debsankha Manik & Julian Gollenstede & Guosong Lin & Hans-Peter Beck & Dirk Witthaut & Marc Timme, 2022. "Understanding Braess’ Paradox in power grids," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    8. Wu, Chengxing & Duan, Dongli, 2024. "Collapse process prediction of mutualistic dynamical networks with k-core and dimension reduction method," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    9. Gjorgievski, Vladimir Z. & Cundeva, Snezana & Georghiou, George E., 2021. "Social arrangements, technical designs and impacts of energy communities: A review," Renewable Energy, Elsevier, vol. 169(C), pages 1138-1156.
    10. Gharebaghi, Sina & Chaudhuri, Nilanjan Ray & He, Ting & La Porta, Thomas, 2023. "An approach for fast cascading failure simulation in dynamic models of power systems," Applied Energy, Elsevier, vol. 332(C).
    11. Kirchem, Dana & Lynch, Muireann Á. & Bertsch, Valentin & Casey, Eoin, 2020. "Modelling demand response with process models and energy systems models: Potential applications for wastewater treatment within the energy-water nexus," Applied Energy, Elsevier, vol. 260(C).
    12. Pei, Jianxin & Liu, Ying & Wang, Wei & Gong, Jie, 2021. "Cascading failures in multiplex network under flow redistribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    13. da Fonseca, André L.A. & Chvatal, Karin M.S. & Fernandes, Ricardo A.S., 2021. "Thermal comfort maintenance in demand response programs: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    14. Gao, Dian-ce & Sun, Yongjun & Zhang, Xingxing & Huang, Pei & Yelin Zhang,, 2022. "A GA-based NZEB-cluster planning and design optimization method for mitigating grid overvoltage risk," Energy, Elsevier, vol. 243(C).
    15. Alizadeh, Ali & Esfahani, Moein & Kamwa, Innocent & Moeini, Ali & Mohseni-Bonab, Seyed Masoud, 2024. "A useable multi-level BESSs sizing model for low-level data accessibility with risk assessment application under marketization and high uncertainties," Energy, Elsevier, vol. 290(C).
    16. Cruz, Marco R.M. & Fitiwi, Desta Z. & Santos, Sérgio F. & Catalão, João P.S., 2018. "A comprehensive survey of flexibility options for supporting the low-carbon energy future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 338-353.
    17. Xiao, Feng & Li, Jin & Wei, Bo, 2022. "Cascading failure analysis and critical node identification in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    18. Qu, Junyi & Liu, Ying & Tang, Ming & Guan, Shuguang, 2022. "Identification of the most influential stocks in financial networks," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    19. Sagnik Basumallik & Sara Eftekharnejad & Makan Fardad, 2022. "Controlled Islanding under Complete and Partial False Data Injection Attack Uncertainties against Phasor Measurement Units," Energies, MDPI, vol. 15(15), pages 1-27, August.
    20. Wang, Shuliang & Guo, Zhaoyang & Huang, Xiaodi & Zhang, Jianhua, 2024. "A three-stage model of quantifying and analyzing power network resilience based on network theory," Reliability Engineering and System Safety, Elsevier, vol. 241(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:356:y:2024:i:c:s0306261923016768. 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.