IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v298y2021ics0306261921005870.html
   My bibliography  Save this article

Design and dispatch optimization of packaged ice storage systems within a connected community

Author

Listed:
  • Heine, Karl
  • Tabares-Velasco, Paulo Cesar
  • Deru, Michael

Abstract

The traditional implementation of cool thermal energy storage (CTES) must be reimagined within the context of a dynamic grid and smart buildings operating as connected communities. As most buildings do not operate central chillers or connect to district cooling loops, this necessitates a broader use of packaged CTES. Our objective is to begin answering the question of how such packaged CTES should be implemented within a connected community. We do so by presenting a simulation–optimization workflow employing building energy modeling software and a mixed-integer linear program to design and dispatch a packaged CTES technology to achieve minimum total annual cost. We demonstrate this methodology on a seven-building case study using current utility rates and find that total annual cooling energy costs can be reduced by 17.8% compared to baseline, after accounting for the cost of storage. We perform three parametric sensitivity studies to evaluate modeling assumptions and obtain the prioritization of storage procurement as a function of annualized life-cycle cost of storage. We find that a community optimization approach provides significantly different results than individual building optimizations and provides greater savings compared to baseline.

Suggested Citation

  • Heine, Karl & Tabares-Velasco, Paulo Cesar & Deru, Michael, 2021. "Design and dispatch optimization of packaged ice storage systems within a connected community," Applied Energy, Elsevier, vol. 298(C).
  • Handle: RePEc:eee:appene:v:298:y:2021:i:c:s0306261921005870
    DOI: 10.1016/j.apenergy.2021.117147
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261921005870
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2021.117147?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. Good, Nicholas & Zhang, Lingxi & Navarro-Espinosa, Alejandro & Mancarella, Pierluigi, 2015. "High resolution modelling of multi-energy domestic demand profiles," Applied Energy, Elsevier, vol. 137(C), pages 193-210.
    2. Hao, Ling & Wei, Mingshan & Xu, Fei & Yang, Xiaochen & Meng, Jia & Song, Panpan & Min, Yong, 2020. "Study of operation strategies for integrating ice-storage district cooling systems into power dispatch for large-scale hydropower utilization," Applied Energy, Elsevier, vol. 261(C).
    3. Mazzoni, Stefano & Sze, Jia Yin & Nastasi, Benedetto & Ooi, Sean & Desideri, Umberto & Romagnoli, Alessandro, 2021. "A techno-economic assessment on the adoption of latent heat thermal energy storage systems for district cooling optimal dispatch & operations," Applied Energy, Elsevier, vol. 289(C).
    4. Barbour, Edward & Parra, David & Awwad, Zeyad & González, Marta C., 2018. "Community energy storage: A smart choice for the smart grid?," Applied Energy, Elsevier, vol. 212(C), pages 489-497.
    5. Tan, Pepe & Lindberg, Patrik & Eichler, Kaia & Löveryd, Per & Johansson, Pär & Kalagasidis, Angela Sasic, 2020. "Thermal energy storage using phase change materials: Techno-economic evaluation of a cold storage installation in an office building," Applied Energy, Elsevier, vol. 276(C).
    6. Kamal, Rajeev & Moloney, Francesca & Wickramaratne, Chatura & Narasimhan, Arunkumar & Goswami, D.Y., 2019. "Strategic control and cost optimization of thermal energy storage in buildings using EnergyPlus," Applied Energy, Elsevier, vol. 246(C), pages 77-90.
    7. Naranjo Palacio, Santiago & Valentine, Keenan F. & Wong, Myra & Zhang, K. Max, 2014. "Reducing power system costs with thermal energy storage," Applied Energy, Elsevier, vol. 129(C), pages 228-237.
    8. 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.
    9. Ruan, Yingjun & Liu, Qingrong & Li, Zhengwei & Wu, Jiazheng, 2016. "Optimization and analysis of Building Combined Cooling, Heating and Power (BCHP) plants with chilled ice thermal storage system," Applied Energy, Elsevier, vol. 179(C), pages 738-754.
    10. Cole, Wesley J. & Rhodes, Joshua D. & Gorman, William & Perez, Krystian X. & Webber, Michael E. & Edgar, Thomas F., 2014. "Community-scale residential air conditioning control for effective grid management," Applied Energy, Elsevier, vol. 130(C), pages 428-436.
    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. Zhao, Yaohua & Liu, Zichu & Quan, Zhenhua & Jing, Heran & Yang, Mingguang, 2022. "Experimental investigation and multi-objective optimization of ice thermal storage device with multichannel flat tube," Renewable Energy, Elsevier, vol. 195(C), pages 28-46.
    2. Jia, Lizhi & Liu, Junjie & Chong, Adrian & Dai, Xilei, 2022. "Deep learning and physics-based modeling for the optimization of ice-based thermal energy systems in cooling plants," Applied Energy, Elsevier, vol. 322(C).
    3. Liu, Zichu & Quan, Zhenhua & Zhang, Nan & Wang, Yubo & Yang, Mingguang & Zhao, Yaohua, 2023. "Energy and exergy analysis of a novel direct-expansion ice thermal storage system based on three-fluid heat exchanger module," Applied Energy, Elsevier, vol. 330(PB).
    4. Wang, Zhaojun & Zhang, Zhonghui & Zhang, Zhonglian & Lei, Dayong & Li, Moxuan & Zhang, Liuyu, 2023. "Two-layer optimization of integrated energy system with considering ambient temperature effect and variable operation scheme," Energy, Elsevier, vol. 278(C).
    5. Meng, Anbo & Xu, Xuancong & Zhang, Zhan & Zeng, Cong & Liang, Ruduo & Zhang, Zheng & Wang, Xiaolin & Yan, Baiping & Yin, Hao & Luo, Jianqiang, 2022. "Solving high-dimensional multi-area economic dispatch problem by decoupled distributed crisscross optimization algorithm with population cross generation strategy," Energy, Elsevier, vol. 258(C).
    6. Filip Vrbanc & Mario Vašak & Vinko Lešić, 2023. "Simple and Accurate Model of Thermal Storage with Phase Change Material Tailored for Model Predictive Control," Energies, MDPI, vol. 16(19), pages 1-18, September.
    7. Guo, Jiacheng & Liu, Zhijian & Wu, Xuan & Wu, Di & Zhang, Shicong & Yang, Xinyan & Ge, Hua & Zhang, Peiwen, 2022. "Two-layer co-optimization method for a distributed energy system combining multiple energy storages," Applied Energy, Elsevier, vol. 322(C).
    8. Fanghan Su & Zhiyuan Wang & Yue Yuan & Chengcheng Song & Kejun Zeng & Yixing Chen & Rongpeng Zhang, 2023. "Enhanced Operation of Ice Storage System for Peak Load Management in Shopping Malls across Diverse Climate Zones," Sustainability, MDPI, vol. 15(20), pages 1-23, October.

    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. Zou, Wenke & Sun, Yongjun & Gao, Dian-ce & Zhang, Xu, 2023. "Globally optimal control of hybrid chilled water plants integrated with small-scale thermal energy storage for energy-efficient operation," Energy, Elsevier, vol. 262(PA).
    2. Luo, Na & Hong, Tianzhen & Li, Hui & Jia, Ruoxi & Weng, Wenguo, 2017. "Data analytics and optimization of an ice-based energy storage system for commercial buildings," Applied Energy, Elsevier, vol. 204(C), pages 459-475.
    3. Jia, Lizhi & Liu, Junjie & Chong, Adrian & Dai, Xilei, 2022. "Deep learning and physics-based modeling for the optimization of ice-based thermal energy systems in cooling plants," Applied Energy, Elsevier, vol. 322(C).
    4. 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).
    5. Zhu, Jianquan & Xia, Yunrui & Mo, Xiemin & Guo, Ye & Chen, Jiajun, 2021. "A bilevel bidding and clearing model incorporated with a pricing strategy for the trading of energy storage use rights," Energy, Elsevier, vol. 235(C).
    6. Chen, Yizhong & He, Li & Li, Jing, 2017. "Stochastic dominant-subordinate-interactive scheduling optimization for interconnected microgrids with considering wind-photovoltaic-based distributed generations under uncertainty," Energy, Elsevier, vol. 130(C), pages 581-598.
    7. Yang, Weijia & Huang, Yuping & Zhao, Daiqing, 2023. "A coupled hydraulic–thermal dynamic model for the steam network in a heat–electricity integrated energy system," Energy, Elsevier, vol. 263(PC).
    8. Good, Nicholas & Martínez Ceseña, Eduardo A. & Zhang, Lingxi & Mancarella, Pierluigi, 2016. "Techno-economic and business case assessment of low carbon technologies in distributed multi-energy systems," Applied Energy, Elsevier, vol. 167(C), pages 158-172.
    9. Hafiz, Faeza & Rodrigo de Queiroz, Anderson & Fajri, Poria & Husain, Iqbal, 2019. "Energy management and optimal storage sizing for a shared community: A multi-stage stochastic programming approach," Applied Energy, Elsevier, vol. 236(C), pages 42-54.
    10. Liu, Liu & Niu, Jianlei & Wu, Jian-Yong, 2023. "Improving energy efficiency of photovoltaic/thermal systems by cooling with PCM nano-emulsions: An indoor experimental study," Renewable Energy, Elsevier, vol. 203(C), pages 568-582.
    11. Lombardi, Francesco & Balderrama, Sergio & Quoilin, Sylvain & Colombo, Emanuela, 2019. "Generating high-resolution multi-energy load profiles for remote areas with an open-source stochastic model," Energy, Elsevier, vol. 177(C), pages 433-444.
    12. Younghoon Kwak & Jeonga Kang & Sun-Hye Mun & Young-Sun Jeong & Jung-Ho Huh, 2020. "Development and Application of a Flexible Modeling Approach to Reference Buildings for Energy Analysis," Energies, MDPI, vol. 13(21), pages 1-22, November.
    13. Chan, Lok Shun, 2022. "Neighbouring shading effect on photovoltaic panel system: Its implication to green building certification scheme," Renewable Energy, Elsevier, vol. 188(C), pages 476-490.
    14. Kim, Icksung & Kim, Woohyun, 2023. "Application of market-based control with thermal energy storage system for demand limiting and real-time pricing control," Energy, Elsevier, vol. 263(PA).
    15. Aghamolaei, Reihaneh & Shamsi, Mohammad Haris & O’Donnell, James, 2020. "Feasibility analysis of community-based PV systems for residential districts: A comparison of on-site centralized and distributed PV installations," Renewable Energy, Elsevier, vol. 157(C), pages 793-808.
    16. Fan Li & Jingxi Su & Bo Sun, 2023. "An Optimal Scheduling Method for an Integrated Energy System Based on an Improved k-Means Clustering Algorithm," Energies, MDPI, vol. 16(9), pages 1-22, April.
    17. William Clements & Surendra Pandit & Prashanna Bajracharya & Joe Butchers & Sam Williamson & Biraj Gautam & Paul Harper, 2021. "Techno-Economic Modelling of Micro-Hydropower Mini-Grids in Nepal to Improve Financial Sustainability and Enable Electric Cooking," Energies, MDPI, vol. 14(14), pages 1-23, July.
    18. Zhao, Yaohua & Liu, Zichu & Quan, Zhenhua & Jing, Heran & Yang, Mingguang, 2022. "Experimental investigation and multi-objective optimization of ice thermal storage device with multichannel flat tube," Renewable Energy, Elsevier, vol. 195(C), pages 28-46.
    19. Fioriti, Davide & Frangioni, Antonio & Poli, Davide, 2021. "Optimal sizing of energy communities with fair revenue sharing and exit clauses: Value, role and business model of aggregators and users," Applied Energy, Elsevier, vol. 299(C).
    20. Wu, Qiong & Ren, Hongbo & Gao, Weijun & Ren, Jianxing, 2017. "Benefit allocation for distributed energy network participants applying game theory based solutions," Energy, Elsevier, vol. 119(C), pages 384-391.

    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:298:y:2021:i:c:s0306261921005870. 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.