IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i5p4388-d1084592.html
   My bibliography  Save this article

Research on a Variable Water Supply Temperature Strategy for a Ground-Source Heat Pump System Based on TRNSYS-GENOPT (TRNOPT) Optimization

Author

Listed:
  • Jiaqi Cao

    (School of Thermal Engineering, Shandong Jianzhu University, Jinan 250101, China)

  • Shiyu Zhou

    (School of Thermal Engineering, Shandong Jianzhu University, Jinan 250101, China)

  • Tao Wang

    (801 Institute of Hydrogeology and Engineering Geology, Shandong Provincial Bureau of Geology & Mineral Resources, Jinan 250013, China
    Shandong Engineering Research Center for Environmental Protection and Remediation on Groundwater, Jinan 250013, China)

  • Baoqi Shan

    (Ecology Institute of Shandong Academy of Sciences, Jinan 250101, China)

  • Xueping Liu

    (Shandong Institute of Product Quality Inspection, Jinan 250101, China)

Abstract

An office building located at Jinan equipped with ground-source heat pump (GSHP) system was selected as the research object. The GSHP system model was established using TRNSYS software. With the total energy consumption of the system as the objective function, several control strategies were proposed for the optimization work of water supply temperature at the load side of the heat pump unit. Firstly, a variable water temperature control strategy was adjusted according to the load ratio of the unit. In addition, the TRNSYS-GENOPT (TRNOPT) optimization module in TRNSYS was used to find the optimal water supply temperatures for different load ratios. After simulating and comparing the system’s energy consumption under the three control strategies, we found that the total annual energy consumption under the variable water supply temperature scheme is less than that under the constant water supply temperature scheme by 10,531.41 kWh. The energy saving ratio is about 5.7%. The simulation found that the total annual energy consumption under the optimized water supply temperature based on TRNOPT is lower than that under the variable water supply temperature scheme by 1072.04 kWh, and it is lower than that under the constant water supply temperature scheme by 11,603.45 kWh. The annual energy saving ratio of the system is about 6.3%. It is concluded that the optimized water supply temperature scheme based on TRNOPT has a better energy saving effect than the first two water supply temperature schemes.

Suggested Citation

  • Jiaqi Cao & Shiyu Zhou & Tao Wang & Baoqi Shan & Xueping Liu, 2023. "Research on a Variable Water Supply Temperature Strategy for a Ground-Source Heat Pump System Based on TRNSYS-GENOPT (TRNOPT) Optimization," Sustainability, MDPI, vol. 15(5), pages 1-14, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:4388-:d:1084592
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/5/4388/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/5/4388/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chang, Yung-Chung & Chen, Wu-Hsing, 2009. "Optimal chilled water temperature calculation of multiple chiller systems using Hopfield neural network for saving energy," Energy, Elsevier, vol. 34(4), pages 448-456.
    2. Shuiping Zhu & Jianjun Sun & Kaiyang Zhong & Haisheng Chen, 2021. "Numerical Investigation of the Influence of Precooling on the Thermal Performance of a Borehole Heat Exchanger," Energies, MDPI, vol. 15(1), pages 1-15, December.
    3. Thangavelu, Sundar Raj & Myat, Aung & Khambadkone, Ashwin, 2017. "Energy optimization methodology of multi-chiller plant in commercial buildings," Energy, Elsevier, vol. 123(C), pages 64-76.
    4. Li, Yang & Han, Meng & Shahidehpour, Mohammad & Li, Jiazheng & Long, Chao, 2023. "Data-driven distributionally robust scheduling of community integrated energy systems with uncertain renewable generations considering integrated demand response," Applied Energy, Elsevier, vol. 335(C).
    5. Lämmle, Manuel & Bongs, Constanze & Wapler, Jeannette & Günther, Danny & Hess, Stefan & Kropp, Michael & Herkel, Sebastian, 2022. "Performance of air and ground source heat pumps retrofitted to radiator heating systems and measures to reduce space heating temperatures in existing buildings," Energy, Elsevier, vol. 242(C).
    6. Arat, Halit & Arslan, Oguz, 2017. "Exergoeconomic analysis of district heating system boosted by the geothermal heat pump," Energy, Elsevier, vol. 119(C), pages 1159-1170.
    7. Walch, Alina & Li, Xiang & Chambers, Jonathan & Mohajeri, Nahid & Yilmaz, Selin & Patel, Martin & Scartezzini, Jean-Louis, 2022. "Shallow geothermal energy potential for heating and cooling of buildings with regeneration under climate change scenarios," Energy, Elsevier, vol. 244(PB).
    8. Zhang, Hongzhi & Han, Zongwei & Li, Gui & Ji, Mingzhen & Cheng, Xinlu & Li, Xiuming & Yang, Lingyan, 2021. "Study on the influence of pipe spacing on the annual performance of ground source heat pumps considering the factors of heat and moisture transfer, seepage and freezing," Renewable Energy, Elsevier, vol. 163(C), pages 262-275.
    9. Wan, Taocheng & Bai, Yan & Wang, Tingxiang & Wei, Zhuo, 2022. "BPNN-based optimal strategy for dynamic energy optimization with providing proper thermal comfort under the different outdoor air temperatures," Applied Energy, Elsevier, vol. 313(C).
    Full references (including those not matched with items on IDEAS)

    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. Liu, Xuefeng & Xu, Jinman & Bi, Mengbo & Ma, Wenjing & Chen, Wencong & Zheng, Minglong, 2024. "Multivariate coupled full-case physical model of large chilled water systems and its application," Energy, Elsevier, vol. 298(C).
    2. Yani Bao & Wai Ling Lee & Jie Jia, 2018. "Exergy Analyses and Modelling of a Novel Extra-Low Temperature Dedicated Outdoor Air System," Energies, MDPI, vol. 11(5), pages 1-25, May.
    3. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Yin, Xiaohong & Xian, Huacai, 2019. "An energy-saving oriented air balancing strategy for multi-zone demand-controlled ventilation system," Energy, Elsevier, vol. 172(C), pages 1053-1065.
    4. Ju-wan Ha & Yu-jin Kim & Kyung-soon Park & Young-hak Song, 2022. "Energy Saving Evaluation with Low Liquid to Gas Ratio Operation in HVAC&R System," Energies, MDPI, vol. 15(19), pages 1-29, October.
    5. Shen, Yuxuan & Pan, Yue, 2023. "BIM-supported automatic energy performance analysis for green building design using explainable machine learning and multi-objective optimization," Applied Energy, Elsevier, vol. 333(C).
    6. Fan, Cheng & Sun, Yongjun & Zhao, Yang & Song, Mengjie & Wang, Jiayuan, 2019. "Deep learning-based feature engineering methods for improved building energy prediction," Applied Energy, Elsevier, vol. 240(C), pages 35-45.
    7. Guelpa, Elisa & Bischi, Aldo & Verda, Vittorio & Chertkov, Michael & Lund, Henrik, 2019. "Towards future infrastructures for sustainable multi-energy systems: A review," Energy, Elsevier, vol. 184(C), pages 2-21.
    8. Li, Xiang & Yilmaz, Selin & Patel, Martin K. & Chambers, Jonathan, 2023. "Techno-economic analysis of fifth-generation district heating and cooling combined with seasonal borehole thermal energy storage," Energy, Elsevier, vol. 285(C).
    9. Cosgrove, Paul & Roulstone, Tony & Zachary, Stan, 2023. "Intermittency and periodicity in net-zero renewable energy systems with storage," Renewable Energy, Elsevier, vol. 212(C), pages 299-307.
    10. Sun, Fengrui & Yao, Yuedong & Li, Guozhen & Li, Xiangfang, 2018. "Geothermal energy extraction in CO2 rich basin using abandoned horizontal wells," Energy, Elsevier, vol. 158(C), pages 760-773.
    11. Xiaoqing Wei & Nianping Li & Jinqing Peng & Jianlin Cheng & Jinhua Hu & Meng Wang, 2017. "Modeling and Optimization of a CoolingTower-Assisted Heat Pump System," Energies, MDPI, vol. 10(5), pages 1-18, May.
    12. Shaik, Saleem, 2024. "Contribution of climate change to sector-source energy demand," Energy, Elsevier, vol. 294(C).
    13. Kusiak, Andrew & Xu, Guanglin & Tang, Fan, 2011. "Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm," Energy, Elsevier, vol. 36(10), pages 5935-5943.
    14. Kavian, Soheil & Hakkaki-Fard, Ali & Jafari Mosleh, Hassan, 2020. "Energy performance and economic feasibility of hot spring-based district heating system – A case study," Energy, Elsevier, vol. 211(C).
    15. 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).
    16. Omar Montero & Pauline Brischoux & Simon Callegari & Carolina Fraga & Matthias Rüetschi & Edouard Vionnet & Nicole Calame & Fabrice Rognon & Martin Patel & Pierre Hollmuller, 2022. "Large Air-to-Water Heat Pumps for Fuel-Boiler Substitution in Non-Retrofitted Multi-Family Buildings—Energy Performance, CO 2 Savings, and Lessons Learned in Actual Conditions of Use," Energies, MDPI, vol. 15(14), pages 1-29, July.
    17. Zhuang, Chaoqun & Wang, Shengwei & Shan, Kui, 2020. "A risk-based robust optimal chiller sequencing control strategy for energy-efficient operation considering measurement uncertainties," Applied Energy, Elsevier, vol. 280(C).
    18. Qing Yin & Chunmiao Han & Ailin Li & Xiao Liu & Ying Liu, 2024. "A Review of Research on Building Energy Consumption Prediction Models Based on Artificial Neural Networks," Sustainability, MDPI, vol. 16(17), pages 1-30, September.
    19. 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).
    20. Tianlei Zang & Shijun Wang & Zian Wang & Chuangzhi Li & Yunfei Liu & Yujian Xiao & Buxiang Zhou, 2024. "Integrated Planning and Operation Dispatching of Source–Grid–Load–Storage in a New Power System: A Coupled Socio–Cyber–Physical Perspective," Energies, MDPI, vol. 17(12), pages 1-43, June.

    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:gam:jsusta:v:15:y:2023:i:5:p:4388-:d:1084592. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.