IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v232y2018icp101-118.html
   My bibliography  Save this article

Performance investigation and economic benefits of new control strategies for heat pump-gas fired water heater hybrid system

Author

Listed:
  • Li, Gang
  • Du, Yuqing

Abstract

More recently, the hybrid system equipped with the combination of heat pump and gas fired water heater in one device is increasingly attracting people’s attention from both the industry and academia. In this study, the hybrid system with two control strategies is presented as the alternative option for domestic hot water supply and space heating with the aim of revealing its economic benefits. For the control strategy I, the two heating sources, i.e. heat pump and gas heater, can be switched and there are only two modes: heat pump mode, and gas heater mode. While for the control strategy II, the two heating sources could be handled with the proper arrangement for the loading share. There are 3 modes under this control strategy: heat pump mode, hybrid mode, and gas heater mode. The auxiliary resistance heater is used for heat pump mode if necessary, not for the hybrid mode. Various climate conditions, water initial temperature and final temperature are investigated for the hybrid system, and different heating supply forms are compared. Regarding the domestic hot water demand of 70 L per apartment per day, the gas heater can lead to a ∼38% operation energy cost saving from electric heater, and both of the two control strategies with hybrid system can result in a ∼20% to ∼65% more operation energy cost reduction than gas heater under climate condition of −5 °C to 20 °C, with a more pronounced reduction towards hot climates. Control strategy II, as the most cost-efficient option, can achieve a ∼10% to ∼23% more energy cost saving than strategy I, especially under climate condition of 0 °C to −12 °C. In the hybrid mode with strategy II, a decrease of the final temperature, results in a better economic merit. Regarding the hourly under-flooring heating case, the control strategy II, as the best option, can lead to a ∼6% to ∼70% more hourly process energy cost saving compared with the gas heater from −15 °C to 20 °C ambient conditions. During the typical heating season with ambient (−15 °C to 7 °C), the lowest process energy cost can be achieved by control strategy II. In the same external conditions, the under-flooring heating application has a lower system operation energy process cost than the indoor radiator heating case.

Suggested Citation

  • Li, Gang & Du, Yuqing, 2018. "Performance investigation and economic benefits of new control strategies for heat pump-gas fired water heater hybrid system," Applied Energy, Elsevier, vol. 232(C), pages 101-118.
  • Handle: RePEc:eee:appene:v:232:y:2018:i:c:p:101-118
    DOI: 10.1016/j.apenergy.2018.09.065
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261918313801
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2018.09.065?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. Zhou, Zhihua & Zhang, Zhiming & Zuo, Jian & Huang, Ke & Zhang, Liying, 2015. "Phase change materials for solar thermal energy storage in residential buildings in cold climate," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 692-703.
    2. Alibabaei, Nima & Fung, Alan S. & Raahemifar, Kaamran & Moghimi, Arash, 2017. "Effects of intelligent strategy planning models on residential HVAC system energy demand and cost during the heating and cooling seasons," Applied Energy, Elsevier, vol. 185(P1), pages 29-43.
    3. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    4. Fumo, Nelson & Rafe Biswas, M.A., 2015. "Regression analysis for prediction of residential energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 332-343.
    5. Zhang, Qunli & Zhang, Lin & Nie, Jinzhe & Li, Yinlong, 2017. "Techno-economic analysis of air source heat pump applied for space heating in northern China," Applied Energy, Elsevier, vol. 207(C), pages 533-542.
    6. Song, Mengjie & Gong, Guangcai & Mao, Ning & Deng, Shiming & Wang, Zhihua, 2017. "Experimental investigation on an air source heat pump unit with a three-circuit outdoor coil for its reverse cycle defrosting termination temperature," Applied Energy, Elsevier, vol. 204(C), pages 1388-1398.
    7. Song, Mengjie & Xu, Xiangguo & Mao, Ning & Deng, Shiming & Xu, Yingjie, 2017. "Energy transfer procession in an air source heat pump unit during defrosting," Applied Energy, Elsevier, vol. 204(C), pages 679-689.
    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. Chen, Yaowen & Chen, Zhihua & Wang, Dengjia & Liu, Yanfeng & Zhang, Yaya & Liu, Yanming & Zhao, Yiting & Gao, Meng & Fan, Jianhua, 2023. "Co-optimization of passive building and active solar heating system based on the objective of minimum carbon emissions," Energy, Elsevier, vol. 275(C).
    2. Beccali, Marco & Bonomolo, Marina & Martorana, Francesca & Catrini, Pietro & Buscemi, Alessandro, 2022. "Electrical hybrid heat pumps assisted by natural gas boilers: a review," Applied Energy, Elsevier, vol. 322(C).
    3. Wang, Jiawei & You, Shi & Zong, Yi & Træholt, Chresten & Dong, Zhao Yang & Zhou, You, 2019. "Flexibility of combined heat and power plants: A review of technologies and operation strategies," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    4. Wang, Jidong & Liu, Jianxin & Li, Chenghao & Zhou, Yue & Wu, Jianzhong, 2020. "Optimal scheduling of gas and electricity consumption in a smart home with a hybrid gas boiler and electric heating system," Energy, Elsevier, vol. 204(C).
    5. Wanrui Qu & Alexander Jordan & Bowen Yang & Yuxiang Chen, 2024. "In Situ Performance Analysis of Hybrid Fuel Heating System in a Net-Zero Ready House," Sustainability, MDPI, vol. 16(3), pages 1-29, January.
    6. Jin, Xin & Wu, Fengping & Xu, Tao & Huang, Gongsheng & Wu, Huijun & Zhou, Xiaoqing & Wang, Dengjia & Liu, Yanfeng & Lai, Alvin CK., 2021. "Experimental investigation of the novel melting point modified Phase–Change material for heat pump latent heat thermal energy storage application," Energy, Elsevier, vol. 216(C).
    7. Xuebin Ma & Junfeng Li & Yucheng Ren & Reaihan E & Qiugang Wang & Jie Li & Sihui Huang & Mingguo Ma, 2022. "Performance and Economic Analysis of the Multi-Energy Complementary Heating System under Different Control Strategies in Cold Regions," Energies, MDPI, vol. 15(21), pages 1-17, November.
    8. Son, Hyunsoo & Kim, Miae & Kim, Jin-Kuk, 2022. "Sustainable process integration of electrification technologies with industrial energy systems," Energy, Elsevier, vol. 239(PB).
    9. Zhao, Liyuan & Yang, Ting & Li, Wei & Zomaya, Albert Y., 2022. "Deep reinforcement learning-based joint load scheduling for household multi-energy system," Applied Energy, Elsevier, vol. 324(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, Wei & Liu, Changping & Li, Angui & Li, Ji & Qiao, Biao, 2020. "Feasibility and performance study on hybrid air source heat pump system for ultra-low energy building in severe cold region of China," Renewable Energy, Elsevier, vol. 146(C), pages 2124-2133.
    2. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
    3. Shao, Suola & Zhang, Huan & You, Shijun & Zheng, Wandong & Jiang, Lingfei, 2019. "Thermal performance analysis of a new refrigerant-heated radiator coupled with air-source heat pump heating system," Applied Energy, Elsevier, vol. 247(C), pages 78-88.
    4. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    5. Yildiz, B. & Bilbao, J.I. & Dore, J. & Sproul, A.B., 2017. "Recent advances in the analysis of residential electricity consumption and applications of smart meter data," Applied Energy, Elsevier, vol. 208(C), pages 402-427.
    6. Ahmad, Tanveer & Chen, Huanxin & Huang, Ronggeng & Yabin, Guo & Wang, Jiangyu & Shair, Jan & Azeem Akram, Hafiz Muhammad & Hassnain Mohsan, Syed Agha & Kazim, Muhammad, 2018. "Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment," Energy, Elsevier, vol. 158(C), pages 17-32.
    7. Khamma, Thulasi Ram & Zhang, Yuming & Guerrier, Stéphane & Boubekri, Mohamed, 2020. "Generalized additive models: An efficient method for short-term energy prediction in office buildings," Energy, Elsevier, vol. 213(C).
    8. Li, Sihui & Gong, Guangcai & Peng, Jinqing, 2019. "Dynamic coupling method between air-source heat pumps and buildings in China’s hot-summer/cold-winter zone," Applied Energy, Elsevier, vol. 254(C).
    9. Hamid R. Khosravani & María Del Mar Castilla & Manuel Berenguel & Antonio E. Ruano & Pedro M. Ferreira, 2016. "A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building," Energies, MDPI, vol. 9(1), pages 1-24, January.
    10. Rafiee, A. & Dias, E. & Koomen, E., 2019. "Analysing the impact of spatial context on the heat consumption of individual households," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 461-470.
    11. Rahman, Aowabin & Srikumar, Vivek & Smith, Amanda D., 2018. "Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 212(C), pages 372-385.
    12. Satre-Meloy, Aven, 2019. "Investigating structural and occupant drivers of annual residential electricity consumption using regularization in regression models," Energy, Elsevier, vol. 174(C), pages 148-168.
    13. Nikolaos Barmparesos & Dimitra Papadaki & Michalis Karalis & Kyriaki Fameliari & Margarita Niki Assimakopoulos, 2019. "In Situ Measurements of Energy Consumption and Indoor Environmental Quality of a Pre-Retrofitted Student Dormitory in Athens," Energies, MDPI, vol. 12(11), pages 1-19, June.
    14. Song, Mengjie & Deng, Shiming & Dang, Chaobin & Mao, Ning & Wang, Zhihua, 2018. "Review on improvement for air source heat pump units during frosting and defrosting," Applied Energy, Elsevier, vol. 211(C), pages 1150-1170.
    15. Rahman, Aowabin & Smith, Amanda D., 2018. "Predicting heating demand and sizing a stratified thermal storage tank using deep learning algorithms," Applied Energy, Elsevier, vol. 228(C), pages 108-121.
    16. Sergio Ortega Alba & Mario Manana, 2017. "Characterization and Analysis of Energy Demand Patterns in Airports," Energies, MDPI, vol. 10(1), pages 1-35, January.
    17. Wenninger, Simon & Kaymakci, Can & Wiethe, Christian, 2022. "Explainable long-term building energy consumption prediction using QLattice," Applied Energy, Elsevier, vol. 308(C).
    18. Somu, Nivethitha & M R, Gauthama Raman & Ramamritham, Krithi, 2020. "A hybrid model for building energy consumption forecasting using long short term memory networks," Applied Energy, Elsevier, vol. 261(C).
    19. Tsai, Sang-Bing & Xue, Youzhi & Zhang, Jianyu & Chen, Quan & Liu, Yubin & Zhou, Jie & Dong, Weiwei, 2017. "Models for forecasting growth trends in renewable energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1169-1178.
    20. Yi Zhang & Guanmin Zhang & Aiqun Zhang & Yinhan Jin & Ruirui Ru & Maocheng Tian, 2018. "Frosting Phenomenon and Frost-Free Technology of Outdoor Air Heat Exchanger for an Air-Source Heat Pump System in China: An Analysis and Review," Energies, MDPI, vol. 11(10), pages 1-36, October.

    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:232:y:2018:i:c:p:101-118. 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.