IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v113y2019ic31.html
   My bibliography  Save this article

An adaptive PID control method to improve the power tracking performance of solar photovoltaic air-conditioning systems

Author

Listed:
  • Zhao, B.Y.
  • Zhao, Z.G.
  • Li, Y.
  • Wang, R.Z.
  • Taylor, R.A.

Abstract

In order to increase the utilization of solar energy to lower the effect of photovoltaic power output fluctuations on power grids, an adaptive PID control method to improve the power tracking performance of solar photovoltaic air-conditioners is proposed in this paper. In this method, a dynamic temperature set point of the indoor zone is generated at each control time step based on the difference between the air-conditioning load and photovoltaic generation. The theoretical analysis shows that the proposed control is essentially a feedforward feedback control where the air-conditioning system can predictably adjust the compressor frequency based on the knowledge of how the solar radiation affects the cooling load. A case study in Shanghai shows that the proposed control method is able to significantly improve the power tracking performance of the photovoltaic air-conditioner, where the Solar Fraction (SF) and the Self-Consumption Ratio (SCR) are improved from 82.74% to 88.11% and from 70.12% to 74.42% respectively, without violating the indoor thermal comfort, where Mean Absolute Error (MAE) from the temperature set point is decreased from 0.197 to 0.168. The tuning of the power difference factor ko will lead to monotonic changes of SF &SCR and a minimum MAE. When ko is well tuned, the proposed control can realize desirable system improvements of both the power tracking ability and the indoor temperature control accuracy. The idea of this paper could be easily modified to benefit other solar cooling applications and conventional air-conditioners.

Suggested Citation

  • Zhao, B.Y. & Zhao, Z.G. & Li, Y. & Wang, R.Z. & Taylor, R.A., 2019. "An adaptive PID control method to improve the power tracking performance of solar photovoltaic air-conditioning systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    • Handle: RePEc:eee:rensus:v:113:y:2019:i:c:31
    DOI: 10.1016/j.rser.2019.109250
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032119304502
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2019.109250?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. Reynolds, Jonathan & Rezgui, Yacine & Kwan, Alan & Piriou, Solène, 2018. "A zone-level, building energy optimisation combining an artificial neural network, a genetic algorithm, and model predictive control," Energy, Elsevier, vol. 151(C), pages 729-739.
    2. Nägele, Florian & Kasper, Thomas & Girod, Bastien, 2017. "Turning up the heat on obsolete thermostats: A simulation-based comparison of intelligent control approaches for residential heating systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 1254-1268.
    3. Rehman, Shafiqur & Mohandes, Mohamed, 2008. "Artificial neural network estimation of global solar radiation using air temperature and relative humidity," Energy Policy, Elsevier, vol. 36(2), pages 571-576, February.
    4. Fiorentini, Massimo & Wall, Josh & Ma, Zhenjun & Braslavsky, Julio H. & Cooper, Paul, 2017. "Hybrid model predictive control of a residential HVAC system with on-site thermal energy generation and storage," Applied Energy, Elsevier, vol. 187(C), pages 465-479.
    5. Chua, K.J. & Chou, S.K. & Yang, W.M. & Yan, J., 2013. "Achieving better energy-efficient air conditioning – A review of technologies and strategies," Applied Energy, Elsevier, vol. 104(C), pages 87-104.
    6. Fadzli Haniff, Mohamad & Selamat, Hazlina & Yusof, Rubiyah & Buyamin, Salinda & Sham Ismail, Fatimah, 2013. "Review of HVAC scheduling techniques for buildings towards energy-efficient and cost-effective operations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 94-103.
    7. Papadopoulos, A. M. & Oxizidis, S. & Kyriakis, N., 2003. "Perspectives of solar cooling in view of the developments in the air-conditioning sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 7(5), pages 419-438, October.
    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. Li, Sihui & Peng, Jinqing & Zou, Bin & Li, Bojia & Lu, Chujie & Cao, Jingyu & Luo, Yimo & Ma, Tao, 2021. "Zero energy potential of photovoltaic direct-driven air conditioners with considering the load flexibility of air conditioners," Applied Energy, Elsevier, vol. 304(C).
    2. Li, Houpei & Li, Jun & Li, Sihui & Peng, Jinqing & Ji, Jie & Yan, Jinyue, 2023. "Matching characteristics and AC performance of the photovoltaic-driven air conditioning system," Energy, Elsevier, vol. 264(C).
    3. 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).
    4. Li, Sihui & Peng, Jinqing & Wang, Meng & Wang, Kai & Li, Houpei & Lu, Chujie, 2024. "Approaching nearly zero energy of PV direct air conditioners by integrating building design, load flexibility and PCM," Renewable Energy, Elsevier, vol. 221(C).
    5. Homod, Raad Z. & Gaeid, Khalaf S. & Dawood, Suroor M. & Hatami, Alireza & Sahari, Khairul S., 2020. "Evaluation of energy-saving potential for optimal time response of HVAC control system in smart buildings," Applied Energy, Elsevier, vol. 271(C).
    6. Edoardo Di Mattia & Agostino Gambarotta & Emanuela Marzi & Mirko Morini & Costanza Saletti, 2022. "Predictive Controller for Refrigeration Systems Aimed to Electrical Load Shifting and Energy Storage," Energies, MDPI, vol. 15(19), pages 1-22, September.
    7. He, Yecong & Sun, Jie & Deng, Qi & Zhang, Xiaofeng & Liu, Huaican & Wen, Ke & Zhou, Jifei, 2023. "Teaching building towards carbon neutrality: Power matching and economy of source-grid-load-storage system," Renewable Energy, Elsevier, vol. 218(C).
    8. Jahangir Hossain & Aida. F. A. Kadir & Ainain. N. Hanafi & Hussain Shareef & Tamer Khatib & Kyairul. A. Baharin & Mohamad. F. Sulaima, 2023. "A Review on Optimal Energy Management in Commercial Buildings," Energies, MDPI, vol. 16(4), pages 1-40, February.
    9. Hua, Pengmin & Wang, Haichao & Xie, Zichan & Lahdelma, Risto, 2024. "Multi-criteria evaluation of novel multi-objective model predictive control method for indoor thermal comfort," Energy, Elsevier, vol. 289(C).
    10. Yin, Linfei & Li, Yu, 2022. "Hybrid multi-agent emotional deep Q network for generation control of multi-area integrated energy systems," 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. Yang, Shiyu & Wan, Man Pun & Ng, Bing Feng & Dubey, Swapnil & Henze, Gregor P. & Chen, Wanyu & Baskaran, Krishnamoorthy, 2020. "Experimental study of model predictive control for an air-conditioning system with dedicated outdoor air system," Applied Energy, Elsevier, vol. 257(C).
    2. Li, Sihui & Peng, Jinqing & Zou, Bin & Li, Bojia & Lu, Chujie & Cao, Jingyu & Luo, Yimo & Ma, Tao, 2021. "Zero energy potential of photovoltaic direct-driven air conditioners with considering the load flexibility of air conditioners," Applied Energy, Elsevier, vol. 304(C).
    3. Gohar Gholamibozanjani & Mohammed Farid, 2021. "A Critical Review on the Control Strategies Applied to PCM-Enhanced Buildings," Energies, MDPI, vol. 14(7), pages 1-39, March.
    4. Yang, Shiyu & Wan, Man Pun, 2022. "Machine-learning-based model predictive control with instantaneous linearization – A case study on an air-conditioning and mechanical ventilation system," Applied Energy, Elsevier, vol. 306(PB).
    5. Abdul Mujeebu, Muhammad & Alshamrani, Othman Subhi, 2016. "Prospects of energy conservation and management in buildings – The Saudi Arabian scenario versus global trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1647-1663.
    6. Abdel-Salam, Mohamed R.H. & Fauchoux, Melanie & Ge, Gaoming & Besant, Robert W. & Simonson, Carey J., 2014. "Expected energy and economic benefits, and environmental impacts for liquid-to-air membrane energy exchangers (LAMEEs) in HVAC systems: A review," Applied Energy, Elsevier, vol. 127(C), pages 202-218.
    7. Finck, Christian & Li, Rongling & Zeiler, Wim, 2019. "Economic model predictive control for demand flexibility of a residential building," Energy, Elsevier, vol. 176(C), pages 365-379.
    8. Yang, Shiyu & Wan, Man Pun & Chen, Wanyu & Ng, Bing Feng & Dubey, Swapnil, 2020. "Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization," Applied Energy, Elsevier, vol. 271(C).
    9. Germán Ramos Ruiz & Eva Lucas Segarra & Carlos Fernández Bandera, 2018. "Model Predictive Control Optimization via Genetic Algorithm Using a Detailed Building Energy Model," Energies, MDPI, vol. 12(1), pages 1-18, December.
    10. Makade, Rahul G. & Chakrabarti, Siddharth & Jamil, Basharat & Sakhale, C.N., 2020. "Estimation of global solar radiation for the tropical wet climatic region of India: A theory of experimentation approach," Renewable Energy, Elsevier, vol. 146(C), pages 2044-2059.
    11. 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.
    12. Fei Wang & Zengqiang Mi & Shi Su & Hongshan Zhao, 2012. "Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters," Energies, MDPI, vol. 5(5), pages 1-16, May.
    13. 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).
    14. Nkwetta, Dan Nchelatebe & Sandercock, Jim, 2016. "A state-of-the-art review of solar air-conditioning systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1351-1366.
    15. Niamsuwan, Sathit & Kittisupakorn, Paisan & Suwatthikul, Ajaree, 2015. "Enhancement of energy efficiency in a paint curing oven via CFD approach: Case study in an air-conditioning plant," Applied Energy, Elsevier, vol. 156(C), pages 465-477.
    16. Svetlana Ratner & Yuri Chepurko & Larisa Drobyshecskaya & Anna Petrovskaya, 2018. "Management of Energy Enterprises: Energy-efficiency Approach in Solar Collectors Industry: The Case of Russia," International Journal of Energy Economics and Policy, Econjournals, vol. 8(4), pages 237-243.
    17. Yang, Xiaohu & Yu, Jiabang & Guo, Zengxu & Jin, Liwen & He, Ya-Ling, 2019. "Role of porous metal foam on the heat transfer enhancement for a thermal energy storage tube," Applied Energy, Elsevier, vol. 239(C), pages 142-156.
    18. Wang, Wenqing & Kolditz, Olaf & Nagel, Thomas, 2017. "Parallel finite element modelling of multi-physical processes in thermochemical energy storage devices," Applied Energy, Elsevier, vol. 185(P2), pages 1954-1964.
    19. 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).
    20. Lork, Clement & Li, Wen-Tai & Qin, Yan & Zhou, Yuren & Yuen, Chau & Tushar, Wayes & Saha, Tapan K., 2020. "An uncertainty-aware deep reinforcement learning framework for residential air conditioning energy management," Applied Energy, Elsevier, vol. 276(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:rensus:v:113:y:2019:i:c:31. 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/600126/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.