IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v221y2024ics0960148123015525.html
   My bibliography  Save this article

Approaching nearly zero energy of PV direct air conditioners by integrating building design, load flexibility and PCM

Author

Listed:
  • Li, Sihui
  • Peng, Jinqing
  • Wang, Meng
  • Wang, Kai
  • Li, Houpei
  • Lu, Chujie

Abstract

The energy matching of PV driven air conditioners is influenced by building load demand and PV generation. Merely increasing energy performance of building or PV capacity separately may improve the energy balance on a large time resolution, the real-time energy mismatching problem is still serious. In this study, a coordinated optimization method of PV capacity, building design, and load flexibility is proposed for improving the real-time energy matching of PVAC system. Then, a methodology integrating data mining method (XG Boost) and parametric simulation was developed to identify the determinant parameters of PV system and building design, exploring feature importance and correlations. The results of XG Boost indicate that the PV capacity, shape factor, and SHGC are the most critical factors. Finally, based on the optimized building design, the PCM layer was applied to improve the real time energy matching. To achieve a goal of 90 % ZEP, the PCM capacity can be decreased by 50.4 % and 62.8 % in Guangzhou and Shanghai in the optimized building. Moreover, the PV capacity can be reduced by 23 % in Guangzhou. The findings of this study provide practical guidance for designing PVAC system coupling with building design and energy storage devices.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:renene:v:221:y:2024:i:c:s0960148123015525
    DOI: 10.1016/j.renene.2023.119637
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148123015525
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2023.119637?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. Ascione, Fabrizio & Bianco, Nicola & De Stasio, Claudio & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2016. "Multi-stage and multi-objective optimization for energy retrofitting a developed hospital reference building: A new approach to assess cost-optimality," Applied Energy, Elsevier, vol. 174(C), pages 37-68.
    2. 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.
    3. Ascione, Fabrizio & Bianco, Nicola & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2019. "A new comprehensive framework for the multi-objective optimization of building energy design: Harlequin," Applied Energy, Elsevier, vol. 241(C), pages 331-361.
    4. Hu, Maomao & Xiao, Fu & Wang, Lingshi, 2017. "Investigation of demand response potentials of residential air conditioners in smart grids using grey-box room thermal model," Applied Energy, Elsevier, vol. 207(C), pages 324-335.
    5. Elaouzy, Y. & El Fadar, A., 2022. "Energy, economic and environmental benefits of integrating passive design strategies into buildings: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    6. Neale, John & Shamsi, Mohammad Haris & Mangina, Eleni & Finn, Donal & O’Donnell, James, 2022. "Accurate identification of influential building parameters through an integration of global sensitivity and feature selection techniques," Applied Energy, Elsevier, vol. 315(C).
    7. Yin, Rongxin & Kara, Emre C. & Li, Yaping & DeForest, Nicholas & Wang, Ke & Yong, Taiyou & Stadler, Michael, 2016. "Quantifying flexibility of commercial and residential loads for demand response using setpoint changes," Applied Energy, Elsevier, vol. 177(C), pages 149-164.
    8. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    9. Li, Hangxin & Wang, Shengwei, 2019. "Coordinated optimal design of zero/low energy buildings and their energy systems based on multi-stage design optimization," Energy, Elsevier, vol. 189(C).
    10. Li, Sihui & Peng, Jinqing & Li, Houpei & Zou, Bin & Song, Jiaming & Ma, Tao & Ji, Jie, 2022. "Zero energy potential of PV direct-driven air conditioners coupled with phase change materials and load flexibility," Renewable Energy, Elsevier, vol. 200(C), pages 419-432.
    11. 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).
    12. Abdul Mujeebu, Muhammad & Bano, Farheen, 2022. "Integration of passive energy conservation measures in a detached residential building design in warm humid climate," Energy, Elsevier, vol. 255(C).
    13. Javed, Muhammad Shahzad & Jurasz, Jakub & McPherson, Madeleine & Dai, Yanjun & Ma, Tao, 2022. "Quantitative evaluation of renewable-energy-based remote microgrids: curtailment, load shifting, and reliability," Renewable and Sustainable Energy Reviews, Elsevier, vol. 164(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. Hanze Yu & Wei Yang & Qiyuan Li & Jie Li, 2022. "Optimizing Buildings’ Life Cycle Performance While Allowing Diversity in the Early Design Stage," Sustainability, MDPI, vol. 14(14), pages 1-21, July.
    2. Hasim Altan & Bertug Ozarisoy, 2022. "An Analysis of the Development of Modular Building Design Elements to Improve Thermal Performance of a Representative High Rise Residential Estate in the Coastline City of Famagusta, Cyprus," Sustainability, MDPI, vol. 14(7), pages 1-50, March.
    3. Abdo Abdullah Ahmed Gassar & Choongwan Koo & Tae Wan Kim & Seung Hyun Cha, 2021. "Performance Optimization Studies on Heating, Cooling and Lighting Energy Systems of Buildings during the Design Stage: A Review," Sustainability, MDPI, vol. 13(17), pages 1-47, September.
    4. Wang, Huilong & Wang, Shengwei, 2021. "A hierarchical optimal control strategy for continuous demand response of building HVAC systems to provide frequency regulation service to smart power grids," Energy, Elsevier, vol. 230(C).
    5. Zhan, Jin & He, Wenjing & Huang, Jianxiang, 2024. "Comfort, carbon emissions, and cost of building envelope and photovoltaic arrangement optimization through a two-stage model," Applied Energy, Elsevier, vol. 356(C).
    6. 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).
    7. Ciardiello, Adriana & Rosso, Federica & Dell'Olmo, Jacopo & Ciancio, Virgilio & Ferrero, Marco & Salata, Ferdinando, 2020. "Multi-objective approach to the optimization of shape and envelope in building energy design," Applied Energy, Elsevier, vol. 280(C).
    8. Hessam Taherian & Robert W. Peters, 2023. "Advanced Active and Passive Methods in Residential Energy Efficiency," Energies, MDPI, vol. 16(9), pages 1-19, May.
    9. Bampoulas, Adamantios & Pallonetto, Fabiano & Mangina, Eleni & Finn, Donal P., 2022. "An ensemble learning-based framework for assessing the energy flexibility of residential buildings with multicomponent energy systems," Applied Energy, Elsevier, vol. 315(C).
    10. Grillone, Benedetto & Danov, Stoyan & Sumper, Andreas & Cipriano, Jordi & Mor, Gerard, 2020. "A review of deterministic and data-driven methods to quantify energy efficiency savings and to predict retrofitting scenarios in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    11. Lu, Chujie & Li, Sihui & Reddy Penaka, Santhan & Olofsson, Thomas, 2023. "Automated machine learning-based framework of heating and cooling load prediction for quick residential building design," Energy, Elsevier, vol. 274(C).
    12. Chi, Fang'ai & Xu, Ying, 2022. "Building performance optimization for university dormitory through integration of digital gene map into multi-objective genetic algorithm," Applied Energy, Elsevier, vol. 307(C).
    13. Ali Sadollah & Mohammad Nasir & Zong Woo Geem, 2020. "Sustainability and Optimization: From Conceptual Fundamentals to Applications," Sustainability, MDPI, vol. 12(5), pages 1-34, March.
    14. Kathirgamanathan, Anjukan & De Rosa, Mattia & Mangina, Eleni & Finn, Donal P., 2021. "Data-driven predictive control for unlocking building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    15. Wagner, Lukas Peter & Reinpold, Lasse Matthias & Kilthau, Maximilian & Fay, Alexander, 2023. "A systematic review of modeling approaches for flexible energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    16. Hu, Maomao & Xiao, Fu & Wang, Shengwei, 2021. "Neighborhood-level coordination and negotiation techniques for managing demand-side flexibility in residential microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    17. 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).
    18. Zhang, Zhihui & Jing, Rui & Lin, Jian & Wang, Xiaonan & van Dam, Koen H. & Wang, Meng & Meng, Chao & Xie, Shan & Zhao, Yingru, 2020. "Combining agent-based residential demand modeling with design optimization for integrated energy systems planning and operation," Applied Energy, Elsevier, vol. 263(C).
    19. Li, Kangping & Li, Zhenghui & Huang, Chunyi & Ai, Qian, 2024. "Online transfer learning-based residential demand response potential forecasting for load aggregator," Applied Energy, Elsevier, vol. 358(C).
    20. Luo, Na & Langevin, Jared & Chandra-Putra, Handi & Lee, Sang Hoon, 2022. "Quantifying the effect of multiple load flexibility strategies on commercial building electricity demand and services via surrogate modeling," Applied Energy, Elsevier, vol. 309(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:renene:v:221:y:2024:i:c:s0960148123015525. 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.journals.elsevier.com/renewable-energy .

    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.