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Zero energy potential of photovoltaic direct-driven air conditioners with considering the load flexibility of air conditioners

Author

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  • Li, Sihui
  • Peng, Jinqing
  • Zou, Bin
  • Li, Bojia
  • Lu, Chujie
  • Cao, Jingyu
  • Luo, Yimo
  • Ma, Tao

Abstract

The real-time energy matching between building load and PV generation is low in actual applications of photovoltaic direct-driven air conditioners (PVACs). The indoor thermal comfort temperature range (TCTR) can enhance the load flexibility of PVACs to improve the real-time zero energy probability. Therefore, a real-time zero-energy potential evaluation method with the adoption of TCTR for PVACs was proposed. The indoor temperatures are mainly determined by real-time PV generation, which minimizes the power taken from the grid and batteries. The indoor temperature, conditioned by PVACs under varying operating conditions, is predicted using machine learning models at a one-minute time resolution. The real-time energy matching performances of PVACs are evaluated by key indicators, including zero-energy probability, real-time zero-energy probability, complete overcooling rate, and complete overheating rate, for different climatic regions. With a fixed indoor setting temperature in summer, the real-time zero-energy probabilities in Shanghai, Guangzhou, and Beijing are 1.89%, 2.45%, and 2.11%, respectively. While adopting the TCTR with the similar PV capacity, the corresponding values in Shanghai, Guangzhou, and Beijing reach 53.13%, 49.48%, and 87.11%, respectively. In addition, the real-time zero-energy points frequently appear when large cooling demands are needed. Finally, an optimization of PV capacity is conducted. The optimal PV capacity was found to be at the point where seasonal PV generation is 1.3 times the dominant seasonal energy consumption. PVACs with TCTR can utilize the PV generation and load flexibility to the greatest extent and the evaluation method for PVACs is beneficial for designing a more practical and economical system.

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  • 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).
    • Handle: RePEc:eee:appene:v:304:y:2021:i:c:s0306261921011508
    DOI: 10.1016/j.apenergy.2021.117821
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    3. 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).
    4. 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).
    5. Liu, Jia & Zhou, Yuekuan & Yang, Hongxing & Wu, Huijun, 2022. "Uncertainty energy planning of net-zero energy communities with peer-to-peer energy trading and green vehicle storage considering climate changes by 2050 with machine learning methods," Applied Energy, Elsevier, vol. 321(C).
    6. 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.
    7. 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).
    8. 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).
    9. Liu, Lu & Shao, Shuangquan, 2023. "Recent advances of low-temperature cascade phase change energy storage technology: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 186(C).
    10. Wang, Kai & Peng, Jinqing & Li, Sihui & Li, Houpei & Zou, Bin & Ma, Tao & Ji, Jie, 2024. "Compressor speed control for optimizing energy matching of PV-driven AC systems during the cooling season," Energy, Elsevier, vol. 298(C).

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