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Optimal sizing and techno-economic analysis of the hybrid PV-battery-cooling storage system for commercial buildings in China

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  • Chen, Qi
  • Kuang, Zhonghong
  • Liu, Xiaohua
  • Zhang, Tao

Abstract

Energy systems for flexibility in buildings are hybrid, primarily including rooftop photovoltaics (PV), cooling storage, and battery. Considering their techno-economic patterns, this research establishes an optimization model to determine the optimal technology portfolio and financial advantages of PV-battery-cooling storage systems for commercial buildings in China. The analysis of all cases indicates that cooling storage outperforms batteries in economic benefits, suggesting the prioritization of cooling storage installation. Once the optimal cooling storage rate is exceeded, it is advisable to proceed with batteries. Meanwhile, PV integration significantly enhances the system efficiency and promotes battery utilization. For example, a 40% PV penetration combined with a 0.006 $/(a·kWhe) energy storage investment results in an impressive 27.3% cost reduction in a Beijing mall, while the optimal cooling storage rate decreases from 55% to 40%. Furthermore, the study emphasizes the impact of tariff patterns and electricity demand on the economic feasibility of hybrid energy systems. The museum's substantial annual cooling requirements and nighttime loads make cooling storage favorable, with PV less suitable than the mall and office. Notably, cities like Beijing, Shanghai, Chongqing, and Guangzhou exhibit considerable peak-to-valley tariff differences, yielding higher economic benefits ranging from 23% to 27%. Finally, as battery costs decline and electricity price becomes more volatile, the battery would gradually replace cooling storage, especially when battery cost drops from 150 $/kWh to 70 $/kWh.

Suggested Citation

  • Chen, Qi & Kuang, Zhonghong & Liu, Xiaohua & Zhang, Tao, 2024. "Optimal sizing and techno-economic analysis of the hybrid PV-battery-cooling storage system for commercial buildings in China," Applied Energy, Elsevier, vol. 355(C).
    • Handle: RePEc:eee:appene:v:355:y:2024:i:c:s0306261923015957
    DOI: 10.1016/j.apenergy.2023.122231
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    4. Chen, Qi & Kuang, Zhonghong & Liu, Xiaohua & Zhang, Tao, 2024. "Application-oriented assessment of grid-connected PV-battery system with deep reinforcement learning in buildings considering electricity price dynamics," Applied Energy, Elsevier, vol. 364(C).

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