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A multi-factor spatio-temporal correlation analysis method for PV development potential estimation

Author

Listed:
  • Xiang, Yue
  • Tang, Qingwei
  • Xu, Weiting
  • Hu, Shuai
  • Zhao, Pengfei
  • Guo, Jingrong
  • Liu, Junyong

Abstract

The impact of wind speed on PV power generation is influenced by the monsoon and the complex topography and environmental factors found in mountainous areas. This complexity can reduce the correlation between PV power and irradiance. To better understand the impact of wind speed, we conducted an analysis in various topographical regions of the Hengduan Mountain in China. Our findings show that high wind speeds can enhance PV utilization, particularly in plateau and hill regions. This is due to the blocking and lifting effect of the north-south oriented mountains on the east-west wind, as well as the channeling effect on the north-south wind. To improve the efficiency of utilizing solar energy, it is important to consider the spatial distribution of wind speed when developing PV. Correlation analysis of multi-factors on a spatial and temporal scale can also offer insight. Furthermore, planning the layout of PV systems with meteorological resources in mind can enhance PV utilization in complex mountainous terrain.

Suggested Citation

  • Xiang, Yue & Tang, Qingwei & Xu, Weiting & Hu, Shuai & Zhao, Pengfei & Guo, Jingrong & Liu, Junyong, 2024. "A multi-factor spatio-temporal correlation analysis method for PV development potential estimation," Renewable Energy, Elsevier, vol. 223(C).
    • Handle: RePEc:eee:renene:v:223:y:2024:i:c:s0960148124000272
    DOI: 10.1016/j.renene.2024.119962
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    Cited by:

    1. Wenjie Pan & Jun Han & Chao Cai & Haofei Chen & Hong Liu & Zhengyang Xu, 2025. "Holistic Hosting Capacity Enhancement Through Sensitivity-Driven Flexibility Deployment and Uncertainty-Aware Optimization in Modern Distribution Networks," Energies, MDPI, vol. 18(3), pages 1-30, February.
    2. Dawei Wang & Shuang Zeng & Liyong Wang & Baoqun Zhang & Cheng Gong & Zhengguo Piao & Fuming Zheng, 2025. "Quantum-Enhanced Predictive Degradation Pathway Optimization for PV Storage Systems: A Hybrid Quantum–Classical Approach for Maximizing Longevity and Efficiency," Energies, MDPI, vol. 18(14), pages 1-27, July.
    3. Li, Mengdi & Han, Chuanfeng & Meng, Lingpeng & Liu, Pihui & Shao, Zhiguo, 2025. "Spatiotemporal dynamics and driving factors of the coupling coordination between solar photovoltaic efficiency and installed capacity in China (2015–2023)," Renewable Energy, Elsevier, vol. 247(C).
    4. Zhiming Gu & Tingzhe Pan & Bo Li & Xin Jin & Yaohua Liao & Junhao Feng & Shi Su & Xiaoxin Liu, 2024. "Enhancing Photovoltaic Grid Integration through Generative Adversarial Network-Enhanced Robust Optimization," Energies, MDPI, vol. 17(19), pages 1-15, September.
    5. Liu, Jiang & Peng, Changhai & Zhang, Junxue, 2025. "Understanding the relationship between rural morphology and photovoltaic (PV) potential in traditional and non-traditional building clusters using shapley additive exPlanations (SHAP) values," Applied Energy, Elsevier, vol. 380(C).
    6. Piotr F. Borowski, 2025. "Renewable Energy Sources Towards a Zero-Emission Economy," Energies, MDPI, vol. 18(20), pages 1-5, October.
    7. Qi, Dong & Kang, Jia & Liu, Jinshi & Chen, Shijun & Chen, Wenlong & Xie, Guo & Wang, Wenquan, 2026. "Layout optimization of mountain PV involving hydro-PV hybrid system and site selection," Renewable Energy, Elsevier, vol. 256(PB).
    8. Dawei Wang & Liyong Wang & Baoqun Zhang & Chang Liu & Yongliang Zhao & Shanna Luo & Jun Feng, 2025. "Quantum State Estimation for Real-Time Battery Health Monitoring in Photovoltaic Storage Systems," Energies, MDPI, vol. 18(11), pages 1-23, May.
    9. Chao Han & Yun Zhu & Xing Zhou & Xuejie Wang, 2025. "Stochastic Optimization Scheduling Method for Mine Electricity–Heat Energy Systems Considering Power-to-Gas and Conditional Value-at-Risk," Energies, MDPI, vol. 18(15), pages 1-22, August.
    10. Wang, Haibing & Wang, Fengxia & Han, Dong & Sun, Weiqing, 2024. "Multi-time scale optimal configuration of user-side energy storage considering demand perception," Renewable Energy, Elsevier, vol. 237(PA).

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