Performance evaluation of a dynamic tracking photovoltaic greenhouse system

Agrivoltaic (APV) systems integrate energy production with crop cultivation, offering a promising solution to land-use conflicts and enhancing resource utilisation efficiency. However, traditional APV designs often struggle to effectively coordinate the dynamic lighting needs of crops with the power generation efficiency of photovoltaic (PV) systems. To address this challenge, this study developed a dynamic tracking photovoltaic greenhouse (TPVG) system, which served as the basis for a real-time model predicting greenhouse shadows by integrating solar geometry and ray-tracing algorithms. In addition, indicators of daily irradiance variation coefficient, crop yield, land equivalent ratio, and energy consumption for TPVG are compared with those of a non-tracking photovoltaic greenhouse (NTPVG) via experiments and simulations. The results show that the daily average coefficient of variation (CV) of indoor irradiance decreased by 8–12%, while power generation increased by 10.11–13.76%. In addition, compared to NTPVG, strawberry yield increases by 20.75%, inter-crop retention rate improves by 8.1%, and the land equivalent ratio (LER) ultimately reaches 1.77. Although the initial investment for TPVG is 29.46% higher than for NTPVG, the levelized cost of strawberries (LCOS) is 20.6% lower, and the payback period (PBP) is 6.88 years, 0.52 years longer than for NTPVG. Moreover, TPVG demonstrates superior self-sufficiency (SS) rates across multiple latitudinal regions, with the annual average SS rate in mid- and low-latitude regions remaining above 0.94. Therefore, the research provides a validated and efficient design framework for the APV field, offering actionable insights for optimising the synergistic production of food and energy globally.