Slight terrain unevenness leads to suboptimal backtracking, reducing tracker tilt and lowering irradiance capture
Real plants show 10-20% lower tracking gain, translating to a 0.8%-2% annual irradiance loss
These losses remain hidden from standard metrics like PR, leading to overestimation in simulations
Utility-scale PV plants are designed assuming near-ideal conditions. In practice, even small deviations affect performance. Terrain is one such factor. While often treated as flat in simulations, sites are slightly uneven in reality. This becomes relevant for single-axis trackers, especially during the backtracking operation.
A paper published in Renewable Energy by researchers from Instituto de Energía Solar (IES-UPM) examines this gap. Based on data from more than 7,000 trackers across 7 PV plants, the authors show that tracker performance under real-world conditions differs from simulation results. The study showed that tracking irradiation gain (TIG) is 10% to 20% lower than expected, resulting in 1% to 3% lower annual irradiance collection.
Backtracking is designed to avoid row-to-row shading while maintaining irradiance capture. Under flat terrain, this works as expected. However, even small height differences between rows change this behavior. If the ideal backtracking angle is applied, some rows begin to shade others. To prevent this, tracker controllers reduce the tilt to a greater extent than required. This avoids shading, but also reduces direct irradiance.
In operating plants, this effect can be observed as light bands between rows. These bands indicate irradiance that is not captured by the modules. The paper identifies this as a direct result of overcorrection in tracker angles due to terrain unevenness.
The authors define this effect as suboptimal backtracking losses, which are not visible in standard performance metrics. The performance ratio (PR) does not capture these losses because both expected and measured energy are calculated using the same in-plane irradiance. As a result, the loss remains embedded in the system. The study estimates that this effect alone can lead to up to 2% loss in irradiance capture, which becomes significant at the utility scale.
To explain the deviation, the paper proposes 3 approaches to model real tracker behavior. These include direct adjustment of the tracker angle, modification of the effective row spacing, and representation of terrain as an equivalent slope. All 3 approaches modify the target angle during backtracking. When implemented in simulations, they show better agreement with measured plant data. The models are implemented in the SISIFO simulation tool to assess performance.
The impact was observed to be consistent across the PV plants analyzed. Tracking irradiation gain is reduced by 4% to 10%, while annual irradiance losses range between 0.8% and 2%. In some cases, additional operational factors such as misalignment or communication delays increase total tracking-related losses beyond 5%.
The study also shows that the most common correction method in practice is to assume a smaller effective distance between rows. This approach is easier to implement, as it requires only changes in input parameters rather than modifications to tracker control algorithms.
The findings point to a structural gap in current simulation practices. Most tools assume flat terrain, leading to systematic overestimation of energy yield. Even small deviations matter at the utility scale, as they directly impact revenue and project evaluation. Since these losses occur during normal operation, they are not mitigated by curtailment or clipping.
The analysis suggests that terrain-induced tracking deviations are not site-specific but inherent to real PV installations. Incorporating unevenness-aware modeling can improve the accuracy of yield predictions and reduce the gap between simulated and actual performance.
The full paper, titled Modelling energy losses arising from overridden backtracking in utility-scale photovoltaic plants on slightly undulating terrain, provides a detailed framework for quantifying these effects and improving yield prediction.
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