Bayesian algorithm to reduce LCOE of bifacial projects

German researchers have proposed a new model, based on a range of parameters, to influence the LCOE of bifacial PV plants, with a focus on components, land costs, inclination angles, albedo, and weather conditions.

Researchers at Germany's Helmholtz-Zentrum Berlin (HZB) have developed an illumination model for the deployment of bifacial solar panels, which they claim can help to reduce the levelized cost of energy (LCOE) in large-scale PV projects.

The proposed model is based on the Bayesian optimization method. The approach is based on Bayes' theorem, which is a simple mathematical formula that can be used to calculate conditional probabilities and update existing beliefs based on the arrival of new, relevant evidence.

The scientists said the model covers a range of parameters that influence the LCOE of bifacial plants, including PV components, land costs, the distance between modules, inclination angles, albedo, and weather conditions.

The scientists applied the algorithm to calculate the annual power yield of four solar plants at three locations near Seattle (warm-temperate Mediterranean climate), Dallas (humid subtropical climate), in the Mojave Desert (hot desert climate), and Havana, Cuba (tropical climate).

“We calculate optimal module inclination angles and module distances yielding minimal LCOE for various module to land cost ratios,” the researchers said. “We find that our calculated optima strongly depend on both the module to land cost ratio and the geographical location.”

The German team concluded that existing rule-of-thumb estimates for optimal module distance and tilt angles should be reconsidered.

“These rules do not consider the economic trade-off between land costs and energy yield or typical weather patterns (e.g., foggy winters) that vary for different locations,” they argued.

They described the algorithm in “Minimizing the levelized cost of electricity for bifacial solar panel arrays using Bayesian optimization,” which was recently published in Sustainable Energy & Fuels.

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