Stochastic Assessment of Predictions and Uncertainties for Reflectance Losses Based on Experimental Data for Three Australian Sites

Abstract

A stochastic reflectance loss model is applied to extended datasets of experimental data collected at three sites in Australia, each representative of a different environment: urban, rural, and remote outback. The three sites are analysed in terms of TSP (Total Suspended Particles) or PM10 (Particulate Matter below 10µm in diameter), depending on the available dust sampler deployed at each location. Assessment of seasonal and daily patterns are also performed for further understanding of local phenomena likely to affect soiling in the area. Airborne dust concentration data are exploited to provide density distributions of expected daily reflectance losses. These mean losses for the three sites are 0.31 pp/day, 0.72 pp/day, and 0.77pp/day for the outback, rural, and urban location, respectively. These values and their distributions are paramount for evaluation of a prospective plant profitability, planning for operating plants cleaning scheduling, and assessment of a prospective CSP location at site selection phase. The developed methodology is capable of providing highly valuable information based on easily measurable airborne dust concentration data only, hence becoming a critical step for de-risking CSP plants financing and deployment.

Keywords

• Reflectance
• Predictions
• Stochastic
• Uncertainties
• Experiments