• Energy Research

Reflectance losses on solar mirrors due to soiling are a significant challenge for Concentrating Solar Power (CSP) plants. Soiling losses can vary significantly from site to site -- with (absolute) reflectance losses varying from fractions of a percentage point up to several percentage points per day (pp/day), a fact that has motivated several studies in soiling predictive modelling. Yet, existing studies have so far neglected the characterization of statistical uncertainty in their parameters and predictions. In this paper, two reflectance loss models are proposed that model uncertainty: an extension of a previously developed physical model and a simplified model. A novel uncertainty characterization enables Maximum Likelihood Estimation techniques for parameter estimation for both models, and permits the estimation of parameter (and prediction) confidence intervals. The models are applied to data from ten soiling campaigns conducted at three Australian sites (Brisbane, Mount Isa, Wodonga). The simplified model produces high-quality predictions of soiling losses on novel data, while the semi-physical model performance is mixed. The statistical distributions of daily losses were estimated for different dust loadings. Under median conditions, the daily soiling losses for Brisbane, Mount Isa, and Wodonga are estimated as $0.53 \pm 0.66$, $0.08 \pm 0.08$, and $0.58 \pm 0.15$ pp/day, respectively. Yet, higher observed dust loadings can drive average losses as high as $2$ pp/day. Overall, the results suggest a relatively simple approach characterizing the statistical distributions of soiling losses using airborne dust measurements and short reflectance monitoring campaigns. 55 pages, 13 figures

This paper presents a novel methodology for characterizing soiling losses through experimental measurements. Soiling predictions were obtained by calibrating a soiling model based on field measurements from a 50 MW modular solar tower project in Mount Isa, Australia. The study found that the mean predicted soiling rate for horizontally fixed mirrors was 0.12 percentage points per day (pp/d) during low dust seasons and 0.22 pp/d during high seasons. Autoregressive time series models were employed to extend two years of onsite meteorological measurements to a 10-year period, enabling the prediction of heliostat-field soiling rates. A fixed-frequency cleaning heuristic was applied to optimise the cleaning resources for various operational policies by balancing direct cleaning resource costs against the expected lost production, which was computed by averaging multiple simulated soiling loss trajectories. Analysis of resource usage showed that the cost of fuel and operator salaries contributed 42 % and 35 % respectively towards the cleaning cost. In addition, stowing heliostats in the horizontal position at night increased daily soiling rates by 114 % and the total cleaning costs by 51 % relative to vertically stowed heliostat-field. Under a simplified night-time-only power production configuration, the oversized solar field effectively charged the thermal storage during the day, despite reduced mirror reflectance due to soiling. These findings suggest that the plant can maintain efficient operation even with a reduced cleaning rate. Finally, it was observed that performing cleaning operations during the day led to a 7 % increase in the total cleaning cost compared to a night-time cleaning policy. This was primarily attributed to the need to park operational heliostats for cleaning.

The accumulation of dust particles on solar collectors can gradually degrade optical performance in solar systems — a phenomenon known as soiling. Additionally, the formation of dew on collectors during early mornings introduces new mechanisms that can impact the soiling process. This study investigates the influence of dew on soiling for uncoated solar mirrors using an artificial soiling station. Several experiments were performed changing testing parameters (e.g. sample tilt, dust amount, dew quantity) and monitoring the outcomes with a range of devices including reflectometers, microscopy, scales, and a luminance camera. The outcomes indicated that condensation becomes relevant for condensation loads above 60 g m −2 and its impact is significantly affected by the tilt angle of the samples. When samples are flat or with low tilt, condensation cycles cause particle dispersion, reducing cleanliness by up to 2 %. On the contrary, for high tilt angles, a honeycomb-like soiling patterns with highly reflective regions appear, enhancing cleanliness restoration by 2.3 %. These findings suggest tilting dewy collectors as a passive self-cleaning strategy.

The use of image analysis has often been suggested as a practical way to monitor the soiling accumulated on the surfaces of solar energy conversion devices. Indeed, the deposited soiling particles can be counted and characterized to calculate the area they cover, and this area can be converted into an energy loss. However, several particle counting methodologies exist and can lead to dissimilar results. This work focuses on the role of thresholding, an essential step where particles are distinguished from a background based on the pixel brightness. Sixteen automatic thresholding methods are assessed using 13 200 micrographs of glass coupons soiled at nine locations globally. In low‐to‐intermediate soiling conditions, the “Triangle” method is found to return the minimum coefficient of variation and a mean deviation closer to zero. On the other hand, methods assuming a bimodal distribution of pixel brightness underestimate the area coverage. In addition, since soiling can be unevenly distributed over a surface, different loss estimations can be returned when the same image analysis process is used on different spots on a sample's surface. For these reasons, image analysis should be repeated at multiple locations on each investigated surface.

Soiling losses and their mitigation via cleaning operations represent important challenges for Solar Tower (ST) plants. Yet soiling losses are not well considered in existing CSP software, likely due to the lack of tools for soiling estimation and cleaning optimization. In this paper, a Python-based heliostat soiling library, called HelioSoil, is introduced which allows for the assessment of heliostats’ soiling state and the optimization of the solar field cleaning schedule to maximize plant profit. The library is freely available on GitHub under a LGPL license, which enables extensions via other Python APIs (e.g. CoPylot) and integration with other CSP plant simulation packages to consider soiling losses. This latter capability is demonstrated in this study through an LCOE assessment and cleaning optimization of a hypothetical Australian ST plant with SolarTherm. Hence, HelioSoil provides the CSP community with a package for soiling assessment and cleaning resource optimization, which can be integrated with available software for high-level, long-term simulations. HelioSoil facilitates the inclusion of soiling and cleaning costs in CSP economics and ultimately aim to de-risk the deployment of ST plants.

The accumulation of soiling on photovoltaic modules and on the mirrors of concentrating solar power systems causes non-negligible energy losses with economic consequences. These challenges can be mitigated, or even prevented, through appropriate actions if the magnitude of soiling is known. Particle counting analysis is a common procedure to characterize soiling, as it can be easily performed on micrographs of glass coupons or solar devices that have been exposed to the environment. Particle counting does not, however, yield invariant results across institutions. The particle size distribution analysis is affected by the operator of the image analysis software and the methodology utilized. The results of a round-robin study are presented in this work to explore and elucidate the uncertainty related to particle counting and its effect on the characterization of the soiling of glass surfaces used in solar energy conversion systems. An international group of soiling experts analysed the same 8 micrographs using the same open-source ImageJ software package. The variation in the particle analyses results were investigated to identify specimen characteristics with the lowest coefficient of variation (CV) and the least uncertainty among the various operators. The mean particle diameter showed the lowest CV among the investigated characteristics, whereas the number of particles exhibited the largest CV. Additional parameters, such as the fractional area coverage by particles and parameters related to the distribution's shape yielded intermediate CV values. These results can provide insights on the magnitude inter-lab variability and uncertainty for optical and microscope-based soiling monitoring and characterization.