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