Abstract
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.
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