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The components exposed to the highest temperature and heat fluxes in Solar Tower plants are the tubes of the panels composing the central receiver. Their structural integrity is weakened due to fatigue and creep phenomena, likely worsened by a corrosive environment, and their solar power harvesting performance suffers from diminished absorptance of their coating. This paper describes a 2D dynamic thermal model of the tubes of a central receiver, developed following an Object-Oriented modelling paradigm, and applies the model to assess the impact of degradation phenomena such as corrosion, coating deterioration, and soiling of heliostats. The model is verified against steady-state data available in literature both on overall receiver metal maximum temperature, HTF outlet temperature and absorbed heat, and on detailed circumferential analysis of the metal external temperature for a specific tube. The dynamic behavior of the receiver is also assessed, with focus on the impact of cloud events and the induced thermal transients.

The reduction of energy production due to heliostat soiling causes generation losses and therefore reduces the contribution and competitiveness of CSP. The optimal cleaning plan for CSP plants must strike the proper balance between the cost for cleaning activities and revenue losses due to a reduced power production. However, uncertainty in future soiling rates, weather conditions, and electricity sale prices (e.g. due to electricity markets, uncertainty in future purchasing agreements) significantly complicate the cleaning optimization. To address these challenges, a bi-objective simulation- optimization approach is developed to optimize the cleaning policy to simultaneously minimize cleaning costs and expected generation losses in the presence of stochastic weather, dust. The outcome of the proposed approach is the optimal number of cleaning trucks and periodic cleaning schedule for non-dominating solutions (Pareto front), which may be used to study the sensitivity of the cleaning schedule to variations in electricity prices. The approach is applied to a case study for a CSP plant located in Woomera, South Australia. The results show that for electricity sale prices between 40-100 AUD/MWh, the optimal number of trucks is 3 and the total cleaning costs are sensitive to the number of trucks purchased, but not to the cleaning frequency. However, as electricity prices increase above 300 AUD/MWh, the total costs become quite sensitive to the cleaning frequency. Finally, a comparison of the simulation-optimization approach and a (simpler) deterministic optimization indicates that the latter approach is suitable for most practical electricity prices.

This thesis investigates the detrimental effect of degradation phenomena in key equipment of solar tower plants, paving the path for their optimal maintenance. Physical models were developed to assess the thermal behaviour of central receivers, accurately estimate the soiling of heliostats, and mitigate the impact of optical efficiency losses in the solar field through optimized cleaning strategies. A hypothetical plant located in Woomera (South Australia) was exploited as a case study to evaluate the effectiveness of the optimizations, while successful synergies between thermal and soiling models proved their suitability for the development of comprehensive degradation prediction tools.

The most solar-specific components in a Concentrated Solar Power (CSP) plant are the solar field and the receiver. The latter is exposed to extreme working conditions as it withstands high heat fluxes and temperatures in a corrosive environment during usual and transient conditions. It is then of paramount importance to assess the thermal behavior of such a fundamental component of the plant. The object-oriented modelling paradigm enables precise dynamic analysis of physical systems through the solution of sets of equations derived following an acausal approach. This guarantees modularity and description of interactions through physical connection equations with no causality specification. The application of the object-oriented modelling technique on the external receiver of a CSP tower plant is described in detail in this paper, proving its potentialities and modelling capabilities. The model is first verified against data available in literature and then applied for the analysis of dynamic operations, including typical issues of CSP plants as the impact of clouds and heliostats soiling.

The optical efficiency of the solar field heliostats of a Concentrating Solar Power (CSP) tower plant is a fundamental parameter to assess the productivity of the plant. The optical efficiency is mainly affected by the position of the sun in the sky during the year, and by the cleanliness level of the heliostats, which is related to their reflectance. The reflectance is continuously diminished by the deposition of dust on the heliostat surface (that is, soiling). Natural cleaning events, as rain, are rare in typical locations for CSP plants, so artificial cleaning is the most common technique used to maintain the optical efficiency (heliostat reflectance) as close as possible to its optimal value in order to maximize the overall production and thus the revenues of the CSP plant. This paper provides a methodology to assess the soiling of a heliostat field and then evaluates the impact of artificial cleaning on the optical efficiency, considering different frequencies of cleaning in four different scenarios. The outcome of this study is a considerable step forward towards the optimization of costly cleaning activities to minimize operation costs in solar power technologies.

This literature review deals with the well-known problem of soiling in solar plants, which it severely affects the energy yield of solar power plants. A loss of reflectivity due to soiling reduces the entire productivity of the plant by limiting the energy harvested (i.e. the incoming direct normal irradiance is not properly reflected towards the right focus). On the other hand, the costs of maintenance and cleaning of the collectors represent a significant component of the plant operational costs. Therefore, in this paper, a multi-disciplinary literature review is conducted with the aim of collecting existing models for the key processes, organising them into a ‘dust life cycle’. This cycle is divided into four steps: Generation, Deposition, Adhesion, and Removal; with emphasis on the interaction between dust particles and solar collectors’ surfaces. Generation deals with the loading of atmosphere with dust particles, deposition concerns the processes that actually bring airborne dust onto the collectors’ surface, adhesion and removal represent the competing forces whose balance determine which particles remains adherent on the collectors and which are detached. The intent is to provide a complete framework for the development of a future physical model for the prediction and estimation of the actual soiling of the solar collectors, which engineers can implement in order to maximize the revenues of CSP plant, pushing towards more clean and sustainable energy production technologies.

Addressing soiling-related losses of glazed surfaces in solar power technologies is one of the most critical issues to improve the competitiveness of solar power plants in the current energy market. Reliable predictions of soiling rates and performance losses would enable optimisation of cleaning strategies and add a valuable assessment criterion for the evaluation of potential future power plant sites. However, currently available soiling models are data-driven and thus specific to the site and do not allow for reliable extrapolation to different sites and environmental conditions. To address this gap, this paper details the development of a physical soiling model for the prediction of the deposition of airborne dust onto the surface of solar collectors and the subsequent loss of performance (i.e. reflectance in the specific case of heliostats). The model inputs are the measured airborne dust concentration and estimated size distribution, the position of the mirrors, and the recorded wind speed and air temperature. The outputs of the model have been validated using experiments performed at the Queensland University of Technology. A reflectometer was used to measure the reflectance of the mirrors on an almost-daily basis while meteorological and dust data were obtained through a dust sensor and a weather station. The agreement between the experimental data and the simulated results demonstrates the effectiveness of the model in dry conditions. For example, for a mirror tilted at 45° with a daily loss of reflectance between 0.5%/day and 2.1%/day, the model reflectance loss predictions have an average relative error of 14%. Furthermore, the visual trends of the model predictions agree well with those observed in the experimental data. Finally, the model is used to investigate scenarios at three candidate sites to assess reflectance losses in different conditions.