Solar energy is one of a very few low-carbon energy technologies with the enormous potential to grow to a large scale. Currently, solar power is generated via the photovoltaic (PV) and concentrating solar power (CSP) technologies. The ability of CSPs to scale up renewable energy at the utility level, as well as to store energy for electrical power generation even under circumstances when the sun is not available (after sunset or on a cloudy day), makes this technology an attractive option for sustainable clean energy. The levelised electricity cost (LEC) of CSP with thermal storage was about 0.16-0.196 Euro/kWh in 2013 (Kost et al., 2013). However, lowering LEC and harvesting more solar energy from CSPs in future motivate researchers to work harder towards the optimisation of such plants. The situation tempts people and governments to invest more in this ultimate clean source of energy while shifting the energy consumption statistics of their societies from fossil fuels to solar energy.
Usually, researchers just concentrate on the optimisation of technical aspects of CSP plants (thermal and/or optical optimisation). However, the technical optimisation of a plant while disregarding economic goals cannot produce a fruitful design and in some cases may lead to an increase in the expenses of the plant, which could result in an increase in the generated electrical power price.
The study focused on a comprehensive optimisation of one of the main CSP technology types, the linear Fresnel collector (LFC). In the study, the entire LFC solar domain was considered in an optimisation process to maximise the harvested solar heat flux throughout an imaginary summer day (optical goal), and to minimise cavity receiver heat losses (thermal goal) as well as minimising the manufacturing cost of the plant (economic goal). To illustrate the optimisation process, an LFC was considered with 12 design parameters influencing three objectives, and a unique combination of the parameters was found, which optimised the performance. In this regard, different engineering tools and approaches were introduced in the study, e.g., for the calculation of thermal goals, Computational Fluid Dynamics (CFD) and view area approaches were suggested, and for tackling optical goals, CFD and Monte-Carlo based ray-tracing approaches were introduced. The applicability of the introduced methods for the optimisation process was discussed through case study simulations. The study showed that for the intensive optimisation process of an LFC plant, using the Monte Carlo-based ray-tracing as high fidelity approach for the optical optimisation objective, and view area as a low fidelity approach for the thermal optimisation objective, made more sense due to the saving in computational cost without sacrificing accuracy, in comparison with other combinations of the suggested approaches.
The study approaches can be developed for the optimisation of other CSP technologies after some modification and manipulation. The techniques provide alternative options for future researchers to choose the best approach in tackling the optimisation of a CSP plant regarding the nature of optimisation, computational cost and accuracy of the process.