This paper explores the design optimization of parabolic-trough solar power generation systems. In these systems, solar radiation is focused onto receiver tubes in which a thermal carrier fluid is circulated. The collected thermal energy is then used to generate steam that powers a steam turbine to drive an electric generator. An optimization model is constructed that aims to minimize the cost of electric energy produced. In this model, the optimization is concerned with decision variables that affect: i) the solar field and ii) thermal storage. The steam turbine and generator are not part of the optimization model, as they are assumed to use the same off-shelf components that are used in fossil-fuel based power plants. It is understood that decisions concerning the solar field both affect and are affected by the design of the solar collector assemblies (SCAs), which are the support structures that hold the focusing mirrors. Design of the SCAs is a structural optimization problem that aims to minimize the cost of the structure while satisfying dimensional and loading constraints. Genetic algorithm (GA) is used for the optimization of the parabolic trough system model. For every candidate design examined by GA for the solar field and thermal storage, the most suitable structural design of the SCAs is obtained from solving the sub-problem of structural optimization. This “nested” optimization model is made possible by pre-analyzing a large range of SCA designs and recording them as a lookup database. The developed optimization model of the parabolic trough systems allows for parametric studies on how certain incentives, government policies and key technological developments may affect the system design decisions.

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