The major problem related to the implementation of Concentrating Solar Power (CSP) systems is related to their Levelized Cost of Electricity (LCOE), which is still higher than that associated to other energy production methods, thus limiting their competitiveness. It is widely recognized that large cost savings can still be achieved by improving the collector and solar field designs. In this context, one important issue is that, currently, very detailed analyses of CSP components’ production tolerances and assembly/mounting errors are performed in order to guarantee the desired optical performances of a CSP plant. Unfortunately, these analyses require very large computational efforts, so that full design optimizations, implying many model runs, become almost impractical. Hence, the main objective of this work is that of developing new, lean methodologies, still relying on proper experimental knowledge, for supporting the CSP system optimal design at affordable computational expenses. The methodological approach stems from an extension of a semi-analytic model of literature for the calculation of the intercept factor, i.e., an important optical efficiency parameter. The proposed modifications explicitly account for the effects of the production tolerances and assembly/mounting errors by including simple, parameterized FEM calculations and geometric considerations. After properly casting the problem into a probabilistic framework, we exploit the simplicity and computational speed of the proposed model in order to perform a Sobol-based global sensitivity analysis (GSA) of the CSP optical performances. From a purely engineering point of view, the results of this kind of analysis provide fundamental insights for supporting a decision-making process aimed at optimizing the CSP component production and the solar plant assembly/mounting at a full power production scale.

Modeling the effects of tolerances and assembly errors on the optical performances of parabolic collectors in a concentrating solar power system

F. Cadini;M. Fossati;S. Cardamone;M. Giglio
2018-01-01

Abstract

The major problem related to the implementation of Concentrating Solar Power (CSP) systems is related to their Levelized Cost of Electricity (LCOE), which is still higher than that associated to other energy production methods, thus limiting their competitiveness. It is widely recognized that large cost savings can still be achieved by improving the collector and solar field designs. In this context, one important issue is that, currently, very detailed analyses of CSP components’ production tolerances and assembly/mounting errors are performed in order to guarantee the desired optical performances of a CSP plant. Unfortunately, these analyses require very large computational efforts, so that full design optimizations, implying many model runs, become almost impractical. Hence, the main objective of this work is that of developing new, lean methodologies, still relying on proper experimental knowledge, for supporting the CSP system optimal design at affordable computational expenses. The methodological approach stems from an extension of a semi-analytic model of literature for the calculation of the intercept factor, i.e., an important optical efficiency parameter. The proposed modifications explicitly account for the effects of the production tolerances and assembly/mounting errors by including simple, parameterized FEM calculations and geometric considerations. After properly casting the problem into a probabilistic framework, we exploit the simplicity and computational speed of the proposed model in order to perform a Sobol-based global sensitivity analysis (GSA) of the CSP optical performances. From a purely engineering point of view, the results of this kind of analysis provide fundamental insights for supporting a decision-making process aimed at optimizing the CSP component production and the solar plant assembly/mounting at a full power production scale.
Solar energy; parabolic trough collector; optical efficiency; sensitivity analysis; optimization, cost minimization, genetic algorithms
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1073506
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