Global optimization of solar power tower systems using a Monte Carlo algorithm: Application to a redesign of the PS10 solar thermal power plant. (April 2018)

• Record Type: Journal Article
• Authors: Farges, O.; Bézian, J.J.; El Hafi, M.
• Is Part Of: Renewable energy. Volume 119(2018)
• Page Start: 345
• Page End: 353
• Publication Date: 2018-04
• Subjects: Global optimization -- Solar power tower -- Lifetime performance -- Heliostat field layout; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2017.12.028
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: There is a need to enhance the performance of Solar Power Tower (SPT) systems in view of their significant capital costs. In this context, the preliminary design step is of great interest as improvements here can reduce the global cost. This paper presents an optimization method that approaches optimal SPT system design through the coupling of a Particle Swarm Optimization algorithm and a Monte Carlo algorithm, in order to assess both the yearly heliostat field optical efficiency and the thermal energy collected annually by an SPT system. This global optimization approach is then validated on a well-known SPT system, ie the PS10 Solar Thermal Power plant. First, the direct model is compared to in-situ measurements and simulation results. Then, the PS10 heliostat field is redesigned using the optimization tool. This redesign step leads to an annual gain between 3.34% and 23.5% in terms of the thermal energy collected and up to about 9% in terms of the heliostat field optical efficiency from case to case. Highlights: Coupling of an efficient Monte Carlo algorithm with a particle swarm optimizer. Redesign of the PS10 heliostats field. Meaningful gains of 23:5% of the yearly energy and 9% of the yearly efficiency.