Design of a parallel genetic algorithm for continuous and pattern-free heliostat field optimization

Cruz, Nicolás C.; Salhi, Saı̈d; Redondo, Juana López; Álvarez, José Domingo; Berenguel, Manuel; Ortigosa, Pilar M.

doi:10.1007/s11227-018-2404-8

Cited by 14 publications

(17 citation statements)

References 25 publications

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“…In this configuration, dynamic receivers mounted on arrays of small towers enable heliostats in mini subfields to direct sunlight with minimal cosine losses, thus improving the field's overall optical efficiency. N. Cruz et al [21] also developed an algorithm using a genetic algorithm that generates a continuous pattern-free field layout. The algorithm developed, using parallelization, provides a solution to the conceptual complexity and high computational cost associated with pattern-free heliostat field optimization.…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation and Design of Multi-Tower Concentrated Solar Power Fields

2020

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In power tower systems, the heliostat field is one of the essential subsystems in the plant due to its significant contribution to the plant's overall power losses and total plant investment cost. The design and optimization of the heliostat field is hence an active area of research, with new field improvement processes and configurations being actively investigated. In this paper, a different configuration of a multi-tower field is explored. This involves adding an auxiliary tower to the field of a conventional power tower Concentrated Solar Power (CSP) system. The choice of the position of the auxiliary tower was based on the region in the field which has the least effective reflecting heliostats. The multi-tower configuration was initially applied to a 50 MWth conventional field in the case study region of Nigeria. The results from an optimized field show a marked increase in the annual thermal energy output and mean annual efficiency of the field. The biggest improvement in the optical efficiency loss factors be seen from the cosine, which records an improvement of 6.63%. Due to the size of the field, a minimal increment of 3020 MWht in the Levelized Cost of Heat (LCOH) was, however, recorded. In much larger fields, though, a higher number of weaker heliostats were witnessed in the field. The auxiliary tower in the field provides an alternate aim point for the weaker heliostat, thereby considerably cutting down on some optical losses, which in turn gives rise to higher energy output. At 400 MWth, the multi-tower field configuration provides a lower LCOH than the single conventional power tower field.

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Section: Introductionmentioning

confidence: 99%

Numerical Simulation and Design of Multi-Tower Concentrated Solar Power Fields

2020

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“…In [28], a genetic algorithm is designed for this optimization approach. The proposal in [29] is also a genetic algorithm, but it allows infeasible solutions during the search.…”

Section: Introductionmentioning

confidence: 99%

“…Its name is 'Hector', which results from replacing the first letter in the word 'sector' with 'h' (from 'heliostat'). Its novelty lies in offering a mid-term solution between handling all the coordinates directly [29,12,28], and fixing the field after every deployment [8]. The greedy approach in [8] is limited due to its local scope.…”

Section: Introductionmentioning

confidence: 99%

“…The greedy approach in [8] is limited due to its local scope. The methods in [29,12,28] have a global scope, but they try to solve such a difficult problem that the results achieved are likely to be suboptimal too. Hector assumes that, in practical terms, the same optimizer can obtain better results by addressing simpler instances, yet without limiting to a greedy strategy.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, all the sectors are linked and cloned into the other side. The effectiveness of Hector has been proved by studying its effect on the optimizer designed in [29] by the authors of this work.…”

Section: Introductionmentioning

confidence: 99%

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