Cruz, N. C., Salhi, Said, Redondo, J. L., Álvarez, J. D., Berenguel, M., Ortigosa, P. M. (2019) Design of a parallel genetic algorithm for continuous and pattern-free heliostat field optimization. The Journal of Supercomputing, 75 (3). pp. 1268-1283. ISSN 0920-8542. (doi:10.1007/s11227-018-2404-8) (KAR id:67058)
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Official URL: https://doi.org/10.1007/s11227-018-2404-8 |
Abstract
The heliostat field of solar power tower plants can suppose up to 50% of investment costs and 40% of energy loss. Unfortunately, obtaining an optimal field requires facing a complex non-convex, continuous, large-scale, and constrained optimization problem. Although pattern-based layouts and iterative deployment are popular heuristics to simplify the problem, they limit flexibility and might be suboptimal. This work describes a new genetic algorithm for continuous and pattern-free heliostat field optimization. Considering the potential computational cost of the objective function and the necessity of broad explorations, it has been adapted to run in parallel on shared-memory environments. It relies on elitism, uniform crossover, static penalization of infeasibility, and tournament selection. Interesting experimental results show an optimization speedup up to 15× with 16 threads. It could approximately reduce a one year runtime, at complete optimization, to a month only. The optimizer has also been made available as a generic C++ library.
Item Type: | Article |
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DOI/Identification number: | 10.1007/s11227-018-2404-8 |
Uncontrolled keywords: | Genetic algorithm, Parallel computing, Heliostat field optimization, Solar power tower |
Divisions: | Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems |
Depositing User: | Said Salhi |
Date Deposited: | 17 May 2018 08:22 UTC |
Last Modified: | 19 Sep 2023 15:03 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/67058 (The current URI for this page, for reference purposes) |
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