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Solar: a solar thermal power plant simulator for blackbox optimization benchmarking.

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Title: Solar: a solar thermal power plant simulator for blackbox optimization benchmarking.
Authors: Andrés-Thió, Nicolau1 (AUTHOR) nandres@student.unimelb.edu.au, Audet, Charles2 (AUTHOR) Charles.Audet@polymtl.ca, Diago, Miguel3 (AUTHOR) diagomartinez.miguel3@hydroquebec.com, Gheribi, Aïmen E.4 (AUTHOR) aimen.gheribi@concordia.ca, Digabel, Sébastien Le2 (AUTHOR) sebastien.le-digabel@polymtl.ca, Lebeuf, Xavier2 (AUTHOR) xavier.lebeuf@polymtl.ca, Garneau, Mathieu Lemyre2 (AUTHOR) mathieu.lemyre-garneau@polymtl.ca, Tribes, Christophe2 (AUTHOR) christophe.tribes@polymtl.ca
Source: Optimization & Engineering. Sep2025, Vol. 26 Issue 3, p1815-1861. 47p.
Subjects: Solar thermal energy, Solar energy, Prediction models, Constrained optimization, Benchmark problems (Computer science), Open source software, Reproducible research
Abstract: This work introduces solar , a collection of ten optimization problem instances for benchmarking blackbox optimization solvers. The instances present different design aspects of a concentrated solar power plant simulated by blackbox numerical models. The type of variables (discrete or continuous), dimensionality, and number and types of constraints (including hidden constraints) differ across instances. The objective or constraints may be deterministic or stochastic outputs of the simulator, with possibilities to execute several replications to control stochasticity. Most instances offer variable fidelity surrogates, two are biobjective and one is constrained only by bounds. The solar plant model takes into account various subsystems: a heliostats field, a central cavity receiver (the receiver), a molten salt thermal energy storage, a steam generator and an idealized power block. Several numerical methods are implemented throughout the solar code and most of the executions are time-consuming. Great care was applied to guarantee reproducibility across platforms. The solar tool encompasses most of the characteristics that can be found in industrial and real-life blackbox optimization problems, all in an open-source and stand-alone code. [ABSTRACT FROM AUTHOR]
Copyright of Optimization & Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 187974151
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PubType: Academic Journal
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  Data: Solar: a solar thermal power plant simulator for blackbox optimization benchmarking.
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  Data: <searchLink fieldCode="AR" term="%22Andrés-Thió%2C+Nicolau%22">Andrés-Thió, Nicolau</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> nandres@student.unimelb.edu.au</i><br /><searchLink fieldCode="AR" term="%22Audet%2C+Charles%22">Audet, Charles</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> Charles.Audet@polymtl.ca</i><br /><searchLink fieldCode="AR" term="%22Diago%2C+Miguel%22">Diago, Miguel</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> diagomartinez.miguel3@hydroquebec.com</i><br /><searchLink fieldCode="AR" term="%22Gheribi%2C+Aïmen+E%2E%22">Gheribi, Aïmen E.</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> aimen.gheribi@concordia.ca</i><br /><searchLink fieldCode="AR" term="%22Digabel%2C+Sébastien+Le%22">Digabel, Sébastien Le</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> sebastien.le-digabel@polymtl.ca</i><br /><searchLink fieldCode="AR" term="%22Lebeuf%2C+Xavier%22">Lebeuf, Xavier</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> xavier.lebeuf@polymtl.ca</i><br /><searchLink fieldCode="AR" term="%22Garneau%2C+Mathieu+Lemyre%22">Garneau, Mathieu Lemyre</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> mathieu.lemyre-garneau@polymtl.ca</i><br /><searchLink fieldCode="AR" term="%22Tribes%2C+Christophe%22">Tribes, Christophe</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> christophe.tribes@polymtl.ca</i>
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  Data: <searchLink fieldCode="JN" term="%22Optimization+%26+Engineering%22">Optimization & Engineering</searchLink>. Sep2025, Vol. 26 Issue 3, p1815-1861. 47p.
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  Data: <searchLink fieldCode="DE" term="%22Solar+thermal+energy%22">Solar thermal energy</searchLink><br /><searchLink fieldCode="DE" term="%22Solar+energy%22">Solar energy</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Constrained+optimization%22">Constrained optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Benchmark+problems+%28Computer+science%29%22">Benchmark problems (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Open+source+software%22">Open source software</searchLink><br /><searchLink fieldCode="DE" term="%22Reproducible+research%22">Reproducible research</searchLink>
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  Data: This work introduces solar , a collection of ten optimization problem instances for benchmarking blackbox optimization solvers. The instances present different design aspects of a concentrated solar power plant simulated by blackbox numerical models. The type of variables (discrete or continuous), dimensionality, and number and types of constraints (including hidden constraints) differ across instances. The objective or constraints may be deterministic or stochastic outputs of the simulator, with possibilities to execute several replications to control stochasticity. Most instances offer variable fidelity surrogates, two are biobjective and one is constrained only by bounds. The solar plant model takes into account various subsystems: a heliostats field, a central cavity receiver (the receiver), a molten salt thermal energy storage, a steam generator and an idealized power block. Several numerical methods are implemented throughout the solar code and most of the executions are time-consuming. Great care was applied to guarantee reproducibility across platforms. The solar tool encompasses most of the characteristics that can be found in industrial and real-life blackbox optimization problems, all in an open-source and stand-alone code. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Optimization & Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s11081-024-09952-x
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        Text: English
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        StartPage: 1815
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      – SubjectFull: Solar thermal energy
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      – SubjectFull: Solar energy
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      – SubjectFull: Prediction models
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      – SubjectFull: Constrained optimization
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      – SubjectFull: Benchmark problems (Computer science)
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      – SubjectFull: Reproducible research
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      – TitleFull: Solar: a solar thermal power plant simulator for blackbox optimization benchmarking.
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              M: 09
              Text: Sep2025
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              Y: 2025
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