A novel multi-objective Dynamic Programming optimization method: Performance management of a solar thermal power plant as a case study

Mahmoudimehr, Javad; Sebghati, Parvin

doi:10.1016/j.energy.2018.11.079

Cited by 44 publications

(22 citation statements)

References 82 publications

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“…Solar energy is the main energy source for the SPT system which consists of a series of well‐placed heliostats field that are used to direct the sun's rays all day long to the receiver which is placed at the top of the tower. The received reflected solar energy is processed based on conventional thermodynamic cycles which considers the effects of several parameters such as Stirling, Brayton, Gas Turbine, Ericsson, and others 2‐5 . These conventional thermodynamic cycles allow for a generated electricity 6‐8 .…”

Section: Introductionmentioning

confidence: 99%

Design optimization of a solar tower power plant heliostat field by considering different heliostat shapes

et al. 2020

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The present study focuses on the optimization of solar tower power plant heliostat field by considering different heliostat shapes including rectangular, square, pentagon, hexagon, heptagon, octagon, and circular heliostat shapes. The optimization is carried out using an in-house developed code-based MATLAB program. The developed in-house code is validated first on a wellknown PS10 Solar Thermal Power plant having rectangular heliostats shape and the resulting yearly unweighted heliostat field efficiency of about 64.43% could be obtained. The optimized PS10 heliostat field using different heliostat shapes showed that the circular and octagon heliostat shapes provide better efficiency with minimum land area. The yearly efficiency is increased from 69.65% for the rectangular heliostat shape to 70.96% and 71% for the octagon and circular shapes, respectively. In addition, the calculated field area (land area) is reduced for the case of circular and octagon heliostat shapes with a gain of about 11.10% and 10.93% (about 42.0436 × 10 3 and 41.4036 × 10 3 m 2), respectively, in comparison with the PS10 field area.

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Section: Introductionmentioning

confidence: 99%

Design optimization of a solar tower power plant heliostat field by considering different heliostat shapes

et al. 2020

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“…Se han aplicado modelos del SF a problemas de control [3], en herramientas de simulación [2] [5], en problemas de planificación de la producción [11,13] o de asignación de tareas [8,12] y en problemas sobre dimensionado de la planta [6,4]. Estos modelos están principalmente basados en primeros principios, y pueden ser tanto dinámicos [2,5] como estáticos (quasi-stationary) [7], aunque también pueden encontrase trabajos donde el campo solar es modelado simplemente con una relación lineal entre la radiación solar y la potencia térmica [10,9]. El desarrollo de modelos basados en prin-cipios físicos tiene cierto grado de complejidad y requiere un conocimiento profundo del SF estudiado.…”

Section: Introductionunclassified

Modelos empíricos del campo solar en plantas termosolares de concentración. Aplicación a la planificación

Cojocaru¹,

Bravo²,

Vasallo³

et al. 2020

XL Jornadas De Automática: Libro De Actas (Ferrol, 4-6 De Septiembre De 2019)

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“…In order to ensure the best system performance, many programming methods are used in the objective function optimization process of the system . Bélanger and Gosselin obtained the effect of variables on the objective function and the potential relationship between different objectives through multiobjective optimization.…”

Section: Introductionmentioning

confidence: 99%

“…In order to ensure the best system performance, many programming methods are used in the objective function optimization process of the system. [16][17][18] Bélanger and Gosselin 19 obtained the effect of variables on the objective function and the potential relationship between different objectives through multiobjective optimization. Pourmirzaagha et al 20 designed a new type of battery cell using the particle swarm optimization, and both single-objective and multiobjective optimization methods were used to find the optimal battery thickness and energy.…”

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confidence: 99%

Multiobjective optimization for exergoeconomic analysis of an integrated cogeneration system

Zhang

Pan

et al. 2019

Int J Energy Res

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Summary In this paper, a novel cogeneration system integrating Kalina cycle, CO2 chemical absorption, process, and flash‐binary cycle is proposed to remove acid gases in the exhaust gas of solid oxide fuel cell (SOFC) system, improve the waste heat utilization, and reduce the cold energy consumed during CO2 capture. In the CO2 chemical absorption process, the methyldiethanolamine (MDEA) aqueous solution is utilized as a solvent, and feed temperature and absorber pressure are optimized via Aspen Plus software. The single‐objective and multiobjective optimization are carried out for the flash‐binary cycle subsystem. Results show that when the multiobjective optimization is applied to identify the exergoeconomic condition, the cogeneration system can simultaneously satisfy the high thermodynamic cycle efficiency and also the low product unit cost. The optimal results of the exergy efficiency, product unit cost, and normalized CO2 emissions obtained by Pareto chart were 75.84%, 3.248 $/GJ, and 13.14 kg/MWhr, respectively.

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A novel multi-objective Dynamic Programming optimization method: Performance management of a solar thermal power plant as a case study

Cited by 44 publications

References 82 publications

Design optimization of a solar tower power plant heliostat field by considering different heliostat shapes

Design optimization of a solar tower power plant heliostat field by considering different heliostat shapes

Modelos empíricos del campo solar en plantas termosolares de concentración. Aplicación a la planificación

Multiobjective optimization for exergoeconomic analysis of an integrated cogeneration system

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