Exergoeconomic analysis and optimization of a solar based multigeneration system using multiobjective differential evolution algorithm

This paper presents and analyzes a multigeneration energy system that consists of a reverse osmosis desalination unit, water heater, organic Rankine cycle, photovoltaic solar collectors, and a single effect absorption chiller. In doing so, energy and exergy analysis are first performed to evaluate t...

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Vydané v:Journal of cleaner production Ročník 170; s. 978 - 990
Hlavní autori: Rashidi, Halimeh, Khorshidi, Jamshid
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.01.2018
Predmet:
absorption
algorithms
desalination
Differential evolution algorithm
exergy
Exergy analysis
Multigeneration energy
Multiobjective optimization
Pareto frontier
Photovoltaic solar system
reverse osmosis
Sensitivity analysis
solar collectors
system optimization
Photovoltaic solar system
Sensitivity analysis
Pareto frontier
Multigeneration energy
Exergy analysis
Multiobjective optimization
Differential evolution algorithm
ISSN:0959-6526, 1879-1786
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Popis
Shrnutí:This paper presents and analyzes a multigeneration energy system that consists of a reverse osmosis desalination unit, water heater, organic Rankine cycle, photovoltaic solar collectors, and a single effect absorption chiller. In doing so, energy and exergy analysis are first performed to evaluate the performance of the system and determine the irreversibility of each component. Next, considering minimizing total cost rate and maximizing exergy efficiency as two objective functions, a multiobjective optimization approach based on differential evolution algorithm is proposed to determine the best design parameters. A self-adaptive technique is utilized to deal with the search capability, population diversity, and convergence speed of the proposed optimization algorithm. An external archive list is used to save all nondominated optimal solutions during the optimization. Dynamic crowding distance approach is employed to decrease archiving size without losing its characteristics. Furthermore, a fuzzy clustering approach is used to select the desired solution among the Pareto-optimal solutions. Simulation results are compared with two other multiobjective optimization algorithms and effectiveness of the proposed optimization method is verified using various indices. Finally, a sensitivity analysis is employed to evaluate effects of design parameters on exergy efficiency and total cost rate of the system.
Bibliografia:ObjectType-Article-1
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ISSN:0959-6526
1879-1786
DOI:10.1016/j.jclepro.2017.09.201