Robust multi-objective optimal design of islanded hybrid system with renewable and diesel sources/stationary and mobile energy storage systems

Yang, Zaoli; Ghadamyari, Mojtaba; Khorramdel, Hossein; Alizadeh, Seyed Mehdi Seyed; Pirouzi, Sasan; Milani, Muhammed; Banihashemi, Farzad; Ghadimi, Noradin

doi:10.1016/j.rser.2021.111295

Cited by 230 publications

(73 citation statements)

References 30 publications

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“…where I is the membrane thickness; S refers to the membrane surface (cm 2 ); I FC is the PEMFC operating current; T FC defines the operating temperature of the cell ( C H2 is the hydrogen saturation and C O2 is the oxygen saturation (mol/cm 3 ); and R m and R c are the membrane and connection resistances, respectively. Table 1 illustrates the optimal value of some undefined parameters based on [15].…”

Section: Modeling Of the Fuel Cellmentioning

confidence: 99%

“…Recently, different types of approaches were presented for this purpose. The use of meta-heuristic algorithms, due to their ability for faster and simpler solving of nonlinear models, are exponentially increasing [13][14][15][16]. Some examples of these methods are the dragonfly algorithm [17], deer hunting optimizer [18_ENREF_20], multi-verse optimizer [19_ENREF_18], improved invasive weed optimization algorithm [20_ENREF_19], and chaotic grasshopper optimizer [21].…”

Section: Introductionmentioning

confidence: 99%

“…Cao et al [15] utilized an improved optimization technique for modeling, designing, and simulating the PEMFC systems with an optimal solution. The optimization method was defined by the seagull optimizer, and the purpose was to use that tool for the best identification of parameters of the PEMFC stack.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of PEMFC Model Parameters Using Meta-Heuristics

et al. 2021

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The present study introduces an economical–functional design for a polymer electrolyte membrane fuel cell system. To do so, after introducing the optimization problem and solving the problem based on the presented equations in the fuel cell, a cost model is presented. The final design is employed for minimizing the construction cost of a 50 kW fuel cell stack, along with the costs of accessories regarding the current density, stoichiometric coefficient of the hydrogen and air, and pressure of the system as well as the temperature of the system as optimization parameters. The functional–economic model is developed for the studied system in which all components of the system are modeled economically as well as electrochemically–mechanically. The objective function is solved by a newly improved metaheuristic technique, called converged collective animal behavior (CCAB) optimizer. The final results of the method are compared with the standard CAB optimizer and genetic algorithm as a popular technique. The results show that the best optimal cost with 0.1061 $/kWh is achieved by the CCAB. Finally, a sensitivity analysis is provided for analyzing the consistency of the method.

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Section: Modeling Of the Fuel Cellmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimization of PEMFC Model Parameters Using Meta-Heuristics

et al. 2021

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“…The magnitude of the negative impact of climate change on water resources varies from point to point and changes in runoff characteristics resulting from climate change are related to the characteristics of each watershed. 5 In particular, watershed geology and surface slope are the first factors controlling the time and amount of runoff in the face of climate change. 6 Numerous models have been proposed to study global climate change and its impact on the hydro-geomorphological characteristics of the watershed.…”

Section: Introductionmentioning

confidence: 99%

“…One of the impacts of climate change is the impact on water resources. The magnitude of the negative impact of climate change on water resources varies from point to point and changes in runoff characteristics resulting from climate change are related to the characteristics of each watershed 5 . In particular, watershed geology and surface slope are the first factors controlling the time and amount of runoff in the face of climate change 6 …”

Section: Introductionmentioning

confidence: 99%

Using an optimized soil and water assessment tool by deep belief networks to evaluate the impact of land use and climate change on water resources

Khayatnezhad

Youssefi

2022

Concurrency and Computation

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This article investigates the negative effect of land use and climate changes on water resources by the SWAT and SWAT-DEEP\LMSFO model. Due to the importance of runoff impact on water resources in this article, the hybrid hydrological-deep neural networks optimized by the improved SFO based on logistic map (LMSFO) algorithm have been used to provide more accurate results for runoff estimation. This method improves runoff simulation. Firstly, runoff under the influence of land use and climate change is estimated by the SWAT model. Once again, runoff is estimated by the SWAT-DEEP/LMSFO model. In the DEEP/LMSFO model, the primary runoff has been estimated by an un-calibrated SWAT model. Then the Primary runoff simulated is entered as an input into the DEEP model. Finally, the runoff is estimated by a test-training method. The results of the SWAT model and the DEEP/LMSFO model show that there is an inverse correlation between land use so that reducing land cover increases runoff. The results show that climate change and land-use change can affect annual runoff changes in the coming years.

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