IChOA is compared with six competing algorithms, including the standard chimp optimization algorithm (ChOA), 25 whale optimization algorithm (WOA), 26 grey wolf optimization algorithm (GWO), 27 opposition-based sine cosine algorithm (OBSCA), 28 and harris hawks optimization (HHO). 29 Meanwhile, for a fair comparison, IChOA utilizes a population size
Customer ServiceThe proposed approach is realized using Solar Cell Capacitance Simulator (SCAPS-1D) software incorporated with a hybrid L32 Taguchi DoE-based Genetic Algorithm.
Customer ServiceAdditionally, for the comparative analysis, the modified one diode model (MODM) of solar PV cell has been used to obtain the output characteristic of PV cell. The
Customer ServiceOne of the most important and challenging issues with PV systems is the accurate and efficient modeling of solar cells (and PV modules). These issues are mainly caused by the nonlinear characteristics of solar cells, as well as the unavailability of all their parameters (Yousri et al., 2020, Chenche et al., 2018) order to properly analyze and evaluate the actual
Customer ServiceAdditionally, for the comparative analysis, the modified one diode model (MODM) of solar PV cell has been used to obtain the output characteristic of PV cell. The metaheuristic algorithm namely
Customer ServiceIdentifying the parameters of solar photovoltaic (PV) cell models accurately and reliably is crucial for simulating, evaluating, and controlling PV systems. For this reason, we present an improved chimp optimization
Customer ServiceTo tackle this challenge, this paper introduces the adaptive sine–cosine particle swarm optimization algorithm (ASCA-PSO) as a method for estimating the parameters of solar cells and photovoltaic modules. The ASCA-PSO approach combines the strengths of the SCA and PSO algorithms in a two-tier process.
Customer ServicePerovskite solar cells are revolutionizing the field of renewable energy with their remarkable properties and engineering applications [11].These solar cells offer high power conversion efficiencies, rivaling traditional silicon-based cells while being significantly cheaper to produce [12].Their low cost is due to the use of abundant and inexpensive raw materials and
Customer ServiceTo tackle this challenge, this paper introduces the adaptive sine–cosine particle swarm optimization algorithm (ASCA-PSO) as a method for estimating the parameters of solar
Customer Service357 1 3 Design and characterization of eective solar cells • We optimized, evaluated, and characterized 15 cell designs. • We present a new algorithm called OptIA-II for MOO of solar cells. • We show that our two-stage MOO can improve the quantum eciency of cells and characterize cell designs into clusters concerning to trade-o between cells
Customer ServiceOur solar cells design characterization enables us to perform a cost-benefit analysis of solar cells usage in real-world applications. We propose a two-stage multi-objective optimization framework for full scheme solar cell
Customer ServiceABC-PSO algorithm predicts 106 k W PV capacity, 8 k W FC, 45 k W electrolyzer with a hydrogen tank of 150 k g. The system shows grid sale and purchase capacity of 25 and 30 k W, respectively. The initial cost of 1,29,824 $ is required to set up the project.
Customer ServiceThe experimental results obtained from three algorithms have been compared in terms of cost effectiveness of the system. The hybrid system has been designed to cater the total electricity demand of 135 M W h / y r of a small community center. It has emerged from the simulation results that the total electricity demand could be met with a 106 k W solar photovoltaic, 8 k W
Customer Service3 天之前· Fig. 3 illustrates the interactions between the design parameters—solar collector area, fuel cell capacity, solar collector type, and cooling system type—and the 3E performance indicators: energy, economic, and environmental outcomes. The flowchart identifies how each design factor influences key interaction metrics, such as energy output
Customer ServiceABC-PSO algorithm predicts 106 k W PV capacity, 8 k W FC, 45 k W electrolyzer with a hydrogen tank of 150 k g. The system shows grid sale and purchase
Customer ServiceIntroducing a hybrid PSO-GA method to provide a robust optimization solution. This study proposes a novel approach to evaluate the integration of solar photovoltaic (PV) and wind turbine renewable energy systems (RES) with Electrolyzer-Fuel Cell Energy Storage System (EFCS) and Battery Energy Storage System (BESS).
Customer ServiceKey learnings: Solar Cell Definition: A solar cell (also known as a photovoltaic cell) is an electrical device that transforms light energy directly into electrical energy using the photovoltaic effect.; Working Principle: The working of solar cells involves light photons creating electron-hole pairs at the p-n junction, generating a voltage capable of driving a current across
Customer ServiceDahbi et al. [19] presented the design and modeling of grid connected SPV-FC hybrid energy system, to manage the excess energy produced by SPV panels to supply it to electrolyzer and further applied electrolysis optimization approach.Aseeb et al. [20] modeled SPV and fuel cell hybrid energy system using buck and boost converter to extract maximum power
Customer ServiceWe present a new algorithm called OptIA-II for MOO of solar cells. We show that our two-stage MOO can improve the quantum eficiency of cells and characterize cell designs into clusters concerning to trade-of between cells fabrication cost and cells quantum eficiency.
Customer ServiceOur solar cells design characterization enables us to perform a cost-benefit analysis of solar cells usage in real-world applications. We propose a two-stage multi-objective optimization framework for full scheme solar cell structure design and characterization, cost minimization and quant
Customer ServiceWe present a new algorithm called OptIA-II for MOO of solar cells. We show that our two-stage MOO can improve the quantum eficiency of cells and characterize cell designs into clusters
Customer ServiceIntroducing a hybrid PSO-GA method to provide a robust optimization solution. This study proposes a novel approach to evaluate the integration of solar photovoltaic (PV)
Customer Service4 天之前· This not only confirms the effectiveness of XGBoost''s novel tree learning algorithm in handling sparse data but also reflects its capacity to prevent overfitting. The model''s superior precision and reliability in predicting the performance parameters of solar cells benefit from the amalgamation of the immune system optimization algorithm that automatically selected the
Customer ServiceThe proposed approach is realized using Solar Cell Capacitance Simulator (SCAPS-1D) software incorporated with a hybrid L32 Taguchi DoE-based Genetic Algorithm. Based on Multiple Linear Regression
Customer Service4 天之前· This not only confirms the effectiveness of XGBoost''s novel tree learning algorithm in handling sparse data but also reflects its capacity to prevent overfitting. The model''s superior
Customer ServiceIdentifying the parameters of solar photovoltaic (PV) cell models accurately and reliably is crucial for simulating, evaluating, and controlling PV systems. For this reason, we present an improved chimp optimization algorithm (IChOA) for the generation of precise and reliable solar PV cell models.
Customer ServiceLiterature carries out multi-objective optimization design of various PPG systems composed of new energy power generation by means of evolutionary algorithm and GA (genetic algorithm) algorithm; Literature uses chaos-based multi-objective GA to optimize the system capacity of wind-solar hybrid power generation microgrid, taking power supply reliability,
Customer ServiceThe main contribution of this study is the proposal of an enhanced version of the Al-Biruni-Algorithm based on chaos theory to determine the optimal equipment capacity of fuel cells, Wind Turbines, and solar panels in a hybrid green
Customer ServiceThe solar power generation capacity has increased by nearly 100 GWp in 2017, which is about 31 per cent more from 2017 [5, 6 a large number of MPPT algorithms are accessible in the literature for both off-grid and grid associated PV systems . The selection of a specific MPPT system from the various existing MPPT methods is a confounding errand since
Customer ServiceWhen designing and optimizing a solar cell structure, we use two light-trapping methods: light-trapping BR layer and nano-texturing. Metals like silver (Ag) maybe used as a BR layer, while alkaline solutions like KOH or NaOH are used for nano-texturing of layer’s interfaces.
Maximization of solar cell quantum eficiency ( Qe) [28, 32] and minimization of microcrystalline silicon layer thickness ( d c-Si ) are two objectives of the cell struc-ture design.
Since solar energy is the most used green energy method, many research works (e.g., [2, 14, 30]) have been done on solar cell design and cell structure optimization to improve cells light-harvesting eficiency and solar energy production capacity maximization.
We optimized, evaluated, and characterized 15 cell designs. We present a new algorithm called OptIA-II for MOO of solar cells. We show that our two-stage MOO can improve the quantum efficiency of cells and characterize cell designs into clusters concerning to trade-off between cells fabrication cost and cells quantum efficiency.
The quantum efficiency (\ (Q_e\)) of a solar cell is the ratio of charge carrier produced at the external circuit of the cell (electronic device) to the number of photons received (or absorbed) by the cell. There are two ways this quantum efficiency ratio is calculated: (i) external quantum efficiency and (ii) internal quantum efficiency.
In our two-stage MOO, the Pareto-fronts of three select best cell designs of NSGA-II based MOO stage were fine-tuned by our designed multi-objective opti-mization-immunological algorithm (OptIA-II). We observed that OptIA-II algo-rithm improved both costs associated with solar cell design.
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