Metaheuristic Algorithm for Photovoltaic Parameters: Comparative Study and Prediction with a Firefly Algorithm

In this paper, a Firefly algorithm is proposed for identification and comparative study of five, seven and eight parameters of a single and double diode solar cell and photovoltaic module under different solar irradiation and temperature. Further, a metaheuristic algorithm is proposed in order to predict the electrical parameters of three different solar cell technologies. The first is a commercial RTC mono-crystalline silicon solar cell with single and double diodes at 33 °C and 1000 W/m2. The second, is a flexible hydrogenated amorphous silicon a-Si:H solar cell single diode. The third is a commercial photovoltaic module (Photowatt-PWP 201) in which 36 polycrystalline silicon cells are connected in series, single diode, at 25 °C and 1000 W/m2 from experimental current-voltage. The proposed constrained objective function is adapted to minimize the absolute errors between experimental and predicted values of voltage and current in two zones. Finally, for performance validation, the parameters obtained through the Firefly algorithm are compared with recent research papers reporting metaheuristic optimization algorithms and analytical methods. The presented results confirm the validity and reliability of the Firefly algorithm in extracting the optimal parameters of the photovoltaic solar cell

Hybrid resolution approaches for dynamic assignment problem of reusable containers

In this study, we are interested in the reusing activities of reverse logistics. We focus on the dynamic assignment of reusable containers problem (e.g. gas bottles, beverages, pallets, maritime containers, etc.). The objective is to minimize the collect, reloading, storage and redistribution operations costs over a fixed planning horizon taking into account the greenhouse gas emissions. We present a new generic Mixed Integer Programming (MIP) model for the problem. The proposed model was solved using the IBM ILOG CPLEX optimization software; this method yield exact solutions, but it is very time consuming. So we adapted two hybrid approaches using a genetic algorithm to solve the problem at a reduced time (The second hybrid approach is enhanced with a local search procedure based on the Variable Neighborhood Search VNS). The numerical results show that both developed hybrid approaches generate high-quality solutions in a moderate computational time, especially the second hybrid method

Dynamic Planning of Reusable Containers in a Close-loop Supply Chain under Carbon Emission Constrain

Nowadays, Companies need to collect and to deliver goods from and to their depots and their customers. Reusable containers are considered as a greener choice and a cost saving strategy. This paper addresses a dynamic management of reusable containers (e.g gases bottles, wood pallets, maritime containers, etc.) in a Closed-loop supply chain. The aim of the study is to find an optimal lot sizing and assignment strategy that minimizes the cost of reusable containers management under environmental constraint. In this contribution, a new integer-linear-programming model and two hybrid approaches based on the genetic algorithm are proposed to solve the problem. The second hybrid method is enhanced with a local search based on the VNS (variable neighborhood search). The numerical results show the performance of the two hybrid approaches in terms of solution quality and response time

Comparison and evaluation of statistical criteria in the solar cell and photovoltaic module parameters’ extraction

Recently, the extraction of photovoltaic parameters was the subject of intensive debate in the scientific literature. This paper presents an analysis and comparison of the use of the statistical errors in extraction of single and double diodes of the solar cell and photovoltaic module parameters. The compared errors are based on the utilisation of the firefly algorithm. The objective function used is adapted to minimise the absolute errors between the experimental and predicted current values. Two technologies of the solar cell are used to compare; a mono-crystalline silicon solar cell and the polycrystalline silicon photovoltaic module with 36 cells connected in series. The performance of the extracted parameters and current is compared with recent algorithms and techniques. The analysis and comparison of commonly used error measure help in evaluating the predictive ability of parameters extraction. The comparisons demonstrate that the firefly algorithm in statistical errors measure provides high performance and accuracy of the extracted parameters

Comparative prediction of single and double diode parameters for solar cell models with firefly algorithm

Due to the non-linearity of current-voltage of solar cell model, the conventional methods are incapable to extract the parameters of solar cell with high accuracy. The implicit nonlinear equation describing the single and double diodes solar cell in five and seven parameters is rewritten as optimization problems with constraint functions and it is solved by using a firefly algorithm optimization. The firefly algorithm is a nature-inspired stochastic optimization algorithm, and able to solve modern global optimization for nonlinear and complex system, based on the flashing patterns and behavior of firefly's swarm. Moreover, this paper develops a unique solar cell modelling approach that incorporates search and optimization techniques for the determination of equivalent circuit parameters of RTC France Company mono-crystalline silicon solar cell single and double diodes at 33°C and 1000W/m2 from experimental current-voltage. The statistical errors are used to verify the accuracy of the results. Finally, accuracy of the extracted parameters is verified by comparing the current-voltage curve generated from simulation with those provided by determined experimentally and with different recent algorithms