Optimizing PEMFC model parameters using ant lion optimizer and dragonfly algorithm: A comparative study

This paper introduced two optimization algorithms which are Ant Lion Optimizer (ALO) and Dragonfly Algorithm (DA) for extracting the Proton Exchange Membrane Fuel Cell (PEMFC) polarization curve parameters. The results produced by both algorithms are being compared to observe their performance. As a results, the ALO shows great performance compared to DA. Furthermore, these results also being compared with the results of the other reported metaheuristics algorithms. The ALO and DA presented competitive results

Tree Growth Algorithm for Parameter Identification of Proton Exchange Membrane Fuel Cell Models

Demonstrating an accurate mathematical model is a mandatory issue for realistic simulation, optimization and performance evaluation of proton exchange membrane fuel cells (PEMFCs). The main goal of this study is to demonstrate a precise mathematical model of PEMFCs through estimating the optimal values of the unknown parameters of these cells. In this paper, an efficient optimization technique, namely, Tree Growth Algorithm (TGA) is applied for extracting the optimal parameters of different PEMFC stacks. The total of the squared deviations (TSD) between the experimentally measured data and the estimated ones is adopted as the objective function. The effectiveness of the developed parameter identification algorithm is validated through four case studies of commercial PEMFC stacks under various operating conditions. Moreover, comprehensive comparisons with other optimization algorithms under the same study cases are demonstrated. Statistical analysis is presented to evaluate the accuracy and reliability of the developed algorithm in solving the studied optimization problem

An efficient parameter estimation algorithm for proton exchange membrane fuel cells

The proton exchange membrane fuel cell (PEMFC) is a favorable renewable energy source to overcome environmental pollution and save electricity. However, the mathematical model of the PEMFC contains some unknown parameters which have to be accurately estimated to build an accurate PEMFC model; this problem is known as the parameter estimation of PEMFC and belongs to the optimization problem. Although this problem belongs to the optimization problem, not all optimization algorithms are suitable to solve it because it is a nonlinear and complex problem. Therefore, in this paper, a new optimization algorithm known as the artificial gorilla troops optimizer (GTO), which simulates the collective intelligence of gorilla troops in nature, is adapted for estimating this problem. However, the GTO is suffering from local optima and low convergence speed problems, so a modification based on replacing its exploitation operator with a new one, relating the exploration and exploitation according to the population diversity in the current iteration, has been performed to improve the exploitation operator in addition to the exploration one. This modified variant, named the modified GTO (MGTO), has been applied for estimating the unknown parameters of three PEMFC stacks, 250 W stack, BCS-500W stack, and SR-12 stack, used widely in the literature, based on minimizing the error between the measured and estimated data points as the objective function. The outcomes obtained by applying the GTO and MGTO on those PEMFC stacks have been extensively compared with those of eight well-known optimization algorithms using various performance analyses, best, average, worst, standard deviation (SD), CPU time, mean absolute percentage error (MAPE), and mean absolute error (MAE), in addition to the Wilcoxon rank-sum test, to show which one is the best for solving this problem. The experimental findings show that MGTO is the best for all performance metrics, but CPU time is competitive among all algorithms

Semi-steady-state jaya algorithm for optimization

The Jaya algorithm is arguably one of the fastest-emerging metaheuristics amongst the newest members of the evolutionary computation family. The present paper proposes a new, improved Jaya algorithm by modifying the update strategies of the best and the worst members in the population. Simulation results on a twelve-function benchmark test-suite and a real-world problem show that the proposed strategy produces results that are better and faster in the majority of cases. Statistical tests of significance are used to validate the performance improvement

Optimal design of stand-alone solutions based on RES + hydrogen storage feeding off-grid communities

4openopenMarocco, Paolo; Ferrero, Domenico; Lanzini, Andrea; Santarelli, MassimoMarocco, Paolo; Ferrero, Domenico; Lanzini, Andrea; Santarelli, Massim

Identification of Linear / Nonlinear Systems via the Coyote Optimization Algorithm (COA)

Classical techniques used in system identification, like the basic least mean square method (LMS) and its other forms; suffer from instability problems and convergence to a locally optimal solution instead of a global solution. These problems can be reduced by applying optimization techniques inspired by nature. This paper applies the Coyote optimization algorithm (COA) to identify linear or nonlinear systems. In the case of linear systems identification, the infinite impulse response (IIR) filter is used to constitute the plants. In this work, COA algorithm is applied to identify different plants, and its performance is investigated and compared to that based on particle swarm optimization algorithm (PSOA), which is considered as one of the simplest and most popular optimization algorithms. The performance is investigated for different cases including same order and reduced-order filter models. The acquired results illustrate the ability of the COA algorithm to obtain the lowest error between the proposed IIR filter and the actual system in most cases. Also, a statistical analysis is performed for the two algorithms. Also, the COA is used to optimize the identification process of nonlinear systems based on Hammerstein models. For this purpose, COA is used to determine the parameters of the Hammerstein models of two different examples, which were identified in the literature using other algorithms. For more investigation, the fulfillment of the COA is compared to that of some other competitive heuristic algorithms. Most of the results prove the effectiveness of COA in system identification problems

Optimal Allocation Of Distributed Renewable Energy Sources In Power Distribution Networks

In this dissertation study, various methods for optimum allocation of renewable distributed generators (DGs) in both balanced and unbalanced distribution networks have been proposed, developed, and tested. These methods were developed with an objective of maximizing several advantages of DG integration into the current distribution system infrastructure. The first method addressed the optimal sitting and sizing of DGs for minimum distribution power losses and maximum voltage profile improvement of distribution feeders. The proposed method was validated by comparing the results of a balanced distribution system with those reported in the literature. This method was then implemented in a co-simulation environment with Electric Power Research Institute\u27s (EPRI) OpenDSS program to solve a three phase optimal power flow (TOPF) problem for optimal location and sizing of multiple DGs in an unbalanced IEEE-123 node distribution network. The results from this work showed that the better loss reduction can be achieved in less computational time compared to the repeated load flow method. The second and third methods were developed with the goal of maximizing the reliability of distribution networks by optimally sitting and sizing DGs and reclosers in a distribution network. The second method focused on optimal allocation of DGs and reclosers with an objective of improving reliability indices while the third method demonstrated the cost based reliability evaluation. These methods were first verified by comparing the results obtained in a balanced network with those reported in literature and then implemented on a multi-phase unbalanced network. Results indicated that optimizing reclosers and DGs based on the reliability indices increases the total cost incurred by utilities. Likewise, when reclosers and DG were allocated to reduce the total cost, the reliability of the distribution system decreased. The fourth method was developed to reduce the total cost incurred by utilities while integrating DGs in a distribution network. Various significant issues like capital cost, operation and maintenance cost, customer service interruption cost, cost of the power purchased from fossil fuel based power plants, savings due to the reduction in distribution power losses, and savings on pollutant emissions were included in this method. Results indicated that integrating DGs to meet the projected growth in demand provides the maximum return on the investment. Additionally, during this project work an equivalent circuit model of a 1.2 kW PEM fuel cell was also developed and verified using electro impedance spectroscopy. The proposed model behaved similar to the actual fuel cell performance under similar loading conditions. Furthermore, an electrical interface between the geothermal power plant and an electric gird was also developed and simulated. The developed model successfully eliminated major issues that might cause instability in the power grid. Furthermore, a case study on the evaluation of geothermal potential has been presented

Metaheuristics algorithms to identify nonlinear Hammerstein model: A decade survey

Metaheuristics have been acknowledged as an effective solution for many difficult issues related to optimization. The metaheuristics, especially swarm’s intelligence and evolutionary computing algorithms, have gained popularity within a short time over the past two decades. Various metaheuristics algorithms are being introduced on an annual basis and applications that are more new are gradually being discovered. This paper presents a survey for the years 2011-2021 on multiple metaheuristics algorithms, particularly swarm and evolutionary algorithms, to identify a nonlinear block-oriented model called the Hammerstein model, mainly because such model has garnered much interest amidst researchers to identify nonlinear systems. Besides introducing a complete survey on the various population-based algorithms to identify the Hammerstein model, this paper also investigated some empirically verified actual process plants results. As such, this article serves as a guideline on the fundamentals of identifying nonlinear block-oriented models for new practitioners, apart from presenting a comprehensive summary of cutting-edge trends within the context of this topic area

Optimisation of stand-alone hydrogen-based renewable energy systems using intelligent techniques

Wind and solar irradiance are promising renewable alternatives to fossil fuels due to their availability and topological advantages for local power generation. However, their intermittent and unpredictable nature limits their integration into energy markets. Fortunately, these disadvantages can be partially overcome by using them in combination with energy storage and back-up units. However, the increased complexity of such systems relative to single energy systems makes an optimal sizing method and appropriate Power Management Strategy (PMS) research priorities. This thesis contributes to the design and integration of stand-alone hybrid renewable energy systems by proposing methodologies to optimise the sizing and operation of hydrogen-based systems. These include using intelligent techniques such as Genetic Algorithm (GA), Particle Swarm Optimisation (PSO) and Neural Networks (NNs). Three design aspects: component sizing, renewables forecasting, and operation coordination, have been investigated. The thesis includes a series of four journal articles. The first article introduced a multi-objective sizing methodology to optimise standalone, hydrogen-based systems using GA. The sizing method was developed to calculate the optimum capacities of system components that underpin appropriate compromise between investment, renewables penetration and environmental footprint. The system reliability was assessed using the Loss of Power Supply Probability (LPSP) for which a novel modification was introduced to account for load losses during transient start-up times for the back-ups. The second article investigated the factors that may influence the accuracy of NNs when applied to forecasting short-term renewable energy. That study involved two NNs: Feedforward, and Radial Basis Function in an investigation of the effect of the type, span and resolution of training data, and the length of training pattern, on shortterm wind speed prediction accuracy. The impact of forecasting error on estimating the available wind power was also evaluated for a commercially available wind turbine. The third article experimentally validated the concept of a NN-based (predictive) PMS. A lab-scale (stand-alone) hybrid energy system, which consisted of: an emulated renewable power source, battery bank, and hydrogen fuel cell coupled with metal hydride storage, satisfied the dynamic load demand. The overall power flow of the constructed system was controlled by a NN-based PMS which was implemented using MATLAB and LabVIEW software. The effects of several control parameters, which are either hardware dependent or affect the predictive algorithm, on system performance was investigated under the predictive PMS, this was benchmarked against a rulebased (non-intelligent) strategy. The fourth article investigated the potential impact of NN-based PMS on the economic and operational characteristics of such hybrid systems. That study benchmarked a rule-based PMS to its (predictive) counterpart. In addition, the effect of real-time fuel cell optimisation using PSO, when applied in the context of predictive PMS was also investigated. The comparative analysis was based on deriving the cost of energy, life cycle emissions, renewables penetration, and duty cycles of fuel cell and electrolyser units. The effects of other parameters such the LPSP level, prediction accuracy were also investigated. The developed techniques outperformed traditional approaches by drawing upon complex artificial intelligence models. The research could underpin cost-effective, reliable power supplies to remote communities as well as reducing the dependence on fossil fuels and the associated environmental footprint

Optimal Design of Photovoltaic, Biomass, Fuel Cell, Hydrogen Tank Units and Electrolyzer Hybrid System for a Remote Area in Egypt

In this paper, a new isolated hybrid system is simulated and analyzed to obtain the optimal sizing and meet the electricity demand with cost improvement for servicing a small remote area with a peak load of 420 kW. The major configuration of this hybrid system is Photovoltaic (PV) modules, Biomass gasifier (BG), Electrolyzer units, Hydrogen Tank units (HT), and Fuel Cell (FC) system. A recent optimization algorithm, namely Mayfly Optimization Algorithm (MOA) is utilized to ensure that all load demand is met at the lowest energy cost (EC) and minimize the greenhouse gas (GHG) emissions of the proposed system. The MOA is selected as it collects the main merits of swarm intelligence and evolutionary algorithms; hence it has good convergence characteristics. To ensure the superiority of the selected MOA, the obtained results are compared with other well-known optimization algorithms, namely Sooty Tern Optimization Algorithm (STOA), Whale Optimization Algorithm (WOA), and Sine Cosine Algorithm (SCA). The results reveal that the suggested MOA achieves the best system design, achieving a stable convergence characteristic after 44 iterations. MOA yielded the best EC with 0.2106533 $/kWh, the net present cost (NPC) with 6,170,134$, the loss of power supply probability (LPSP) with 0.05993%, and GHG with 792.534 t/y