scholarly journals Economic and Ecological Design of Hybrid Renewable Energy Systems Based on a Developed IWO/BSA Algorithm

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 687
Author(s):  
Mohammed Kharrich ◽  
Salah Kamel ◽  
Rachid Ellaia ◽  
Mohammed Akherraz ◽  
Ali S. Alghamdi ◽  
...  
Keyword(s):  
Optimal Design ◽  
Wind Turbine ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Optimization Methods ◽  
Invasive Weed ◽  
Battery Capacity ◽  
Battery Systems

In this paper, an optimal design of a microgrid including four houses in Dakhla city (Morocco) is proposed. To make this study comprehensive and applicable to any hybrid system, each house has a different configuration of renewable energies. The configurations of these four houses are PV/wind turbine (WT)/biomass/battery, PV/biomass, PV/diesel/battery, and WT/diesel/battery systems. The comparison factor among these configurations is the cost of energy (COE), comparative index, where the load is different in the four houses. Otherwise, the main objective function is the minimization of the net present cost (NPC), subject to several operating constraints, the power loss, the power generated by the renewable sources (renewable fraction), and the availability. This objective function is achieved using a developed optimization algorithm. The main contribution of this paper is to propose and apply a new optimization technique for the optimal design of a microgrid considering different economic and ecological aspects. The developed optimization algorithm is based on the hybridization of two metaheuristic algorithms, the invasive weed optimization (IWO) and backtracking search algorithm (BSA), with the aim of collecting the advantages of both. The proposed hybrid optimization algorithm (IWO/BSA) is compared with the original two optimization methods (IWO and BSA) as well as other well-known optimization methods. The results indicate that PV/biomass and PV/diesel/battery systems have the best energy cost using the proposed IWO/BSA algorithm with 0.1184 $/kWh and 0.1354 $/kWh, respectively. The best system based on its LCOE factor is the PV/biomass which represents an NPC of 124,689 $, the size of this system is 349.55 m2 of PV area and the capacity of the biomass is 18.99 ton/year. The PV/diesel/battery option has also good results, with a system NPC of 142,233 $, the size of this system is about 391.39 m2 of PV area, rated power of diesel generator about 0.55 kW, and a battery capacity of 12.97 kWh. Otherwise, the proposed IWO/BSA has the best convergence in all cases. It is observed that the wind turbine generates more dumped power, and the PV system is highly suitable for the studied area.

scholarly journals Power Loss Calculation in 11-Level Cascaded H-Bridge Inverter using Harmony Search Optimized Switching Scheme

2019 ◽  
Vol 8 (6) ◽  
pp. 5200-5205
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
System Analysis ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Harmony Search ◽  
Multilevel Inverter ◽  
Total Power ◽  
Multi Objective Optimization ◽  
Multi Objective

Power loss is the most significant parameter in power system analysis and its adequate calculation directly effects the economic and technical evaluation. This paper aims to propose a multi-objective optimization algorithm which optimizes dc source magnitudes and switching angles to yield minimum THD in cascaded multilevel inverters. The optimization algorithm uses metaheuristic approach, namely Harmony Search algorithm. The effectiveness of the multi-objective algorithm has been tested with 11-level Cascaded H-Bridge Inverter with optimized DC voltage sources using MATLAB/Simulink. As the main objective of this research paper is to analyze total power loss, calculations of power loss are simplified using approximation of curves from datasheet values and experimental measurements. The simulation results, obtained using multi-objective optimization method, have been compared with basic SPWM, optimal minimization of THD, and it is confirmed that the multilevel inverter fired using multi- objective optimization technique has reduced power loss and minimum THD for a wide operating range of multilevel inverter.

scholarly journals Optimal design of symmetric switching CMOS inverter using symbiotic organisms search algorithm

2020 ◽  
Vol 10 (1) ◽  
pp. 171
Author(s):  
Nihad Dib ◽  
Umar Al-Sammarraie
Keyword(s):  
Optimal Design ◽  
High Speed ◽  
Search Algorithm ◽  
Fitness Function ◽  
Logic Gate ◽  
Optimization Methods ◽  
Optimization Method ◽  
Cmos Inverter ◽  
Symbiotic Organisms Search ◽  
Symbiotic Organisms

This paper investigates the optimal design of symmetric switching CMOS inverter using the Symbiotic Organisms Search (SOS) algorithm. SOS has been recently proposed as an effective evolutionary global optimization method that is inspired by the symbiotic interaction strategies between different organisms in an ecosystem. In SOS, the three common types of symbiotic relationships (mutualism, commensalism, and parasitism) are modeled using simple expressions, which are used to find the global minimum of the fitness function. Unlike other optimization methods, SOS has no parameters to be tuned, which makes it an attractive and easy-to-implement optimization method. Here, SOS is used to design a high speed symmetric switching CMOS inverter, which is considered the most fundamental logic gate. SOS results are compared to those obtained using several optimization methods, like particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), and other ones, available in the literature. It is shown that the SOS is a robust straight-forward evolutionary algorithm that can compete with other well-known advanced methods.

A Tabu-Gradient Search-Based Optimization Technique for Synthesis of Mechanisms

2004 ◽  
Author(s):  
Ahmad Smaili ◽  
Naji Atallah
Keyword(s):  
Tabu Search ◽  
Global Minimum ◽  
Short Term Memory ◽  
Search Algorithm ◽  
Random Search ◽  
Optimization Technique ◽  
Optimization Methods ◽  
Gradient Search ◽  
Optimum Synthesis ◽  
Timing Task

Mechanism synthesis requires the use of optimization methods to obtain approximate solution whenever the desired number of positions the mechanism is required to traverse exceeds a few (five in a 4R linkage). Deterministic gradient-based methods are usually impractical when used alone because they move in the direction of local minima. Random search methods on the other hand have a better chance of converging to a global minimum. This paper presents a tabu-gradient search based method for optimum synthesis of planar mechanisms. Using recency-based short-term memory strategy, tabu-search is initially used to find a solution near global minimum, followed by a gradient search to move the solution ever closer to the global minimum. A brief review of tabu search method is presented. Then, tabu-gradient search algorithm is applied to synthesize a four-bar mechanism for a 10-point path generation with prescribed timing task. As expected, Tabu-gradient base search resulted in a better solution with less number of iterations and shorter run-time.

Metaheuristic Optimization of Dual-Element Vertical Axis Wind Turbine Using Genetic Algorithm

2019 ◽  
Author(s):  
Sushrut Kumar ◽  
Priyam Gupta ◽  
Raj Kumar Singh
Keyword(s):  
Genetic Algorithm ◽  
Wind Turbine ◽  
Optimization Technique ◽  
Vertical Axis ◽  
Single Element ◽  
Vertical Axis Wind Turbine ◽  
Invasive Weed ◽  
Metaheuristic Optimization ◽  
Attached Flow ◽  
Dynamics Simulations

Abstract This paper presents a framework for the optimization of Dual-Element Vertical Axis Wind Turbine (VAWT) Blade configurations for improvement in power generation. Multi-element nature of the turbine was specifically chosen as this configuration offers better-attached flow over a conventional single element H-type turbine. The framework was based on a genetic evolutionary algorithm which is a metaheuristic optimization technique based on the principle of survival of the fittest. The class of genetic algorithm used was Invasive Weed Optimization. The geometry of the turbine consists of a rotor with three sets of dual-element airfoil oriented symmetrically. Effective chord length and relative chord angle were taken as modifying parameters for generating new configurations. The fitness of each individual was evaluated by performing two-dimensional Computational Fluid Dynamics Simulations. OpenFOAM was used for performing numerical simulations. Qualitative data of torque, pressure, velocity, and turbulence kinetic energy of best configuration is shown. A considerable increase in torque in the final geometry. The model was found ideal for optimizing multi-element VAWT configuration.

Application of Multi-Criteria Optimization to Large-Scale Structures Design

2014 ◽  
Vol 693 ◽  
pp. 171-176
Author(s):  
Milan Sága ◽  
Peter Pecháč ◽  
Lenka Jakubovičová
Keyword(s):  
Finite Element ◽  
Optimal Design ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Finite Element Analyses ◽  
Large Scale Structures ◽  
Whole Process ◽  
Deformation Stability ◽  
Scale Structures

The paper presents fundamental principles and application of the large-scale truss structure PKP25-20i optimal design based on a multi-criteria optimization algorithm. The multi-objective function contains conditions for deformation, stability and cumulative damage obtained by finite element analyses. The whole process was implemented and realized in special Matlab’s procedures and FEM software Cosmos/M.

A Differential Evolution-Based Inverse Method to Optimize Blade Configurations in Elliptical-Bladed Savonius Wind Turbines

2019 ◽  
Author(s):  
Nur Alom ◽  
Ranjan Das ◽  
Ujjwal K. Saha
Keyword(s):  
Aspect Ratio ◽  
Differential Evolution ◽  
Wind Turbine ◽  
Objective Function ◽  
Wind Turbines ◽  
Search Algorithm ◽  
Chord Length ◽  
Operating Conditions ◽  
Design Variables ◽  
Overlap Ratio

Abstract It is well-known that elliptical-bladed Savonius wind turbine yields relatively better performance than conventionally used semicircular-bladed turbines. This is mainly due to lesser tip loss and delayed flow separation that allow the elliptical turbine to acquire higher rotational speeds than semicircular turbine under a given wind load. In this work, an experimentally-validated inverse analysis is done to determine the optimum blade configurations involving the chord length, turbine height, aspect ratio, and the necessary overlap ratio to derive a required power and torque from elliptical-bladed Savonius wind turbines. Due to obvious advantages of evolutionary metaheuristic optimizers in general, here differential evolution (DE) search algorithm is used to solve the inverse problem through a least-squares minimization of the relevant objective function. The objective function is further subjected to feasible bounds of the unknown design variables. The effects of blockage corrections are duly considered and the variations of the design variables along with the objective function are studied over a range of iterations of the DE algorithm. Through comprehensive analysis, it is highlighted from the present study that for harvesting a given performance, rotor swept area can be reduced by 6.25% with respect to the available experimental data under identical operating conditions of the wind turbine. Multiple blade configurations can be acquired, all of which invariably satisfy the required performance criterion. This study also highlights that amongst various dimensional parameters, turbine height and aspect ratio play more prominent role than chord length and overlap ratio and the blade chord influences only the torque but not markedly the power. The results obtained from the present work are proposed to facilitate the concerned designer to explore various feasible blade designs and determine the suitable one, thereby avoiding valuable time elapsed in repetitive fabrication and testing of various designs.

A Novel Machine-Learning Aided Optimization Technique for Material Design: Application in Thin Film Solar Cells

2016 ◽  
Author(s):  
Shima Hajimirza
Keyword(s):  
Thin Film ◽  
Machine Learning ◽  
Objective Function ◽  
Search Algorithm ◽  
Random Search ◽  
Optimization Technique ◽  
Global Search ◽  
Radiative Properties ◽  
Mcmc Methods ◽  
Predictive Tool

Patterned thin film structures can offer spectrally selective radiative properties that benefit many engineering applications including photovoltaic energy conversion at extremely efficient scales. Inverse design of such structures can be expressed as an interesting optimization problem with a specific regime of complexity; namely moderate number of optimization parameters but highly time-consuming forward problem. For problems like this, a search technique that can somehow learn and parameterize the multi-dimensional behavior of the objective function based on past search points can be extremely useful in guiding the global search algorithm and expediting the solution for such complexity regimes. Based on this idea, we have developed a novel search algorithm for optimizing absorption coefficient of visible light in a multi-layered silicon-based nano-scale thin film solar cell. The proposed optimization algorithm uses a machine-learning predictive tool called regression-tree in an intermediary step to learn (i.e. regress) the objective function based on a previous generation of random search points. The fitted model is then used as a guide to resample from a new generation of candidate solutions with a significantly higher average gain. This process can be repeated multiple times and better solutions are obtained with high likelihood at each stage. Through numerical experiments we demonstrate how in only one resampling stage, the propose technique dominates the state-of-the-art global search algorithms such as gradient based techniques or MCMC methods in the considered nano-design problem.

Development of an Optimization Design Method for Turbomachinery by Incorporating the Cooperative Coevolution Genetic Algorithm and Adaptive Approximate Model

2011 ◽  
Author(s):  
Peng Song ◽  
Jinju Sun ◽  
Ke Wang ◽  
Zhilong He
Keyword(s):  
Optimal Design ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Optimization Design ◽  
Design Method ◽  
Mathematical Optimization ◽  
Cryogenic Liquid ◽  
Design Solution ◽  
Cooperative Coevolution ◽  
The Real

An optimization design method is developed, which is motivated by the optimal design of a cryogenic liquid turbine (including an asymmetric volute, variable stager vane nozzles, shroud impeller and diffuser) for replacement of the Joule-Thompson throttling valve in the internal compression air-separation unit. The method involves mainly three elements: geometric parameterization, prediction of objective function, and mathematical optimization algorithm. Traditional parameterization approach is used for the geometry representation, while some novel work in the latter two aspects (i.e. objective function evaluation and optimization algorithm) is done to reduce the computing time and improve the optimization solution. A modified Cooperative Coevolution Genetic Algorithms (CCGA) is developed by incorporating a modified variable classification algorithm and some new self-adapted GA operators, which help to enhance the global search ability with an excessive number of optimization variables. Design of Experiment (DOE) is carried out to initialize the kriging approximation model, which is used to approximate the time-costly objective function. Then the CCGA is started, and once a potential superior individual is found, a decision will be made by the in-house code on whether or not it needs a updating. If required, the true objective function prediction based on the real model will be conducted and the obtained value of objective function will be used to update the kriging model. In such a way, the CCGA can complete its optimal searching with a limited number of real evaluations for objective function. All the above features are integrated into the optimization framework and encoded for the optimal turbine design. In addition, CFD software ANSYS CFX is used for the real objective function evaluations, and a well-organized batch code is developed by the authors for calling the CFD simulation which helps to promote this automation of the optimization process. For validation, the optimization method is used to solve some classical mathematical optimization problems and its effectiveness is demonstrated. The method is then used in the optimal design of the cryogenic liquid turbine stage, it is demonstrated that the optimal design method can help to reduce significantly the searching time for the optimal design and improve the design solution to the liquid turbine.

Finite Element Model Updating Based on Direct Optimization Technique

2010 ◽  
Vol 163-167 ◽  
pp. 2804-2810 ◽  
Author(s):  
Bei Dou Ding ◽  
Heng Lin Lv ◽  
Yong Sheng Ji
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Model Updating ◽  
Optimization Technique ◽  
Element Model ◽  
Mode Shapes ◽  
Fe Model ◽  
Direct Optimization

Setting up of an objective function, update parameters and use of robust optimization algorithm are three crucial steps in FE model updating. In order to calculate the gradient of the objective function, analytic optimization algorithm is not easy to be achieved, while the direct optimization algorithm may achieve the objective function optimization simply by comparing the size of the objective function to move the iteration point. In this paper, the eigenvalues and mode shapes are used as the optimization objective function, the direct optimization algorithm is adopted, an updated finite element model is achieved, and a numerical example is given.

Discrete-Continuous Configuration Optimization Methods for Structures Using the Harmony Search Algorithm

2006 ◽  
Vol 324-325 ◽  
pp. 1293-1296 ◽  
Author(s):  
K.S. Lee ◽  
Chang Sik Choi
Keyword(s):  
Search Algorithm ◽  
Random Search ◽  
Optimization Technique ◽  
Harmony Search ◽  
Optimization Methods ◽  
Harmony Search Algorithm ◽  
Perfect State ◽  
Gradient Search ◽  
Geometric Variables ◽  
Derivative Information

This paper proposes an efficient structural optimization methods based on the harmony search (HS) heuristic algorithm that treat integrated discrete sizing and continuous geometric variables. The recently developed HS algorithm was conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so derivative information is unnecessary. A benchmark truss example is presented to demonstrate the effectiveness and robustness of the new method, as compared to current optimization methods. The numerical results reveal that the proposed method is a powerful search and design optimization technique for structures with discrete member sizes, and may yield better solutions than those obtained using current methods.

Sign in / Sign up

Export Citation Format

Share Document