Guobin Yan; Shunlei Li
Abstract
Recently, the application of the intelligent parking lot (IPL) in the power market has been exponentially increasing to decrease the greenhouse gasses, the pollution, and to decrease the deviation cost of the energy production based on electric vehicles (EV). IPLs uses charge and discharge features of ...
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Recently, the application of the intelligent parking lot (IPL) in the power market has been exponentially increasing to decrease the greenhouse gasses, the pollution, and to decrease the deviation cost of the energy production based on electric vehicles (EV). IPLs uses charge and discharge features of EVs to exchange the energy in the upstream grid. This paper study on a new interval-analysis based optimal solution of an IPL by considering the interval uncertainties for the price of upstream gird value. The method based on using an interval-based particle swarm optimization algorithm to optimize an interval objective function with lower and upper limitations with a single-valued output. Simulation results of the presented procedure are compared with a deterministic mixed-integer linear programming to show its superiority. The results show that deviation cost has been decreased up to 10.74% while average cost has been raised into 5.17% which demonstrates the methods high performance in decreasing the average cost of IPL and the reliability of the intelligent parking lot in the presence of uncertainties derived from the upstream grid.
Caiyuan Xiao; Guiju Zhang
Abstract
This paper presents an analysis of improving the efficiency of a hybrid solid oxide fuel cell (SOFC) and a micro gas turbine (mGT) system. The main reason for using SOFC technology is the generation of its less harmful products with higher performance compared to the traditional power generation systems. ...
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This paper presents an analysis of improving the efficiency of a hybrid solid oxide fuel cell (SOFC) and a micro gas turbine (mGT) system. The main reason for using SOFC technology is the generation of its less harmful products with higher performance compared to the traditional power generation systems. In addition, the combination of the gas turbine can improve the SOFC system’s reliability. Due to the importance of SOFC systems degradation in the industry, using the optimized hybrid system to reduce SOFC degradation is a proper process. This study presents a new developed bio-inspired optimization technique based on the rhino herd algorithm. After validation of the method with some different bio-inspired methods, it is employed to optimal size selection of the gas turbine for the fuel cell system reliability. Simulation results show that using a larger size of the turbine gives a higher level of power to the SOFC. It also decreases the efficiency of the initial turbine and increases the initial capital investment.
Haoran Fu; Huahui Li; T. Ramayah
Abstract
A new methodology is suggested in this study for providing an optimum energy demand forecasting for the future projections. The paper presents an improved version of manta ray foraging optimizer (iMRFO) for giving an optimum and suitable forecasting model. The model designing has been done on Taiwan ...
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A new methodology is suggested in this study for providing an optimum energy demand forecasting for the future projections. The paper presents an improved version of manta ray foraging optimizer (iMRFO) for giving an optimum and suitable forecasting model. The model designing has been done on Taiwan as the case study. The optimized forecasting is performed based on three models, including linear, exponential, and quadratic models where their coefficients are optimized by the suggested iMRFO algorithm based on different affective factors containing yearly growth rate of the real GDP, yearly growth rate of the population, annual industry share in growth rate of GDP, annual rate of urbanization, and annual coal consumption. Simulation results showed that using the proposed -energy demand prediction technique based on iMRFO has higher accuracy and reliability prediction in the direction of the other compared methods from the literature, such as ACO, GA/PSO, basic MRFO-based, and multiple linear regression models. Two different scenarios have been measured for more analyzing the suggested method. The results finally show that energy intensity in Taiwan will decline in varying degrees based on both scenarios which indicates that additional growth of efficient strategies and actions is needed for ensuring that the target is accomplished.
Lin Yongxing; Si Yanru
Abstract
One of the most widely used metals in the world is the Iron. The world cost of iron ore is defineded by its supply and demand. Numerous variabes such as steel, scrap, oil, gold, interest rate, inflation rate, dollar value, and stock value affect the world price of iron ore. Therefore, for economic investment ...
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One of the most widely used metals in the world is the Iron. The world cost of iron ore is defineded by its supply and demand. Numerous variabes such as steel, scrap, oil, gold, interest rate, inflation rate, dollar value, and stock value affect the world price of iron ore. Therefore, for economic investment of iron ore, it should be forecasted precisely by the scientists to give a direction to the decision makers to make a proper decision for the society. Due to the multiplicity of effective parameters and the complexity of the relationships between the iron ore variables, artificial intelligence is the best idea for forecasting. In this paper, we utilized a new optimized version of Convolutional Neural Network (CNN) to facilitate this task. To do so, a modified version of the Search and Rescue (MSAR) optimization algorithm has been designed and used for optimizing the CNN for improving its training efficiency in forecasting the iron ore price volatilities. The method is then validated based on ten different variables. Finally, a comparison of the results with various state of the art techniques was carried out to show the suggested method effectiveness. The results showed that the suggested technique has the fittest results in comparison to the other newest techniques.
Huo Dongdong
Abstract
In this paper, a control approach for direct control of the wind turbine torque based on a doubly-fed induction generator using space vector modulation and matrix convertor control method is presented and also evaluated. The goal of many conventional torque control methods is creating a sinusoidal current ...
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In this paper, a control approach for direct control of the wind turbine torque based on a doubly-fed induction generator using space vector modulation and matrix convertor control method is presented and also evaluated. The goal of many conventional torque control methods is creating a sinusoidal current in input and output sides. In the conventional direct torque control method, despite the proper performance in transient and steady states, the switching frequency is not constant. However in this paper, using the estimative method and direct torque control method, a constant switching frequency is obtained. In addition, fast dynamic response and wind turbine control are provided in the proposed control method. This paper compares the theoretical and operational complexities of direct torque control using space vector modulation and matrix convertor method. In this paper, the direct torque control approach is simulated using matrix convertor and space vector modulation methods for a wind turbine based on a doubly-fed inductive generator and the results of simulations, using MATLAB-SIMULINK software, are discussed under different operational conditions and in terms of theoretical complexity, load current quality, dynamic response, sampling frequency, switching frequency and presence of resonance in the input filter.
Giorgos Jimenez
Abstract
Environment and financial matters provide careful attention to electric vehicles (EV) and economical power resource benefits. There is an answer suggestion for increasing the effect of this benefit that is using the Potential of electric vehicles. The capacity for electric vehicles needs getting ready ...
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Environment and financial matters provide careful attention to electric vehicles (EV) and economical power resource benefits. There is an answer suggestion for increasing the effect of this benefit that is using the Potential of electric vehicles. The capacity for electric vehicles needs getting ready for Smart Dispensation Systems (SDS). Demand response schemes, as an appropriate gadget using the potential of ratifier in the perfect organization of the framework, grants dynamic closeness in the system of control supporters implementation shift and these undertakings, in the essential situations, can grant the demand requirements decreasing, in a short time period. In the proposed paper, endeavors to give a multipurpose schematization of EV in view of the keen lattice maintainable resources, wear vulnerability brought about by unlimited resources and EVs, due to the demand response undertakings, EV cell accumulating structure, restrict the performing expenses and the amount of force system contamination, by improving methodology. Enhanced advancement calculation is used for dealing with the propelling issue. Working expenses dropped considerably additionally using financial pattern of the request reaction and vehicle charge/release and intelligent plan in the times that the heap is little. Viability of the suggested strategy is connected to 94 standard transport power framework.
Dong Sun
Abstract
One of the important and more challenging categories in the smart cities and IoT is to monitor the vehicles plate licenses. This system is a key factor in most of the traffic monitoring in the IoT based smart city applications. In this research, a method for plate license recognition based on optimal ...
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One of the important and more challenging categories in the smart cities and IoT is to monitor the vehicles plate licenses. This system is a key factor in most of the traffic monitoring in the IoT based smart city applications. In this research, a method for plate license recognition based on optimal training of the CNN is proposed. To do this, the configuration and the hyperparameters of the CNN were optimized by a new hybrid optimization including world cup optimizer, whale optimizer, and chaotic theory to obtain a better result with high convergence. Simulations are applied to the UFPR-ALPR dataset and are compared with six popular techniques in terms of accuracy and time. Experimental achievements indicated that the proposed method gives superiority toward the other comparative techniques and is an efficient method for vehicles plate licenses detection.
Roza Gholamin
Abstract
The combined heating, cooling, and power source (CCHP) system is a good tool for the optimal consumption of fossil fuel thermal energy. In CCHPs, the produced waste heat from the hot gases can be recycled for generating power, heat, and water cooling and oil in electrical power generation systems which ...
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The combined heating, cooling, and power source (CCHP) system is a good tool for the optimal consumption of fossil fuel thermal energy. In CCHPs, the produced waste heat from the hot gases can be recycled for generating power, heat, and water cooling and oil in electrical power generation systems which can improve the efficiency of the system to more than 85%. This study presents an optimum structure for the combined heating, cooling, and power source energy flow to decrease the power demand in a building in Yazd city, Iran. In this research, a developed version of collective animal behavior optimizer is introduced to develop the combined heating, cooling, and power source system efficiency compared to the separation generation system. Two different scenarios have been studied for analyzing system efficiency. In one scenario, a constant value (670 kW) was assumed to the capacity while the electric cooling (EC) to cool load ratio (CLR) is assumed variant in a determined range and at the other scenario, an opponent condition with 0.75 constant EC to CLR were assumed. Simulation achievements of the presented technique are put in comparison with standard balanced moth search optimizer and genetic algorithm to indicate the efficiency of the algorithm.
Majid Khayatnezhad
Abstract
Renewable energy technology is quickly developing in last decades due to the increasing attention of countries to sustainable and clean energy, and is constantly evolving in terms of technology. Nevertheless, there are obstacles to this case, such as rising costs and declining reliability due to the ...
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Renewable energy technology is quickly developing in last decades due to the increasing attention of countries to sustainable and clean energy, and is constantly evolving in terms of technology. Nevertheless, there are obstacles to this case, such as rising costs and declining reliability due to the volatility of renewable power resources. Renewable technologies with the aim of using one source to cover other weaknesses, is one way to overcome these obstacles. In the present paper, a technique for optimum sizing of the components in a hybrid renewable power system (HRPS) consists of PV panels, electrolyzer, fuel cell, wind turbines, and the converters has been studied with keeping the value of the Net Present Cost (NPC) minimum. For giving a more efficient power generation cost, the optimization is obtained by using an Adaptive version of Wildebeest Herd Optimizer (AWHO). The main profit of the suggested algorithm is to resolve the main disadvantages of the other metaheuristic from the literature, like reliability, convergence speed, premature convergence, and accuracy, as it is possible. The proposed system is performed to a real-world case study in Yantai, China. Simulation results are put in comparison with several latest methods.
Rolando Simoes
Abstract
The off-grid electricity production is a method of supplying energy to commercial, industrial, residential, and rural or remote regions, which is often the grid connecting is unfeasible because of its difficult regional location and the staggering transmitting cost. In cases like this, the application ...
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The off-grid electricity production is a method of supplying energy to commercial, industrial, residential, and rural or remote regions, which is often the grid connecting is unfeasible because of its difficult regional location and the staggering transmitting cost. In cases like this, the application of local energy help to develop these regions. However, there is always need to a diesel generator to increase the electricity reliability. Hence, in the new method, diesel generators (DG) are coupled with renewable energy techniques like solar photovoltaics which may also use an energy storage system (ESS). The main idea in this paper is to propose a new optimum form for a hybrid battery/PV/diesel generator/ energy storage tool to resolve the load demand in a distant region in Changsha of China. Three main objectives are considered for minimization: annualized system cost, load probability loss, and value of CO2 emissions. To reduce the complexity of the system, ε-constraint technique is used. Here, a new modified bio-inspired algorithm, which is Chaotic Thermal Exchange Optimization algorithm is also applied to solve the optimization problem. Simulation achievements of the proposed system were put in comparison with the achievements of two latest methods to indicate the method effectiveness. The achievements indicated that the total production for the suggested method, PSO-based method, and HOMER are achieved 44051 kWh/yr, 44532 kWh/yr, and 43560 kWh/yr, respectively.
Saleh Mobayen
Abstract
Several methods have been proposed for sale and payment mechanisms in electricity markets; but, appropriate evaluation of this mechanisms is so difficult. The offer cost minimization (OCM) has been presented previously for solving this problem which minimizes the total offer cost through the evaluation ...
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Several methods have been proposed for sale and payment mechanisms in electricity markets; but, appropriate evaluation of this mechanisms is so difficult. The offer cost minimization (OCM) has been presented previously for solving this problem which minimizes the total offer cost through the evaluation by locational marginal prices (LMPs). In recent years, payment cost minimization (PCM) method is suggested which directly minimizes the consumer payments and is more complicated than OCM in terms of framework and converting to single-level linearized optimization problem as well as computational burden. In the current study, a new meta-heuristic optimizer has been proposed for to PCM through solving the joint energy-reserve PCM problem. The achievements are put in comparison with conventional model based offer cost minimization through various studied cases.
Huahui Li; Haoran Fu
Abstract
A hybrid PV/battery/DG energy production system is configured and optimized in this study for powering a network of a remote rural of china. The target of system design is to minimize the system’s fuel costs subject to the load demanded (LD) and several limitations. Thus, the concept comprises ...
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A hybrid PV/battery/DG energy production system is configured and optimized in this study for powering a network of a remote rural of china. The target of system design is to minimize the system’s fuel costs subject to the load demanded (LD) and several limitations. Thus, the concept comprises a problem of optimization which has been solved by a new optimization method using the Sequential Quadratic Programming (SQP) and a modified version of Sparrow Search Optimizer (MSSO). The achievements of the suggested technique are evaluated in various seasons and also in weekends and weekdays to indicate their impact on the operating cost of the PV/Diesel BESS system. The achievements show that in summer and winter, the costs of weekday are lower toward costs of weekend fuel. Moreover, the fuel cost of summer is lower than the fuel costs of winter that is due to lower demand in summer and also the more summer radiation levels mean less usage of auxiliary sources.
Myo Thwin
Abstract
In this study, a new optimized version of multi-layer perceptron neural network has been used for modeling achieving an optimized configuration in biofuel production process. 25 semi-pilot fermentation runs are used to determine the best arrangement of percentage selection for the combined substrate ...
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In this study, a new optimized version of multi-layer perceptron neural network has been used for modeling achieving an optimized configuration in biofuel production process. 25 semi-pilot fermentation runs are used to determine the best arrangement of percentage selection for the combined substrate of rice bran, cow dung, paper waste, banana stem, and saw dust to develop the biogas generation process efficiency and speed. The neural network is optimized by an improved version of Monarch Butterfly Optimization (DMBO) algorithm and the results have been compared with basic MBO algorithm and GA based algorithm from literature to illustrate the algorithm capability.
Nasser Yousefi
Abstract
In this study, economic, environmental, and technical optimization of a hybrid Microturbine-Solid Oxide Fuel Cell (SOFC) system is performed in a full load for the distributed generated electricity. To achieve better results, a new modified metaheuristic, called Balanced Manta Ray Foraging Optimization ...
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In this study, economic, environmental, and technical optimization of a hybrid Microturbine-Solid Oxide Fuel Cell (SOFC) system is performed in a full load for the distributed generated electricity. To achieve better results, a new modified metaheuristic, called Balanced Manta Ray Foraging Optimization Algorithm is adopted for multi-objective optimization of the problem. The system has been thermodynamically modeled and the results validated the system efficiency by considering the available data from the reference. During the optimization, the decision variable values have been evaluated by considering the system constraints to achieve an optimal criterion for the cost and exergy efficiency objective functions. Also, the cost of environmental degradation penalty has been added to the system total cost. The effect of the fuel price, investment cost, and the system output power value on the system are taken into consideration. The results show that the most sensitive and the most significant design parameter of the system is the current density of the fuel cell where the accurate selection of it, has a big effect on forming a trade-off between the system cost and the efficiency.
Zumrat Druzhinin
Abstract
A new optimal hybrid solar/diesel/battery system was suggested in this paper for covering the load demand in a rural area in Taklamakan Desert located in China. The major idea is to design a new improved version of the Student Psychology Based Optimization Algorithm for minimizing the load probability ...
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A new optimal hybrid solar/diesel/battery system was suggested in this paper for covering the load demand in a rural area in Taklamakan Desert located in China. The major idea is to design a new improved version of the Student Psychology Based Optimization Algorithm for minimizing the load probability loss, the annualized system expense, and the value of the CO2 release. The study also used the ε-constraint method to make a single objective problem from the multi-objective problem. The final results are compared with two other methods from the literature to indicate the higher capability of the method. The study also applied sensitivity analysis on the εCO2, εLLP, and the load consumption to show proper results of the system against different variations.
Mohammad Mohammaditab
Abstract
This paper proposes a new methodology for optimal sizing of a combined cooling heating and power (CCHP) system with a gas turbine as its primary mover in a residential building in Xihu area in Hangzhou, Zhejiang, China. Due to the significant impact of four substantial parameters including energetic, ...
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This paper proposes a new methodology for optimal sizing of a combined cooling heating and power (CCHP) system with a gas turbine as its primary mover in a residential building in Xihu area in Hangzhou, Zhejiang, China. Due to the significant impact of four substantial parameters including energetic, exergetic, environmental, and economic (4E), their features are used during the gas engine size optimization of the CCHP system. The paper also proposes a new modified version of the Farmland Fertility Optimization (FFO) Algorithm to solve the sizing problem. The new algorithm is designed to resolve the premature convergence and the local optimum of the basic algorithm. The results of the proposed MFFO-based system compared with the GA-based system and the basic FFO-based system. Simulation results show that the optimal cost value for the MFFO, FFO, and the GA are 0.191, 0.191, and 0.192, respectively that are achieved after 25, 35, and 50 iterations. This shows the effectiveness of the proposed method in terms of convergence and accuracy.
Giorgos Jimenez
Abstract
This paper addresses one of the most important challenges in utilization of renewable energy source the design of a system that incorporates this type of energy sources, which is the size determination of system component. A meta-model of hybrid renewable power system is studied here which can include ...
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This paper addresses one of the most important challenges in utilization of renewable energy source the design of a system that incorporates this type of energy sources, which is the size determination of system component. A meta-model of hybrid renewable power system is studied here which can include many sorts of renewable and nonrenewable energy sources as well as energy storage. This system is then optimized in terms of component size to supply the load with minimum overall cost. In addition, a novel optimization algorithm is proposed here named modified marine predators optimization technique which addresses the shortcomings observed in classic and metaheuristic optimization algorithm which are slow convergence, local optima, and immature convergence. Moreover, a real-world case study in an isolated location is analyzed here and a hybrid renewable power system with diesel generator, PV panels, and battery and the proposed optimization algorithm are implemented to determine optimum component size. The results suggest that, due to high investment cost, only a battery with very small capacity is economically advisable. Utilization of the proposed methodology here can help the system designers and operators to increase renewable energy penetration with higher reliability and lower costs.
Antonella Pasternak
Abstract
The main idea of this paper is to multi-criteria optimal designing of a combined cooling, heat and power (CCHP) system in an industrial unit by considering cooling loads, electricity, and heating. Different scenarios such as selling scenarios, no-selling scenario, as well as the possibility of electricity ...
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The main idea of this paper is to multi-criteria optimal designing of a combined cooling, heat and power (CCHP) system in an industrial unit by considering cooling loads, electricity, and heating. Different scenarios such as selling scenarios, no-selling scenario, as well as the possibility of electricity selling with identical capacities of the gas engine have been utilized. Because of the complexity of this problem, a new developed metaheuristic methodology, called Balanced Tree Growth Algorithm (BTGA) is designed and utilized. Relative Annual Benefit (RAB) as a multi-criterion function along with a gas engine is utilized as the primary mover during the optimization. Final simulation indicate that the proposed approach has well results toward the method from the literature. The results also specified that however using of the proposed configuration gives suitable results for different scenarios, selling scenario is more profitable.
Hamid Asadi Bagal; Maryam Nasseri
Abstract
The required cold and heat are supplied by recycling the heat lost from the stimulus in combined cooling, heating, and power (CCHP) generation system. The present study proposes a new optimal arrangement for a CCHP system for annual dynamic simulation. This study uses CCHP system to design a separated ...
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The required cold and heat are supplied by recycling the heat lost from the stimulus in combined cooling, heating, and power (CCHP) generation system. The present study proposes a new optimal arrangement for a CCHP system for annual dynamic simulation. This study uses CCHP system to design a separated stand-alone generation system to provide higher effectiveness. The system is then improved by a new enhanced metaheuristic technique, namely supply-demand-based optimization algorithm to enhance the efficiency of the designed system. The method is implemented on a hospital and its achievements are put in comparison with several different newest optimization techniques. The achievements indicate that the electricity system purchased from the utility network and fuel consumption for the optimized combined cooling, heating, and power system in comparison with the system of separated generation provides a decreasing trend.
Achla Anderson
Abstract
A new technique is prsented in this paper for optimum design of a combined cooling heating and power (CCHP) system. The prime mover in this study is a gas turbine and this is designed for a rural area in Zhaoping County, Guangxi, China. The effectiveness of the method is assessed based on four main parameters: ...
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A new technique is prsented in this paper for optimum design of a combined cooling heating and power (CCHP) system. The prime mover in this study is a gas turbine and this is designed for a rural area in Zhaoping County, Guangxi, China. The effectiveness of the method is assessed based on four main parameters: exergetic, energetic, economic, and environmental features. A Modified Group Teaching Optimization Algorithm (MGTO)is utilized for achieving resuls with better accuracy and convergence. The achievements of the suggested MGTO-based technique are put in comparison with genetic optimizer-based method and improved owl optimization algorithm-based method to illustrate the method higher efficiency.
Antonella Pasternak; Charis Bresser
Abstract
A new Blackbox technique has been presented in the current paper for model estimation of the solid oxide fuel cells (SOFCs) for providing better results. The proposed method is based on a Hierarchical Radial Basis Function (HRBF). The presented method is then developed by a new modified metaheuristic, ...
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A new Blackbox technique has been presented in the current paper for model estimation of the solid oxide fuel cells (SOFCs) for providing better results. The proposed method is based on a Hierarchical Radial Basis Function (HRBF). The presented method is then developed by a new modified metaheuristic, called Developed Coronavirus Herd Immunity Algorithm. The suggested model has been named DCHIA-HRBF. The proposed model is then trained by some data and prepared for the identification and prediction. The model is then analyzed and were put in comparison with several latest techniques for validation of the efficiency of the technique. It is also verified by the empirical data to prove its validation with the real data. Simulation results specified that the suggested DCHIA-HRBF delivers high effectiveness as an identifier and prediction tool for the SOFCs.
Scott Mizzi
Abstract
This paper presents an optimal size selection of a hybrid renewable PV/DG/Battery system in a remote area in Maowusu Desert, China. The idea is to select optimal numbers of PV panels, DGs, and battery storage units by minimizing of the total annual cost of the hybrid system. The optimization of the problem ...
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This paper presents an optimal size selection of a hybrid renewable PV/DG/Battery system in a remote area in Maowusu Desert, China. The idea is to select optimal numbers of PV panels, DGs, and battery storage units by minimizing of the total annual cost of the hybrid system. The optimization of the problem is performed based on a new improved version of Mayfly Algorithm (IMA) which is introduced for improving the effectiveness of the optimization in terms of accuracy, convergence, and consistency. Simulation results of the suggested algorithm are compared with some different optimization algorithm to show the prominence of the method. The proposed method shows that the optimal numbers for the optimized system includes 28 PV panels, 88 battery units, and 1 DG unit. Final results show that utilizing the suggested hybrid system gives the best minimum operational cost of the system compared with others.
Andrea Settanni; Giorgos Jimenez
Abstract
In this paper, a new technical and economic analysis for a hybrid system of energy has been performed. The study presents a new procedure for optimal modeling of the hybrid renewable energy system (HRES). The main idea is to optimize the hybrid system configuration based on a multiple-criteria optimization, ...
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In this paper, a new technical and economic analysis for a hybrid system of energy has been performed. The study presents a new procedure for optimal modeling of the hybrid renewable energy system (HRES). The main idea is to optimize the hybrid system configuration based on a multiple-criteria optimization, including three objectives. To simplify the problem and turning it to a single objective problem of optimization, ε-constraints technique has been used. This optimization is done by minimizing the capital cost (CC) and maximizing the electrical power effectiveness and the power supply consistency. To provide a well solution, a new amended design of the rain optimizer has been employed. The method provided a pareto solution with three groups that can be selected based on the decision maker’s purpose.
Francis Abza
Abstract
The present study proposes a new optimal configuration of a combined cooling, heating, and power (CCHP) system for annual dynamic simulation. To provide higher efficiency from the CCHP system, a new enhanced bio-inspired algorithm, namely Amended Coyote Optimizer is designed and utilized. The proposed ...
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The present study proposes a new optimal configuration of a combined cooling, heating, and power (CCHP) system for annual dynamic simulation. To provide higher efficiency from the CCHP system, a new enhanced bio-inspired algorithm, namely Amended Coyote Optimizer is designed and utilized. The proposed optimal technique is then carried out to a commercial building in Tongchuan, China. The simulations of the suggested method are finally confirmed by the data achieved by the case study which is done to show the method efficacy. Simulation achievements showed that the suggested technique has better effectiveness to provide similar data with the real value.
