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.
