Papers by Keyword: Optimization Problem

Abstract: This paper addresses the problems of control and energy management in micro-grid with the incorporation of renewable energy generation, hybrid storage technologies, and the integration of the electric vehicles (EVs) with vehicle to grid (V2G) technology. The adaptive model predictive control (AMPC) technique is used to optimize the charge/discharge of the EVs in a receding horizon manner in order to reduce operational cost in a renewable energy-based micro-grid. V2G systems integration can be a crucial element in the assurance of network reliability against variability in loads. In this context, the paper presents an AMPC algorithm for the optimization of a micro-grid coupled with a V2G system consisting of six electric vehicle charging stations. The proposed algorithm effectively manages the use of renewable energy sources, vehicles charge, energy storage units, and the purchase and sale of electric power to the external network. Two scenarios are investigated in this paper to examine the performance of the proposed controller to manage the renewable energy sources in the micro-grid system. The first case uses a load shifting mechanism to solve the charge management problem during a known interval of parking time. The second case introduces the EVs with V2G capabilities when connected with the micro-grid. In this case, the vehicle battery collaborates with the ESS of the micro-grid to maximize costs benefits and mitigate the intermittency of renewable generation. Furthermore, other benefits of V2G concepts, such as voltage and frequency control for the micro-grid stability, are investigated. Therefore, it is evident from the obtained results that the proposed control algorithm was able to effectively manage the renewable energy sources, energy storage units, vehicles charge, and the purchase and sale of electric power with the grid. Keywords: Adaptive model predictive control, Energy management system, Electric vehicles, Vehicle to grid technology, Grid reliability, Load shifting, Optimization problem and MATLAB/Simulink.

Abstract: In this paper an inverse heat conduction problem for estimating the constant value of power of internal heat source during induction heating of paramagnetic material is developed. The experimentally measured temperature data are used as the input for the inverse heat transfer model. The problem is formulated as a problem of optimal control over an object with distributed parameters, the internal heat power is considered as the control action. The problem is reduced to a problem of mathematical programming; the special optimisation method based on the alternance properties of the sought optimum solutions is applied to estimate the internal heat power.

Abstract: Mae Sariang is remote area about 200 kilometers from main substation, which creates several problems of power system operation, specially losses and reliability. This paper studies the optimize operation and design sizing of lower reservoir and pump station for hydro pump energy storage in order to maximize profit. The system is modelled using GAMS. Data in analysis are based on load, power generation from solar farm, natural stream flowed profile in Mae Sariang area. From the simulation results, the optimized size of hydro pump storage should be 6.6 MW, the size of pump at 2.07 MW, the lower reservoir volume capacity at least 20,731 cubic meter. The maximum profit is 56.45 million baht per year and optimized operation results in winter season have the excess energy will be used to pump the water to upper reservoir. Summer season the power system has to import power from grid and lower profit more than other season. Rainy season the power system can use power from hydro pump system as hydro power plant without drawing from the grid and best profit more than other seasons. Natural stream is important to sensitivity analyze it is the important variables that greatest profit. If in upstream problems with little rainfall, forests were destroyed. They will have problem solve in the future.

Abstract: In the present work we propose the method for the solution inverse heat conduction problem that consists of the identification of unknown internal heat source function depending simultaneously on space and time variables. The source function can be represented as a separable variable. We consider an inverse problem of determining the heat source function based on temperature measurements.Inverse problem is formulated as an optimization problem, a variational formulation for solving the optimization problem is given. The estimation of internal heat source is investigated with the analytical method combined with the method for approximate modal definition of temperature state and source function. The temperature state and desired control input are represented in the form of finite expansion in terms of orthogonal system of eigenfunctions. The required eigencoefficients of temperature state can be determined using temperature measurements. Then eigencoefficients of heat source function can be defined sequentially.Some numerical examples are provided to show the efficiency of the proposed method.

Abstract: This paper develops an improved novel global harmony search (INGHS) algorithm for solving optimization problems. INGHS employs a novel method for generating new solution vectors that enhances accuracy and convergence rate of novel global harmony search (NGHS) algorithm. Simulations for five benchmark test functions show that INGHS possesses better ability to find the global optimum than that of harmony search (HS) algorithm. Compared with NGHS and HS, INGHS is better in terms of robustness and efficiency.

Abstract: The Genetic Algorithm (GA) is a stochastic global search method that mimics the metaphor of natural biological evolution. GA operates on a population of potential solutions applying the principle of survival of the fittest to produce (hopefully) better and better approximations to a solution. Genetic algorithms are particularly suitable for solving complex optimization problems and for applications that require adaptive problem solving strategies. Here, in this paper genetic algorithm is introduced as an optimization technique.

Abstract: In this paper, we propose an efficient parts surface defect detection method using SVM algorithm, and particle swarm optimization is used to make parameters selection for SVM. The proposed parts surface defect detection systems is made up of six parts, and the main ideas of our method lie in that we exploit computer vision and machine learning in the research field of mechanical manufacturing and automation. We convert the parts surface defect detection problem to the classification problem, and the images of parts surface are used as testing samples. The SVM algorithm regards the classification problem as the constrained optimization problem. The classification accuracy is determined by the quality of parameters selection. Hence, particle swarm optimization is exploited to make parameters selection for SVM by defining two fitness functions. Afterwards, the best particle of the current population and the gbest is obtained. Utilizing the output from the particle swarm optimization then the parameters for SVM can be obtained. Finally, experiments are conducted based on a dataset with 563 samples, and experimental results illustrate that the proposed is quite effective for parts surface defect detection.

Abstract: Various exact and heuristic methods have been proposed for assembly line balancing problem (ALBP) but unequal multiple operators have not been considered much. In this paper we present a novel approach of assembly line balancing Type-2 with unequal multiple operators by using an already developed code in Matlab (Tomlab modeling platform). The adopted approach can be applied at line balancing problems ranging from few to hundreds of work elements to achieve minimum cycle time with very less computational effort.

Abstract: Economic dispatch (ED) is a typical power system operation optimization problem. But it has non-smooth cost functions with equality and inequality constraints that make the problem of finding the global optimum difficult. According to the characteristics of economic dispatch problem, a improved algorithm based on particle swarm optimization for solving economic dispatch strategy is researched in this paper. Multi-objective economic\environmental dispatch demands that the pollutant emission of power plants should reach minimum while the condition of least generation cost should be satisfied. According to this demand, this multi-objective problem is solved by improved particle swarm optimization (PSO) algorithm. Using particle position and speed of change in the familiar update, the multi-objective particle swarm algorithm based on test function of this algorithm, and the simulation results of simulation optimization. The effectiveness of the proposed algorithm is verified by Simulation.

Abstract: Glowworm Swarm Optimization (GSO) algorithm is a derivative-free, meta-heuristic algorithm and mimicking the glow behavior of glowworms which can efficiently capture all the maximum multimodal function. Nevertheless, there are several weaknesses to locate the global optimum solution for instance low calculation accuracy, simply falling into the local optimum, convergence rate of success and slow speed to converge. This paper reviews the exposition of a new method of swarm intelligence in solving optimization problems using GSO. Recently the GSO algorithm was used simultaneously to find solutions of multimodal function optimization problem in various fields in today industry such as science, engineering, network and robotic. From the paper review, we could conclude that the basic GSO algorithm, GSO with modification or improvement and GSO with hybridization are considered by previous researchers in order to solve the optimization problem. However, based on the literature review, many researchers applied basic GSO algorithm in their research rather than others.