• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.742-751
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• 2018

Electric vehicles (EV) are emerging as the future transportation vehicle reflecting their potential safe environmental advantages. Vehicle to Grid (V2G) system describes the hybrid system in which the EV can communicate with the utility grid and the energy flows with insignificant effect between the utility grid and the EV. The paper presents an optimal power control and energy management strategy for Plug-In Electric Vehicle (PEV) charging stations using Wind-PV-FC-Battery renewable energy sources. The energy management optimization is structured and solved using Multi-Objective Particle Swarm Optimization (MOPSO) to determine and distribute at each time step the charging power among all accessible vehicles. The Model-Based Predictive (MPC) control strategy is used to plan PEV charging energy to increase the utilization of the wind, the FC and solar energy, decrease power taken from the power grid, and fulfil the charging power requirement of all vehicles. Desired features for EV battery chargers such as the near unity power factor with negligible harmonics for the ac source, well-regulated charging current for the battery, maximum output power, high efficiency, and high reliability are fully confirmed by the proposed solution.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.659-668
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• 2018

This paper proposes an optimization approach of a grid-connected photovoltaic and wind hybrid energy system including energy storage considering voltage fluctuation in the electricity grid. A techno-economic analysis is carried out in order to minimize the size of hybrid system by considering the benefit-cost. Lithium-ion battery type is used for both managing the electricity selling to the grid and reducing voltage fluctuation. A new technique is developed to limit the voltage perturbation caused by the solar irradiance and the wind speed through determining the state-of-charge of battery for every hour of a day. Improved particle swarm optimization (PSO) methods, referred to as FC-VACPSO which combines Fast Convergence Particle Swarm Optimization (FCPSO) method and Variable Acceleration Coefficient Based Particle Swarm Optimization (VACPSO) method are used to solve the optimization problem. A comparative study has been performed between standard PSO method and PSO based methods to extract the best size with the benefit cost. A sensitivity analysis has been studied for different kinds and costs of batteries, by considering variable and constant state-ofcharge of battery. The simulations, performed under Matlab environment, yield good results using the FC-VACPSO method regarding the convergence and the benefit cost of the hybrid system.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.26 no.12
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• pp.50-55
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• 2012

This paper presents an application of optimization method for efficient operation in micro grid. For operational efficiency, the objective function in a diesel generator consists of the fuel cost function similar to the cost functions used for the conventional fossil-fuel generating plants. The wind turbine generator is modeled by the characteristics of variable output. The cost function of fuel cell plant considers the efficiency of fuel cell. Particle swarm optimization(PSO) and sequential quadratic programming(SQP) are used for solving the problem of microgrid system operation. Also, from the results this paper presents the way to attend power markets which can buy and sell power from upper lever grids by connecting a various generation resources to micro grid.

• Journal of international Conference on Electrical Machines and Systems
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• v.2 no.3
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• pp.367-374
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• 2013

The electrical vehicles (EV) with vehicle-to-grid (V2G) capability can be used as loads, energy sources and energy storage in MicroGrid integrated with renewable energy sources. The output power of generators will be reallocated in the considering of V2G. An intelligent unit commitment (UC) with V2G for cost optimization is presented in this paper. A new constraint of UC with V2G is considered to satisfy daily use of EVs. A hybrid optimiza-tion algorithm combined Binary Particle Swarm Optimization (BPSO) with Lagrange Mul-tipliers Method (LMM) is proposed. The difference between results of UC with V2G and UC without V2G is presented.

• Journal of the Society of Naval Architects of Korea
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• v.54 no.1
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• pp.26-33
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• 2017

In this study, the surface marker method, one of the particle tracking methods, used to track the free surface is extended to cover the more general cases easily including the collision and separation of the free surface. In surface particle method to redistribute particles effectively using the grid system, the free surface is composed of the sum of quadrilaterals having four curves where fixed markers are placed at ends of each curve. Fixed markers are used to know how curves are connected to each other. The position of fixed markers can move as the free surface deforms but all fixed markers cannot be deleted during all time of simulation to keep informations of curve connection. In the case of the collision or separtion of the free surface where several curves can be intersected disorderly, severe difficulties can occur to define newly states of curve connection. In this study, the adaptable surface parTicle method without fixed markers is introduced. Intersection markers instead of the fixed markers are used to define quadrilaterals. The position of the intersection markers is defined to be the intersection point between the free surface and the edge of the grid and it can be added or deleted during the time of simulation to allow more flexibilities. To verify numerical schemes, two flow cases are simulated and the numerical results are compared with other's one and shown to be valid.

• Journal of the Korea Computer Graphics Society
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• v.14 no.4
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• pp.1-5
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• 2008

In this paper, we propose an efficient technique for improving the grid based fluid simulation by sub-grid visuals. The detailed turbulency generated efficiently by Vortex Particle Method are blended with the flow fields coming from the traditional incompressible Navier-Stokes solver. The algorithm enables large- and small- scale detail to be edited separately.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3367-3382
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• 2023

Smoothed Particle Hydrodynamics (SPH) is a Lagrangian computational fluid dynamics method that has been widely used in the analysis of physical phenomena characterized by large deformation or multi-phase flow analysis, including free surface. Despite the recent implementation of eddy-viscosity models in SPH methodology, sophisticated turbulent analysis using Lagrangian methodology has been limited due to the lack of computational performance and numerical consistency. In this study, we implement the standard and dynamic Smagorinsky model and dynamic Vreman model as sub-particle scale models based on a weakly compressible SPH solver. The large eddy simulation method is numerically identical to the spatial discretization method of smoothed particle dynamics, enabling the intuitive implementation of the turbulence model. Furthermore, there is no additional filtering process required for physical variables since the sub-grid scale filtering is inherently processed in the kernel interpolation. We simulate lid-driven flow under transition and turbulent conditions as a benchmark. The simulation results show that the dynamic Vreman model produces consistent results with experimental and numerical research regarding Reynolds averaged physical quantities and flow structure. Spectral analysis also confirms that it is possible to analyze turbulent eddies with a smaller length scale using the dynamic Vreman model with the same particle size.

• Nuclear Engineering and Technology
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• v.52 no.11
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• pp.2479-2490
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• 2020

We performed experiments to measure a single-phase upward flow in a 5 × 5 rod bundle with spacer grids using a particle image velocimetry, focusing on the crossflow. The Reynolds number based on the hydraulic diameter and the bulk velocity is 10,000. The ratio of pitch between rods and rod diameter is 1.4 and spacer grid is installed periodically. The turbulence in the rod bundle results from the combination of a forced mixing and natural mixing. The forced mixing by the spacer grid persists up to 10D_h from the spacer grid, while the natural mixing is attributed to the crossflow between adjacent subchannels. The combined effects contribute to a sinusoidal distribution of the time-averaged stream-wise velocity along the lateral direction, which is relatively weak right behind the spacer grid as well as in the gap. The streamwise and lateral turbulence intensities are stronger right behind the spacer grid and in the gap. Based on these findings, we newly defined a modified mixing coefficient as the ratio of the lateral turbulence intensity to the time-averaged streamwise velocity, which shows a spatial variation. Finally, we compared the developed model with the measured data, which shows a good agreement with each other.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.8
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• pp.1092-1097
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• 2014

In this paper, we propose the grid-based output control method for wind farm. To do this, we propose the output control method using the PSO(Particle Swarm Optimization) algorithm. Secondly, we propose the method for detecting the harmonics using STFT(Short-Time Fourier Transform) algorithm. And last, we propose the method for compensating the harmonics using neural network. Finally, we show the effectiveness and feasibility of the proposed method though some simulations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.4
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• pp.547-554
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• 2016

Analyze the customer daily load patterns, be used to determine the optimal charging and discharging schedule which can minimize the electrical charges through the battery energy storage system(BESS) installed in consumers is an object of this paper. BESS, which analyzes the load characteristics of customer and reduce the peak load, is essential for optimal charging and discharging scheduling to save electricity charges. This thesis proposes optimal charging and discharging scheduling method, using particle swarm optimization (PSO) and penalty function method, of BESS for reducing energy charge. Since PSO is a global optimization algorithm, best charging and discharging scheduling can be found effectively. In addition, penalty function method was combined with PSO in order to handle many constraint conditions. After analysing the load patterns of target BESS, PSO based on penalty function method was applied to get optimal charging and discharging schedule.