Current Issue : October - December Volume : 2017 Issue Number : 4 Articles : 5 Articles
Lithium ion (Li-ion) batteries work as the basic energy storage components in modern\nrailway systems, hence estimating and improving battery efficiency is a critical issue in optimizing\nthe energy usage strategy. However, it is difficult to estimate the efficiency of lithium ion\nbatteries accurately since it varies continuously under working conditions and is unmeasurable via\nexperiments. This paper offers a learning-based simulation method that employs experimental data\nto estimate the continuous-time energy efficiency and coulombic efficiency of lithium ion batteries,\ntaking lithium titanate batteries as an example. The state of charge (SOC) regions and discharge\ncurrent rates are considered as the main variables that may affect the efficiencies. Over eight million\nempirical datasets are collected during a series of experiments performed to investigate the efficiency\nvariation. A back propagation (BP) neural network efficiency estimation and simulation model is\nproposed to estimate the continuous-time energy efficiency and coulombic efficiency. The empirical\ndata collected in the experiments are used to train the BP network model, which reveals a test error of\n10âË?â??4. With the input of continuous SOC regions and discharge currents, continuous-time efficiency\ncan be estimated by the trained BP network model. The estimated and simulated result is proven to\nbe consistent with the experimental results....
The energy loss of the power grid is one of the key factors affecting the economic\noperation of power systems. How to calculate the electric energy consumption\naccurately will have a great influence on the planning, operation\nand management of the power grid. Currently there is a mountain of theoretical\nmethods to calculate the line loss of the power system. However, these\nmethods have some limitation, such as less considering the volatility of wind\npower resources. This paper presents an improved method to calculate the\nenergy loss of wind power generation, considering the fluctuations of wind\npower generation. First, data are collected to obtain the curve of the typical\ndaily expected output of wind farms for one month. Second, the curve of the\ntypical daily expected output are corrected by the average electricity and the\nshape factor to obtain the curve of the typical daily equivalent output of wind\nfarms for one month. Finally, the power flow is calculated by using typical\ndaily equivalent output curve to describe the energy loss for one month. The\nresults in the 110 kV main network show that the method is feasible....
This paper will propose an approach to calculate and evaluate the reserve capacity\nand energy size of Pumping-Hydro Combined Energy Storage (PHCES)\nwhen wind power is integrated to power grid while considering the scheme of\ngeneration capacity allocation and operation of PHCES. This approach will\nuse Monte Carlo Method to simulate large amount of samples to obtain the\nminimum value of capacity and energy size that could satisfy the requirement\nof system reliability. Finally this approach will apply in a RBTS system to assess\nthe project feasibility....
This work presents a methodology for optimizing the layout and geometry of an m Ã?â?? n\nhigh power (HP) light emitting diode (LED) luminaire. Two simulators are used to analyze an LED\nluminaire model. The first simulator uses the finite element method (FEM) to analyze the thermal\ndissipation, and the second simulator uses the ray tracing method for lighting analysis. The thermal\nand lighting analysis of the luminaire model is validated with an error of less than 10%. The goal of\nthe optimization process is to find a solution that satisfies both thermal dissipation and light efficiency.\nThe optimization goal is to keep the LED temperature at an acceptable level while still obtaining\nuniform illumination on a target plane. Even though no optical accessories or active cooling systems\nare used in the model, the results demonstrate that it is possible to obtain satisfactory results even\nwith a limited number of parameters. The optimization results show that it is possible to design\nluminaires with 4, 6 and up to 8 HP-LEDs, keeping the LED temperature at about 100 ââ??¦C. However,\nthe best uniformity on a target plane was found by the heuristic algorithm....
This paper presents a multi-objective optimization procedure for bidirectional bulb turbine\nrunners which is completed using ANSYS Workbench. The optimization procedure is able to\ncheck many more geometries with less manual work. In the procedure, the initial blade shape is\nparameterized, the inlet and outlet angles (Ã?²1, Ã?²2), as well as the starting and ending wrap angles (Ã?¸1,\nÃ?¸2) for the five sections of the blade profile, are selected as design variables, and the optimization\ntarget is set to obtain the maximum of the overall efficiency for the ebb and flood turbine modes.\nFor the flow analysis, the ANSYS CFX code, with a SST (Shear Stress Transport) k-Ãâ?° turbulence\nmodel, has been used to evaluate the efficiency of the turbine. An efficient response surface model\nrelating the design parameters and the objective functions is obtained. The optimization strategy\nwas used to optimize a model bulb turbine runner. Model tests were carried out to validate the final\ndesigns and the design procedure. For the four-bladed turbine, the efficiency improvement is 5.5%\nin the ebb operation direction, and 2.9% in the flood operation direction, as well as 4.3% and 4.5%\nfor the three-bladed turbine. Numerical simulations were then performed to analyze the pressure\npulsation in the pressure and suction sides of the blade for the prototype turbine with optimal\nfour-bladed and three-bladed runners. The results show that the runner rotational frequency (fn) is\nthe dominant frequency of the pressure pulsations in the blades for ebb and flood turbine modes,\nand the gravitational effect, rather than rotor-stator interaction (RSI), plays an important role in a low\nhead horizontal axial turbine. The amplitudes of the pressure pulsations on the blade side facing the\nguide vanes varies little with the water head. However, the amplitudes of the pressure pulsations on\nthe blade side facing the diffusion tube linearly increase with the water head. These results could\nprovide valuable insight for reducing the pressure amplitudes in the bidirectional bulb turbine....
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