In this work an algorithm to control the power flow of an electric power system with two integrated energy storage systems is investigated. The power system under consideration consists of a conventional distribution ...
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ISBN:
(纸本)9781509041695
In this work an algorithm to control the power flow of an electric power system with two integrated energy storage systems is investigated. The power system under consideration consists of a conventional distribution feeder that supplies the power to satisfy the customers' demand, a set of photovoltaic (PV) panels that also contribute to the power generation, one unit of Lithium-Ion battery storage for the intra-day use and a combined power-to-gas (PtG) and gas-to-power installation that converts the power excess in the summertime into hydrogen and injects power back to the system in the wintertime. The proposed control algorithm is based on model predictive control tailored for the energy system under investigation. To demonstrate the performance of the proposed control, a set of synthetic PV and demand profiles representing future conditions in Switzerland were created and used as input data to the system model. The synthesized generation and consumption data span a whole year of operation. A number of detailed simulations performed in the framework of the study reported in this paper demonstrate the effectiveness of the proposed control algorithm and provided invaluable insights into the optimum operation of the complex integrated power system.
Safety analysis and design optimization depend on the accurate prediction of various reactor attributes. Predictions can be enhanced by reducing the uncertainty associated with the attributes of interest. An inverse p...
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Safety analysis and design optimization depend on the accurate prediction of various reactor attributes. Predictions can be enhanced by reducing the uncertainty associated with the attributes of interest. An inverse problem can be defined and solved to assess the sources of uncertainty, and experimental effort can be subsequently directed to further improve the uncertainty associated with these sources. In this work a subspace-based algorithm for inverse sensitivity/uncertainty quantification (IS/UQ) has been developed to enable analysts account for all sources of nuclear data uncertainties in support of target accuracy assessment-type analysis. An approximate analytical solution of the optimization problem is used to guide the search for the dominant uncertainty subspace. By limiting the search to a subspace, the degrees of freedom available for the optimization search are significantly reduced. A quarter PWR fuel assembly is modeled and the accuracy of the multiplication factor and the fission reaction rate are used as reactor attributes whose uncertainties are to be reduced. Numerical experiments are used to demonstrate the computational efficiency of the proposed algorithm. Our ongoing work is focusing on extending the proposed algorithm to account for various forms of feedback, e.g., thermal-hydraulics and depletion effects.
Nonlinear Muskingum models are important tools in hydrological forecasting. In this paper, we have come up with a class of new discretization schemes including a parameter theta to approximate the nonlinear Muskingum ...
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Nonlinear Muskingum models are important tools in hydrological forecasting. In this paper, we have come up with a class of new discretization schemes including a parameter theta to approximate the nonlinear Muskingum model based on general trapezoid formulas. The accuracy of these schemes is second order, if theta not equal 1/3, but interestingly when theta = 1/3, the accuracy of the presented scheme gets improved to third order. Then, the present schemes are transformed into an unconstrained optimization problem which can be solved by a hybrid invasive weed optimization (HIWO) algorithm. Finally, a numerical example is provided to illustrate the effectiveness of the present methods. The numerical results substantiate the fact that the presented methods have better precision in estimating the parameters of nonlinear Muskingum models.
The authors propose a new stereo matching algorithm based on an iterative optimisation framework including bi-cubic B-spline surface fitting and accelerated region belief propagation (BP). They first compute the initi...
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The authors propose a new stereo matching algorithm based on an iterative optimisation framework including bi-cubic B-spline surface fitting and accelerated region belief propagation (BP). They first compute the initial cost and disparity map by the adaptive support-weight approach and then launch the iterative process in which the disparity space image is refined via the bi-cubic B-spline fitting and optimised via the accelerated region BP. Two innovations are contained in the algorithm: (i) disparity space image refinement based on segmented bi-cubic B-spline surface fitting;and (ii) an accelerated region message passing approach for BP. The algorithm is verified on the Middlebury benchmark and experimental results show the algorithm is effective and achieves the state-of-the-art accuracy.
Demand response (DR) is an effective method to lower peak-to-average ratio of demand, facilitate the integration of renewable resources (e.g., wind and solar) and plug-in hybrid electric vehicles, and strengthen the r...
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Demand response (DR) is an effective method to lower peak-to-average ratio of demand, facilitate the integration of renewable resources (e.g., wind and solar) and plug-in hybrid electric vehicles, and strengthen the reliability of power system. In smart grid, implementing DR through home energy management system (HEMS) in residential sector has a great significance. However, an algorithm that only optimally controls parts of HEMS rather than the overall system cannot obtain the best results. In addition, single objective optimization algorithm that minimizes electricity cost cannot quantify user's comfort level and cannot take a tradeoff between electricity cost and comfort level conveniently. To tackle these problems, this paper proposes a framework of HEMS that consists of grid, load, renewable resource (i.e., solar resource), and battery. In this framework, a user has the ability to sell electricity to utility grid for revenue. Different comfort level indicators are proposed for different home appliances according to their characteristics and user preferences. Based on these comfort level indicators, this paper proposes a multiobjective optimization algorithm for HEMS that minimizes electricity cost and maximizes user's comfort level simultaneously. Simulation results indicate that the algorithm can reduce user's electricity cost significantly, ensure user's comfort level, and take a tradeoff between the cost and comfort level conveniently.
[...]modern sensor network applications pose increasingly complex and stringent performance requirements on localization in terms of scalability, robustness, and accuracy. [...]many new location-related issues such a...
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[...]modern sensor network applications pose increasingly complex and stringent performance requirements on localization in terms of scalability, robustness, and accuracy. [...]many new location-related issues such as security and privacy in WSNs are rapidly emerging and must be carefully considered and addressed. [...]we hope that the papers published in this special issue would be helpful to other researchers with a common interest in these location-related topics.
Graph partitioning is required for solving tasks on graphs that need to be distributed over disks or computers. This problem is well studied, but the majority of the results on this subject are not suitable for proces...
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Graph partitioning is required for solving tasks on graphs that need to be distributed over disks or computers. This problem is well studied, but the majority of the results on this subject are not suitable for processing graphs with billions of nodes on commodity clusters, since they require shared memory or lowlatency messaging. One of the approaches suitable for cluster computing is the balanced label propagation, which is based on the label propagation algorithm. In this work, we show how multi-level optimization can be used to improve quality of the partitioning obtained by means of the balanced label propagation algorithm.
The phase error caused by the speed mismatch issue is researched in the line-scan images capturing 3D profile measurement. The experimental system is constructed by a line-scan CCD camera, an object moving device, a d...
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The phase error caused by the speed mismatch issue is researched in the line-scan images capturing 3D profile measurement. The experimental system is constructed by a line-scan CCD camera, an object moving device, a digital fringe pattern projector, and a personal computer. In the experiment procedure, the detected object is moving relative to the image capturing system by using a motorized translation stage in a stable velocity. The digital fringe pattern is projected onto the detected object, and then the deformed patterns are captured and recorded in the computer. The object surface profile can be calculated by the Fourier transform profilometry. However, the moving speed mismatch error will still exist in most of the engineering application occasion even after an image system calibration. When the moving speed of the detected object is faster than the expected value, the captured image will be compressed in the moving direction of the detected object. In order to overcome this kind of measurement error, an image recovering algorithm is proposed to reconstruct the original compressed image. Thus, the phase values can be extracted much more accurately by the reconstructed images. And then, the phase error distribution caused by the speed mismatch is analyzed by the simulation and experimental methods.
Extreme learning machine (ELM) has been well recognized as an effective learning algorithm with extremely fast learning speed and high generalization performance. However, to deal with the regression applications invo...
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Extreme learning machine (ELM) has been well recognized as an effective learning algorithm with extremely fast learning speed and high generalization performance. However, to deal with the regression applications involving big data, the stability and accuracy of ELM shall be further enhanced. In this paper, a new hybrid machine learning method called robust AdaBoost. RT based ensemble ELM (RAE-ELM) for regression problems is proposed, which combined ELM with the novel robust AdaBoost. RT algorithm to achieve better approximation accuracy than using only single ELM network. The robust threshold for each weak learner will be adaptive according to the weak learner's performance on the corresponding problem dataset. Therefore, RAE-ELM could output the final hypotheses in optimally weighted ensemble of weak learners. On the other hand, ELM is a quick learner with high regression performance, which makes it a good candidate of "weak" learners. We prove that the empirical error of the RAE-ELM is within a significantly superior bound. The experimental verification has shown that the proposed RAE-ELM outperforms other state-of-the-art algorithms on many real-world regression problems.
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