Voltage support to the distribution Node (DN) is necessary requirement due to fluctuations of time varying reactive load. The Photovoltaic's Arrays (PAs) and Electric Vehicles (EVs) provide active and reactive pow...
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ISBN:
(纸本)9781479964161
Voltage support to the distribution Node (DN) is necessary requirement due to fluctuations of time varying reactive load. The Photovoltaic's Arrays (PAs) and Electric Vehicles (EVs) provide active and reactive power support to DN. Voltage support can be achieved at DN node from RES and EVs. In this paper, coordination of EVs and PAs are integrated with DN to improve the voltage profile. The coordination can be achieved by providing active and reactive power support through PAs and the EVs charge/discharge (support/inject) based on the node voltage. The bulk numbers of EVs are connected to the DN through point of common coupling (PCC) which is called as Charging Station (CS). Many PAs are used to generate power for DN support;it's known as DN Supporting Point (DNSP). The main focus of this work is to predict the most accurate location of CS and DNSP. A 33kV distribution System (DS) of Guwahati city is modeled in MATLAB Simulink environment with four types of active and reactive load profiles. Typical DS having 48 nodes, the CS and DNSP are connected in each node of the DS. To control the power flow of the CS and DNSP, fuzzy based control strategies and optimal aggregator has been implemented. Also, probability distribution function has been used to find the scheduled arrival of EVs at CS. Voltage, power and energy of the CS and DNSP are compared.
[1] Simulated N2O distributions at midlatitudes and high latitudes in the Northern Hemisphere are analyzed in early and late vortex breakup years with the probability distribution function (PDF) technique. The data ar...
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[1] Simulated N2O distributions at midlatitudes and high latitudes in the Northern Hemisphere are analyzed in early and late vortex breakup years with the probability distribution function (PDF) technique. The data are from a Center for Climate System Research/National Institute for Environmental Studies (CCSR/NIES) nudging chemical transport model (CTM) for 24 years from 1979 to 2002. Results show that there is a large difference in midlatitude and high-latitude N2O concentrations on the 600 K isentrope between early and late vortex breakup years. In the early breakup years, the N2O concentration with the maximum area shows low values in the lower stratosphere in the springtime after the vortex breakup. In the late breakup years, the maximum area concentration shows constant high values from the winter to the summer. Our analyses show that the winter and springtime meridional circulation is a main factor for these differences in N2O concentration. In the early breakup years, a larger eddy heat flux causes a stronger winter meridional circulation and a stronger downward advection of low-N2O concentration air at higher altitudes to the lower stratosphere, which leads to the low values of N2O concentration in the lower stratosphere in late winter and early spring. Inside the Arctic vortex, however, the importance of vertical advection is smaller than or comparable to other processes such as horizontal divergence and subgrid-scale motions. These results are consistent with the previous studies on tracer distribution, which showed that not only the vertical advection but also the horizontal eddy transport are important for tracer concentration tendency in the polar vortex.
In this paper, a method to evaluate the power system vulnerability in terms of voltage magnitudes and transmission lines passing their limits is presented. Probabilistic technique is applied to obtain the distribution...
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ISBN:
(纸本)9781424465460
In this paper, a method to evaluate the power system vulnerability in terms of voltage magnitudes and transmission lines passing their limits is presented. Probabilistic technique is applied to obtain the distribution characteristics of the voltage magnitude and transmission line power flows. Cumulant-based expansion method is used to obtain the probabilistic distributionfunction (PDF) and cumulative distributionfunction (CDF) of power flows on transmission lines and voltage magnitudes at buses. The index to measure the system vulnerability is developed. The western North American power system model is used in the simulation to demonstrate the effectiveness of the method.
Short term load forecasting methods involve estimation of the model parameters. The estimation is done by using the historical data of load profiles. Therefore quality of the data is very crucial for the better estima...
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Short term load forecasting methods involve estimation of the model parameters. The estimation is done by using the historical data of load profiles. Therefore quality of the data is very crucial for the better estimation of the model parameters. In practice, many events occur which degrade the quality of data. These events include natural and man-made calamities, network outages, trade strikes, general elections, important sporting events etc. These events impact the load profile in an irregular manner. Inclusion of these events' data may contaminate the forecast. Anomaly of data could be seen due to significant shift or due to some spikes in the load profile. These anomalous load profiles should be detected and their use should be avoided in estimation process. In this paper, three approaches to identify the anomalous load profiles are proposed. The approaches are based on i) the notion of vector norm ii) probability distribution function and iii) hybrid of the two approaches. The approaches are tested with actual load data of an urban electrical distribution utility.
The characterization of random load behavior has been largely attempted through statistics-based model fitting. Remarkably, the use of Gaussian mixture model (GMM) has proven to be adequate to tackle the heterogeneity...
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ISBN:
(纸本)9781467325950
The characterization of random load behavior has been largely attempted through statistics-based model fitting. Remarkably, the use of Gaussian mixture model (GMM) has proven to be adequate to tackle the heterogeneity and variability of the statistical distribution of loads. In this paper, an application of the Mean-Variance Mapping Optimization (MVMO) algorithm to the identification of the parameters of GMMs, is presented. The feasibility of the proposed identification approach is demonstrated using historical data records from the Venezuelan transmission system portion that covers the Paraguana Peninsula.
An effective algorithm based on signal coverage of effective communication and local energy-consumption saving strategy is proposed for the application in wireless sensor *** algorithm consists of two *** is the multi...
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An effective algorithm based on signal coverage of effective communication and local energy-consumption saving strategy is proposed for the application in wireless sensor *** algorithm consists of two *** is the multi-hop partition subspaces clustering algorithm for ensuring local energybalanced consumption ascribed to the deployment from another algorithm of distributed locating deployment based on efficient communication coverage probability(DLD-ECCP).DLD-ECCP makes use of the characteristics of Markov chain and probabilistic optimization to obtain the optimum topology and number of sensor *** simulation,the relative data demonstrate the advantages of the proposed approaches on saving hardware resources and energy consumption of networks.
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