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Modern Microgrid (MG) mainly consists of distributed generators (DGs), energy storage systems (ESSs), different loads, and protection systems. Microgrid plays an important role not only to ensure the power supply reliability but also to improve the power quality in distribution network. Moreover, deployment of distributed generators such as Photovoltaic Generation System (PVS), wind turbine generation system, and energy storage systems diversifies operation and control modes of AC microgrid in order to meet local demand response. This paper studies on power-flow dispatching cases for a MG with PVS and Battery Energy Storage System (BESS), which considers the maximum power consumption generated by the PVS and minimizes the power received from the utility grid. Simulation results validate the effectiveness of BESS for actively dispatching power-flow of MG in case the PVSs cannot partially or fully meet the local demand response in peak hours. By using ETAP software, real data of the PVSs are used to do simulation and power-flow calculation for the MG, which is to evaluate the feasibility of power-flow dispatch solutions proposed in this paper.

As the economy increasingly developed, the demand for power systems increasingly large. Should be to improve the reliability of the grid, short term load forecasting is the indispensable factor for improving the fastness of power supply and distribution smart grid (SG) applications. Based on the comparison of applicability and aptitude of Feed-forward Deep Neural Network (FF-DNN) according to the criteria of accuracy and performance applied for short term forecast, and integrated with Recurrent Deep Neural Network (R-DNN). In my article, we study the pertinency and combination two deep neural network architectures Feed-forward Deep Neural Network (FF-DNN) and Recurrent Deep Neural Network (R-DNN) to the Ho Chi Minh City electricity load forecasting task. Thereby, we suggest DNN based electricity load forecasting system to manage in distribution and consumption in the best way. My article used method with activation functions which are different such as Sigmoid, Hyperbolic Tangent (tanh) and Rectifier Linear Unit (ReLU). The propound sheme is administrated to required offline load parameters of Ho Chi Minh City. Load forecasts of weather attribute analysis using DNN. It allows for the capture of these factors affecting the electricity consumption model. Three performance metrics include absolute percentage error (MAPE), normalized root mean square (NRMSE), and correlation coefficients are used to formalize the propound sheme. The final results have demonstrated that FF-DNN and R-DNN are authntic.

With the increasingly modern development of the power system. Along with that is the data source collected from them is huge. Combined with other systems such as GIS, MDMS-AMR (Automatic Meter Reading), weather forecast and socio-economic indicators. We consider performing an effective analysis of the data sources in order to understand the evolution, characteristics, and modeling of the power consumption system. Thereby predict future energy trends and build bases for the system model. To implement the issues raised, we appreciate using Hadoop platform for storage and segmentation data, enabling better handle large amounts of data initially. Then, the data was analyzed using the scalable machine learning algorithms - MLib was supported and developed on the Spark/SPARKNET platform. The Hadoop framework has recently evolved to the standard framework implementing the MapReduce model. In this paper, we evaluate Hadoop with Mlib/Sparknet performance in both the traditional model of collocated data and compute services as well as consider the impact of separating out the services. Energy modeling from multiple data sources such large may help to understand the change of the system according to consumer demand for practical, predictable trends of energy in the future and provide the basis for building energy models for similar systems.

This paper has been formally withdrawn on ethical grounds because the article contains extensive and repeated instances of plagiarism. Web of Conferences treats all identified evidence of plagiarism in the published articles most seriously. Such unethical behaviour will not be tolerated under any circumstance.

[ABSTRACT]Urban Area is an important class of Global Land Use/Land Cover Map. Global Urban Mapping is always difficult because of the different definition of "urban area" in different countries. In remote sensing, "urban area" is often best defined as population density distribution, as percentage of impervious surface, nighttime light. Green fields and water bodies (such as a big park or a golf course) were not considered as urban. In this study, urban area was mapped using multi-source geospatial data of 2008 year such as Population Distribution and Density, DMSP-OLS (The Defense Meteorological Satellite Program's Operational Linescan System) nighttime lights, MODIS data and Estimate the density of constructed Impervious Surface Area (EstISA) data of 2010 year. Information from all data sources was combined to create a final map of urban area and compared with fine resolution Landsat ETM+ and Google Earth image. The Global Urban Map 2008 produced was compared with some other existing global maps such as GLC2000, GLOBCover2004_2006 and GLCNMO 2003 (version 1). Results of the comparisons showed that it was newer and better than other existing maps for the purpose of urban mapping. This study describes recent progress in producing reliable representations of urban areas at 500m resolution, a part of the project of GLCMNO version 2. Green areas in urban, rural areas and water bodies were better excluded from urban areas by Global Urban Map ofGLCNMO 2008 than by GLCNMO 2003. The objective of this study was improving the mapping method by GLCNMO 2003 using automated urban mapping classification and monitoring urban areas at continental and global scales using multi-source Geospatial Data and MODIS data.

[ABSTRACT]Multispectal remote sensing data has been widely used for land cover mapping as well as urban study. The mechanism of ground object recognition is based mainly on its spectral reflectance characteristics. However, in most cases, it has certain limitation of accuracy in ground objects recognition. SAR remote sensing data on the other hand supplies information on surface structure, texture patterns. To achieve the best result it is strongly required to combine both these data for the analysis.

[ABSTRACT]Optical remote sensing usually has not enough multi-temporal high resolution images to describe phenology of objects for forest mapping in local scale. This paper presents a possibility to improve accuracy of tropical forest mapping by combination of optical and microwave images. Study area is located in the southern part of Vietnam. The first, ALOS/AVNIR-2 images were used to create the forest map, then ALOS/PALSAR single-polarized and dual-polarized images were used to improve the accuracy of the classification result by a combination model. ALOS/PRISM images were also used to make Pan-sharpen images for collecting training data and validation data. Discrimination of Planted Forest and Natural Forest is one of the most important purposes of this study. The overall accuracy of ALOS/AVNIR-2 classification result is 77.0%, while after combining with ALOS/PALSAR, it is increased up to 88.2%. The accuracy is higher than 90% for main forest classes.

This study aims to develop an efficient method to extract mangrove forests at a regional scale using remote sensing data and Geographic Information Systems (GIS) technique. A regional mangrove forests mapping method was developed based on the spectral characteristics and the topographic existence condition of mangrove forests. Landsat Enhanced Thematic Mapper plus (ETM+) and digital elevation model (DEM) were used to enhance the discrimination between mangrove forests and other different kinds of forests and vegetations to achieve the purpose of this study in the southern part of Japan. The maximum likelihood supervised classification technique was applied to map mangrove forests. The resultant mangrove forests map were further validated by using aerial photographs, topographic maps and other local vegetation maps. This study revealed that mangrove forests in the study area could be accurately mapped from integration of spectral data derived from Landsat ETM+ together with topographic information.

[ABSTRACT]Because of the capacity of observing land cover information in any weather, microwave sensor is good at monitoring forest change especially in the tropical area. In this study, Japanese ALOS PALSAR 50m orthorectified mosaic data will be used for extracting the distribution of forest in Kalimantan as a free product. However, we have to consider a big problem caused by the effects of terrain slope. Therefore, we compared two existing slope correction methods for PALSAR 50m mosaic data, and applied the better one to complete the terrain slope calibration in this paper.

形態: カラー図版あり, 形態: DVD-ROM1枚, Physical characteristics: Original contains color illustrations, Note: One DVD-ROM, 資料番号: AA0065135091, レポート番号: JAXA-SP-11-007E

研究科: 千葉大学大学院理学研究科, 千大院理博甲第理15号