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The opportunity and challenge of Big Data's application in distribution grids

机译:大数据在配电网中的应用机遇与挑战

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In order to meet the challenge of Big Data, enhance the intelligent level of distribution grid, the better for the power user service. Starting from the characteristics of 4 V of Big Data, the 6 links to the power supply system of industrial chain (i.e., planning, design, construction, operation, management & regulation of distribution grids, and equipment design and manufacturing) is relatively mature degree angle; the needs of Big Data's application in distribution network have been analysed. B y using the method o f SWOT, analysed the double-edged sword effect Big Data for the distribution gird, provides both opportunities and challenges. The benefits and opportunities is that, Big Data bringing data view, changing thinking methods and tools, expanding the application scene, providing better service to the society, enhancing the value of the opportunity. At the same time, Big Data will lead to the challenges in distribution grid, for example, because of security challenges of Big Data itself, Big Data more concentrated, cause safety challenges in distribution grid is more serious; the energy consumption challenges of Big Data; Big Data privacy threat distribution grid and user. The demand for Big Data's application in distribution grids in industrial chain is that, from strong to weak, management & regulation of distribution grids, operation, equipment design and manufacturing, construction, design, planning. Big Data's source of power supply enterprise's internal operation, including 3 parts, physical grid operation, marketing services and grid enterprise operation. Power system technology innovation by three wheel drive (experimental science, theoretical science, computational science) increased to four wheel drive (experimental science, theoretical science, computational science, data intensive science/data exploration science) paradigm. Big data is still “explosion”, control the fourth paradigm — data intensive science a- so need to redouble our efforts. Non structure data in distribution grid will be rapid growth, 50% more than the amount of data in the next five years.
机译:为了满足大数据的挑战,增强了分布网格的智能水平,更好的电力用户服务。从大数据的4 V的特点开始,6个连接工业链电源系统(即规划,设计,施工,操作,管理和调节,配电网格,设备设计和制造)是相对成熟的程度角度;已经分析了大数据在分销网络中应用的需求。 B y采用F SWOT的方法,分析了双刃剑效应分布仪的大数据,提供了机会和挑战。好处和机遇是,大数据带来数据视图,更改思维方法和工具,扩展应用程序场景,为社会提供更好的服务,提高机会的价值。与此同时,大数据将导致分销网格中的挑战,例如,由于大数据本身的安全挑战,大数据更集中,发挥分配网格的安全挑战更严重;大数据的能源消耗挑战;大数据隐私威胁分配网格和用户。对产业链中分销网的大数据应用的需求是,从强大到弱,管理和调节分配网格,操作,设备设计和制造,建设,设计,规划。大数据的电源企业内部操作源,包括3件,物理网格运行,营销服务和电网企业运行。电力系统技术创新三轮驱动(实验科学,理论科学,计算科学)增加到四轮驱动(实验科学,理论科学,计算科学,数据密集型科学/数据勘探科学)范式。大数据仍然是“爆炸”,控制第四个范式 - 数据密集型科学A-所以需要加倍努力。分布网格中的非结构数据将快速增长,比未来五年的数据量多50%。

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