Research focuses on the issue of wireless big data computing in smart grid. Smart grid means electrical network architecture is purposed for generating, distributing and administering efficiently the power consumption to end-users. First investigate the consistency between the characteristics of big data and smart grid data. A propose a hybrid approach for storage planning, which consists of an outer optimization based on genetic and an inner optimization algorithm for energy scheduling. Propose a big data computing architecture for smart grid, consisting of four main levels: data sources, data transmission, data storage, and data analysis. First level, Data Source Level: data are generated from different data sources which are distributed, that is, distribution and transmission data, distributed generation data, intelligence application related data, and so on. These data are distributed in different places and managed by different companies/departments, which belong to different systems. Next, Data Storage Level: data collected from the data sources contain a lot of meaningless information, which does not need to occupy a large amount of storage space. Therefore, the data in a data center may be stored and processed. This level is suitable for the planning of smart grid with low real-time demands. Third level, Data Analysis Level: Various analytical methods and tools have been utilized to extract value in different application fields. It categorized by two division one is costumer-oriented service another one is utility-company-oriented service. Final level, Data Transmission Level: This level builds a bridge among the other three levels and also enables the intra-communication within each level. Experimental results indicate that the proposed storage planning scheme significantly reduces the cost for consumers.
smart grid, data sources, data transmission, data storage, data analysis
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