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About Using Electricity Pricing for Smart Grid Dynamic Management with Renewable Sources

机译:关于将电价用于具有可再生能源的智能电网动态管理

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A new approach to the design subsets (clusters) of “objects” for optimal pricing in local systems of Smart Grid (SG) of the combined type has been proposed. In our case, under term “objects” we understand the day-time (more precisely, twenty-four hours) periods, in which the necessary measurements of informative, from the standpoint of the task, values (the energy generated by the components of the system, its current cost for each type of generator, etc.) were performed. We assume that systems Smart Grid are based on renewable generation sources such as solar radiation and wind energy in combination with such system components as high-power storage batteries and power generators based on autonomous power station. The obtained statistical information formed the basis of constructing models that describe certain optimal in terms of developed criteria of a subset of “objects” using bi-clustering algorithms. The authors of this innovative approach have in mind the further application of the model output (optimal clustering) for the dynamic estimation of the total cost of energy generated by its own components, taking into account the cost of the network involved in the subsequent periods of the day. In research the half-hourly sampling time within one day was used. Simulation on the basis of collected statistical data, the results of which can be applied in processes (algorithms) of electricity pricing for smart grid dynamic management with renewable sources has been performed.
机译:提出了一种针对“对象”的设计子集(集群)的新方法,以在组合类型的智能电网(SG)的本地系统中实现最优定价。在我们的案例中,在“对象”一词下,我们理解白天(更准确地说是二十四小时)的时间段,其中从任务的角度而言,必要的信息量度是值(由组件的能量生成)。系统,每种发电机的当前成本等)。我们假设系统智能电网基于可再生能源,例如太阳辐射和风能,并结合了系统组件,例如大功率蓄电池和基于自主电站的发电机。获得的统计信息构成了构建模型的基础,该模型使用双聚类算法根据“对象”子集的开发标准描述了某些最优值。这种创新方法的作者考虑到了模型输出(最优聚类)的进一步应用,用于动态估算其自身组件所产生的能源的总成本,同时考虑了后续阶段的网络成本。那天。在研究中,使用了一天内半小时的采样时间。已经执行了基于收集到的统计数据的仿真,其结果可应用于用可再生资源进行智能电网动态管理的电价定价过程(算法)。

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