基于可再生能源的智能电网动态管理电价研究

V. Kaplun, V. Osypenko
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引用次数: 7

摘要

提出了一种用于组合型智能电网局部系统最优定价的“目标”设计子集(聚类)的新方法。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24小时)的周期,从任务的角度来看,在此期间执行了必要的信息测量值(系统组件产生的能量,每种发电机的当前成本等)。我们假设智能电网系统是基于太阳辐射和风能等可再生能源,并结合大功率蓄电池和基于自主电站的发电机等系统组件。获得的统计信息构成了构建模型的基础,该模型根据使用双聚类算法的“对象”子集的开发标准描述某些最优。这种创新方法的作者考虑到模型输出(最优聚类)的进一步应用,以动态估计其自身组件产生的总能源成本,同时考虑到一天中后续时段涉及的网络成本。研究采用一天内半小时采样时间。在收集到统计数据的基础上进行了仿真,仿真结果可应用于可再生能源智能电网动态管理的电价过程(算法)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
About Using Electricity Pricing for Smart Grid Dynamic Management with Renewable Sources
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.
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