基于容载比色带的区域配电网充裕性分析

Sufficiency Analysis of Regional Distribution Network Based on the Color Band of Capacity-load Ratio

容载比是评价配电网充裕性的重要指标,由于导则规定范围较大,影响因素较多,无法为配电网规划改造提供具体支撑,不利于电网投资效益的提升,为此提出了一种基于容载比色带的配电网充裕性分析方法。首先提取典型配电区域影响充裕性的网络和设备参数,并结合容载比在“N-1”原则与概率可靠性原则下的量化原理,基于多元组理论建立了表征各参数特征下的十一元组集合。在此基础上,以不同可靠性原则和边界条件下的容载比理想值为基准,提出了容载比色带定义和求解算法,以此作为配电网充裕性和再投资的标尺。最后,通过算例说明了容载比色带在实际电网中的使用方法,并验证了其在充裕性分析中的可行性和有效性,算例结果表明容载比色带能够支持配电网薄弱环节的挖掘,有效支撑了电网分析的科学性和投资的精益化。

The capacity-load ratio is an important indicator for evaluating the adequacy of the distribution network.Due to the large scope of the guidelines and many influencing factors,it cannot provide specific support for the planning and transformation of the distribution network,which is not conducive to the improvement of the investment efficiency of the power grid.For this reason,we proposed a method for analyzing the sufficiency of the distribution network based on the capacity-load ratio color band.First,we extracted the network and equipment parameters that affect the adequacy of the typical distribution area.We combined the quantification principle of the capacity-load ratio under the principle of"N-1"and the principle of probability reliability and based on the tuple theory,an eleven-tuple that characterizes the characteristics of each parameter is established.On this basis,the ideal value of the capacity ratio under different reliability principles and boundary conditions was treated as reference value and the definition and solution algorithm of the capacity and capacity ratio color band was proposed as a ruler of the abundance and reinvestment of the distribution network.Finally,we illustrated application of the capacity-load ratio color band in the actual power grid through an example and verified the feasibility and effectiveness in the sufficiency analysis.The results of the calculation example show that the capacity-load ratio color band is useful in mining of weak links in the distribution network,effectively supporting the scientific nature of the grid analysis and the lean investment.