论文标题
传输系统的控制感知概率负载流量:一种分析方法
Control-aware Probabilistic Load Flow for Transmission Systems: An Analytical Method
论文作者
论文摘要
数十年来,已经研究了概率负载流量(PLF)计算作为分析传输系统行为的基本工具。尽管有多种可用方法,但现有的PLF方法很少考虑系统控制。但是,系统控制是随机变量的波动与系统状态变化之间的自动缓冲,对最终PLF结果产生了重大影响。为了考虑控制动作的影响,本文提出了第一种考虑到主要和次要频率控制的传输网格的分析PLF方法。该方法基于高精度线性功率流模型,本文通过原始校正方法进一步提高了精度。本文还证明,如果随机变量的关节概率分布(JPD)由高斯混合模型(GMM)表达,则系统状态的JPD(例如,淋巴结电压)是无限GMM。通过利用此命题,提出的方法可以生成整个系统的关节PLF,适用于遵守任何分布的随机变量,并且能够捕获其相关性。该方法的高精度和令人满意的效率在测试用例从14至1354总线上缩放上得到了验证。
Probabilistic load flow (PLF) calculation, as a fundamental tool to analyze transmission system behavior, has been studied for decades. Despite a variety of available methods, existing PLF approaches rarely take system control into account. However, system control, as an automatic buffer between the fluctuations in random variables and the variations in system states, has a significant impact on the final PLF result. To consider control actions' influence, this paper proposes the first analytical PLF method for the transmission grid that takes into account primary and secondary frequency controls. This method is based on a high-precision linear power flow model, whose precision is even further improved in this paper by an original correction approach. This paper also proves that if the joint probability distribution (JPD) of random variables is expressed by a Gaussian mixture model (GMM), then the JPD of system states (e.g., nodal voltages) is an infinite GMM. By leveraging this proposition, the proposed method can generate the joint PLF of the whole system, is applicable to random variables obeying any distributions, and is capable of capturing their correlation. The high accuracy and satisfactory efficiency of this method are verified on test cases scaling from 14 to 1354 buses.