Probabilistic Power Flow Algorithm Research Containing Distributed Generation In Distribution System

At present, a large number of distributed generations have been integrated to power system especially the distribution system, however, the distributed generation which mainly based on wind power and photovoltaic generation have a characteristic of strong randomness and weak controllability, the uncertainty of the system will greatly increase with a large number of distributed generation being connected to distribution system. Therefore, in this paper we make a research on probabilistic power flow concering distributed wind and photovoltaic power source being connected to distribution system. The main works are as follows:This thesis mainly considers "the source" and "load side" uncertainty of the distribution system, considering wind power output have a characteristic of large fluctuations and discontinuity, in this paper, we make a discrete improvement treatment on the active output model of wind power, besides, discrete load distribution is also taken into consideration. Accordingly, we establish wind power and photovoltaic power generation output stochastic models of the distributed generation, as well as conventional generator and load output stochastic models. In this case, it can not only improve the calculation accuracy but also effectively deal with that the system state variables fluctuating even distorting by accessing wind power. Meanwhile, the thesis proposes an indicator named “kurtosis-voltage-ratio” to quantify the impact on distribution system when wind power and photovoltic power are integrated to the grid in different proporations.The thesis mainly studies the method based on combined cumulant and Gram-Charlier expansion on the basis of making a comprehensive comparison on existing stochastic power flow algorithm. Studies have shown that this method can avoid complex algebraic operations by using the additivity of combined cumulant method, but the skewness and kurtosis of series expansion will greatly deviate from standard value when wind power of great intermittent output is connected to the distribution system, then the probability distribution of state variable will have a great volatility even distortion. For this shortcoming, we propose an improved mixed stochastic power flow algorithm, random variables are divided into two parts separately obeying normal distribution and discrete distribution, then the discrete distribution can be obtained by Von Mises method, the normal distribution can be obtained by combined cumulant method, then a more reasonable state variables probability distribution can be obtained by making a convolution of them.Applying various stochastic models established in this thesis as well as proposed probability power flow algorithm to the IEEE 33 nodes distribution network and making a sample simulation analysis, then the distribution system’s probability distribution of each node voltage and branch power flow are obtained, besides, we make a multi-angle analysis of the influence on distribution system, it verifies the accurateness that using an indicator named “kurtosis-voltage-ratio” to quantify the impact on distribution system when wind power and photovoltic power are integrated to the grid in different proporations. At the same time, it proves the effectiveness of improved mixing stochastic power flow algorithm.