Abstract
Interval power flow (IPF) is an important tool for steady state analysis of wind power system. All the existing interval power flow calculation methods require that the input interval variables are independent of each other, so the interval correlation of wind power in wind farms cannot be reasonably taken into account. In response to this question, in this article, firstly, the correlation Angle is used to describe the interval correlation of wind power output, and constructed an interval power flow model considering wind power correlation. Then, affine transformation technique is used to realize the de-correlation of wind power output and convert the wind power output into independent interval variables. Finally, monte carlo method or optimal scenario algorithm is used to solve interval power flow, and the maximum and minimum values of the power flow state quantity which are the interval distribution are obtained. Simulating in the modified ieee-14 and 118 systems, and the results verified the effectiveness and feasibility of the proposed methods.
Highlights
The construction and grid connection scale of wind farms are increasing, which makes the proportion of wind power on the power side increase year by year and plays an important role in adjusting the primary energy structure
It is assumed that its random distribution or characteristic parameters of probability are known, and the random distribution of power flow electric parameters is obtained by using probability analysis method, so as to reveal the complete random law of power flow state
Interval power flow describes the wind power output as interval number, and interval analysis method can obtain the boundary information of power flow state, which are the maximum and minimum value
Summary
The construction and grid connection scale of wind farms are increasing, which makes the proportion of wind power on the power side increase year by year and plays an important role in adjusting the primary energy structure. Interval power flow describes the wind power output as interval number, and interval analysis method can obtain the boundary information of power flow state, which are the maximum and minimum value. Orthogonal transformation, copula function and other technologies can be combined for processing in probability power flow, but there is no technology in the interval power flow that reasonably describes and deals with the correlation of wind power output. The algorithm uses affine transformation to transform the relevant wind power output interval distribution into independent interval variables, and uses Monte Carlo method or optimal scenario algorithm to solve the interval power flow to obtain the maximum and minimum value of the power flow state, which is the Interval distribution. IEEE-14 and 118 systems’ results showed that both methods can deal with the correlation of interval variables accurately, and the calculation efficiency of the optimal scenario algorithm can be increased by tens of times compared with the Monte Carlo method
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