Probabilistic Power Flow Analysis of Bulk Power System for Practical Grid Planning Application

Abstract

The sizes of PV power plants have grown in such a way that their effects on the power system can no longer be neglected. In order to address these issues, grid operators are forced to expand grid connection points, and a power flow analysis considering uncertain renewable generation is required. Thus, a modified probabilistic power flow (PPF) analysis for practical grid planning is suggested in this paper. The regularity and randomness of PV power are modeled by a Monte Carlo-based probabilistic model combining both k-means clustering and the kernel density estimation method. The certain cluster group is selected so as to reflect the severe PV generation scenario, and the chi-square test to represent the $n$ th conservative network planning was suggested. In order to provide the power flow result more effectively, a mapping function of graphic representation based on a significant grid code violation is provided in an automatic PPF tool written by Python scripts. Following this procedure yields a reasonable network design for various renewable energy penetration levels.

Keywords

• Probabilistic power flow
• k-means clustering%22%2C%22default_operator%22%3A%22AND%22%7D%7D%7D"> <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">k</italic>-means clustering
• randomness
• renewable energy
• conservative grid design