Probabilistic Load Flow Modeling Comparing Maximum Entropy and Gram-Charlier Probability Density Function Reconstructions

摘要

Probabilistic load flow (PLF) modeling is gaining renewed popularity as power grid complexity increases due to growth in intermittent renewable energy generation and unpredictable probabilistic loads such as plug-in hybrid electric vehicles (PEVs). In PLF analysis of grid design, operation and optimization, mathematically correct and accurate predictions of probability tail regions are required.