A probabilistic power flow (PPF) study is an essential tool for the analysis and planning of a power system when specific variables are considered as random variables with particular probability distributions. The most widely used method for solving the PPF problem is Monte Carlo simulation (MCS). Although MCS is accurate for obtaining the uncertainty of the state variables, it is also computationally expensive, since it relies on repetitive deterministic power flow solutions. On the other hand, MCS does not not take into account the fact that previous knowledge of state variables might be available in terms of probability distributions. In this project, we explore different perspectives
for solving the PPF problem. We frame the PPF as a probabilistic inference problem, and instead of repetitively solving optimization problems, we use Bayesian inference for computing posterior distributions over state variables. We provide likelihood-free methods to solve the PPF problem. Results in two different test systems show that the proposed methodologies are competitive alternatives for solving the PPF problem.
