Bayesian Inference Framework for Probabilistic Power Flow Analysis for Microgrid Uncertainty Problem

The intermittence of renewable sources of energy and uncertainty in power system calls for the development of algorithms which can include uncertainty. In the probabilistic power flow (PRPF) analysis methods, injected powers have been considered uncertain, however there is also uncertainty associated with state variables. To take into consideration that uncertainty, Bayesian inference have been proposed for the microgrid. This involves specifying the prior and likelihood to find out posterior using Bayesian inference. Hamiltonian Monte-carlo (HMC) has been used as likelihood based Bayesian simulator. The method has been simulated using R-Stan for a 6 bus microgrid system.