Battery State of Charge Stochastic Model determination for Microgrids Probabilistic Power Flow computation

Abstract

This document proposes a novel methodology for probabilistic estimation of the State of Charge (SOC) stochastic model of Battery Energy Storage Systems (BESS). Proper estimation of SOC is one of the most important parameters for microgrids expansion planning and operation analysis. For this aim, a script that links DIgSILENT PowerFactory and Python is structured. This computational tool allows a probabilistic assessment of the microgrid operation, considering the intermittent availability of the renewable energy primary resource and the electric demand variability. As a result, the stochastic models of SOC of BESS are determined for each period of time. This methodological proposal is applied to a microgrid test system connected to the “Bus 6” of the three machine - nine bus WSCC test power system, obtaining promising results.