Towards a Decarbonized Power System: a Matrix Tool for the Assessment of Power Flows Under Uncertainties

In a power system with high penetration of variable Renewable Energy Sources (vRES), the high uncertainty in generation could jeopardize the power system stability. Probabilistic Power Flow (PPF) is the leading steady-state tool to interpret how generation uncertainties affect the power flow results in power systems. However, for the PPF tools developed so far, it seems difficult to have ease of implementation, high computational efficiency and accuracy together. In this paper, a new probabilistic power flow algorithm (named PFPD_PRB) is presented. This algorithm combines the Monte Carlo statistical analysis techniques and PFPD, an original power flow algorithm characterized by easiness of implementation, accuracy and good computational performances. Hence, it is shown that the combination of these two numerical tools, PFPD from one side and Monte Carlo analysis to the other, is characterized by ease of implementation, high computational efficiency and accuracy. The algorithm is tested on the IEEE 39-bus and validated by means of solution comparisons with the commercial software DiGSILENT Power Factory. Eventually, three uncertainty scenarios on the IEEE 39-bus are discussed to show how to interpret and exploit PFPD_PRB results under different uncertainty conditions.