Reliable Algorithms for Power Flow Analysis in the Presence of Data Uncertainties

Power flow analysis serves to determine the steady state of the power system for a specified set of load and generation values. It is one of the most intensely used tools in various power engineering applications, including network optimization, voltage control, state estimation and market studies. The most common formulation of the power flow problem—the deterministic power flow—has all input data specified from the snapshot corresponding to a point in time or from a proper set of “crisp” values that the analyst constructs under the assumptions for the system under study, such as the expected generation/load profiles for a certain peak demand condition. The solution for the steady is deemed representative for a limited set of system conditions. However, when the input conditions are uncertain, the analyst fails to know the precise actual conditions in the system and therefore numerous scenarios need to be analyzed to cover the range of uncertainty. Under such conditions, reliable solution algorithms, incorporating the effect of data uncertainty into the power flow analysis, are therefore required. Reliable power flow solution algorithms allow the analyst to estimate both the uncertainty in the input data and in the solution tolerance. In this way, the uncertainty propagation effect is explicitly represented and the level of confidence of power flow studies can be assessed.