Probabilistic optimal power flow in correlated hybrid wind-PV power systems: A review and a new approach

Hastening the power industry reregulation juxtaposed with the unprecedented utilization of uncertain renewable energies (REs), faces power system operation with sever uncertainties. Consequently, uncertainty assessment of system performance is an obligation. This paper reviews the probabilistic techniques used for probabilistic optimal power flow (P-OPF) studies and proposes a novel and powerful approach using the unscented transformation (UT) method. The heart of the proposed method lies in how to produce the sampling points. Appropriate sampling points are chosen to perform the P-OPF with a high degree of accuracy and less computational burden compared with features of other existing methods. The proposed method can take into account the correlation between uncertain input variables. In order to examine performance of the suggested method, two case studies are conducted and obtained results are compared with those of Monte Carlo simulation (MCS) and two point estimation method (2PEM). Comparison of the results justifies the effectiveness of the proposed method with regards to both accuracy and execution time criteria.