Stochastic Optimal Power Flow in Hybrid Power System Using Reduced-Discrete Point Estimation Method and Latin Hypercube Sampling

Stochastic nature of some input variables dictates the requisite of probabilistic analysis in power systems operation and planning. Wind generation is considered as a main source of intermittency in power systems due to the uncertain nature of wind speed. The proposed probabilistic optimal power-flow (POPF) method investigates spatial correlation among sources to attain more practical output distributions. The method established reduced-discrete point estimate method (RDPEM) along with the Latin hypercube sampling (LHS) in order to attain the stochastic characteristic of optimization's outputs. Despite needing less computational effort, highly accurate results can be obtained, while there is no prerequisite for probability distribution of the input random variables. In order to more validate the efficiency of the proposed method, the Gram-Charlier (GC) expansion is used to compare the outputs' cumulative distribution functions (CDFs) that are obtained from Monte Carlo (MC) with RDPEM methods. The performance and precision of the proposed solution are ascertained by comparison with those of Monte Carlo with discrete LHS (MCDLHS) in a hybrid IEEE 14-bus test system.

Automated summary

The study proposes a probabilistic optimal power-flow method that investigates spatial correlation among variables to achieve practical output distributions in power systems. The method offers high accuracy with less computational effort, and does not require knowledge of the probability distribution of the input variables.