Probabilistic Load Flow Evaluation With Hybrid Latin Hypercube Sampling and Cholesky Decomposition

TLDR

Monte Carlo simulation with simple random sampling is computationally intensive and requires large storage for probabilistic load flow evaluation. This paper proposes incorporating Latin hypercube sampling combined with Cholesky decomposition into Monte Carlo simulation to solve probabilistic load flow problems. The proposed LHS‑CD method is tested on IEEE 14‑bus and 118‑bus systems and compared with simple random sampling and Latin hypercube sampling with random permutation. Results show that LHS‑CD is robust, flexible, and suitable for a wide range of power system probabilistic analyses.

Abstract

Monte Carlo simulation method combined with simple random sampling (SRS) suffers from long computation time and heavy computer storage requirement when used in probabilistic load flow (PLF) evaluation and other power system probabilistic analyses. This paper proposes the use of an efficient sampling method, Latin hypercube sampling (LHS) combined with Cholesky decomposition method (LHS-CD), into Monte Carlo simulation for solving the PLF problems. The LHS-CD sampling method is investigated using IEEE 14-bus and 118-bus systems. The method is compared with SRS and LHS only with random permutation (LHS-RP). LHS-CD is found to be robust and flexible and has the potential to be applied in many power system probabilistic problems.