These methods propose to stratify sampling in presence of ancillary data. Two case studies modified from the Electric Reliability Council of Texas (ERCOT) and IEEE Reliability Test System (IEEE RTS) are presented to demonstrate the performances of the proposed sampling methods. clhs Conditioned Latin Hypercube Sampling Description Implementation of the conditioned Latin hypercube sampling, as published by Minasny and McBrat-ney (2006) and the DLHS variant method (Minasny and McBratney, 2010). It is shown that the proposed methods are as accurate as the other sampling methods while requiring much less CPU time. Carlo approach (randomized Halton sequences, modified Latin hypercube. Results from Monte Carlo (MC) sequential sampling, MC nonsequential sampling, and that from the proposed LHS methods are compared. However, other sampling approaches are available. Reliability indices such as loss of load expectation and loss of load probability are estimated. Contents 5.4.7 Antithetic sequences 153 5.4.8 PMC and QMC rates of convergence 155 5.5 Correlation and drawing from densities 157 5.6 Calculating choice probabilities for models without a closed analytical. The LHS methods that are applicable for systems with correlated random variables - system load and renewable generation - are proposed. 5.4.5 Modified Latin Hypercube sampling 148 5.4.6 Sobol sequences 150. EGU - SSS Conference ELS 2014 The Earth Living Skin: Soil, Life and Climate Changes, 22-25 September 2014, Bari, Italy. handle this, we modify and extend arguments that Avramidis and Wilson (1996. Integrating legacy soil information in a Digital Soil Mapping approach based on a modified conditioned Latin Hypercube Sampling design. This paper proposes Latin hypercube sampling (LHS) methods for reliability analysis of power systems including renewable energy sources, with an emphasis on the fluctuation of bus loads and intermittent behavior of renewable generations such as wind and solar power. simulation with Latin hypercube sampling (LHS), a variance-reduction. IEEE Transactions on Power Systems 26 (4) : 2066-2073. Latin hypercube sampling techniques for power systems reliability analysis with renewable energy sources. Latin hypercube sampling techniques for power systems reliability analysis with renewable energy sources