Survey of Morris and E-FAST algorithms based on power-generation operation and assistant decision model

Abstract

Global uncertainty and sensitivity analysis(UA-SA) can be applied to quantify the influence of uncertain model inputs on the response variability of a power system model. Uncertainty and sensitivity analysis algorithms are becoming an efficient tool for the understanding, application and development of mathematical and computer models. In this paper, Morris and Extended Fourier Amplitude Sensitivity Test (E-FAST) is used to test the Power-generation Operation and Assistant Decision Model (POADM). Rankings of POADM parameters (from the most to the least relevant) were generated. And then we further analyse the effect of the uncertainty of parameters and interaction between the parameters. Sensitivity algorithms was devoted to predict the risk of facing a loss of total profits as thermoelectric conversion efficiency decreases and assess the relative importance of input parameters on the output. As evidenced by the performance indices, Morris and E-FAST algorithms have demonstrated to be powerful techniques for quantifying uncertainty in complex model. Those two algorithms are reliable and robust in global uncertainty and sensitivity analysis.