Optimizing the Utilization Rate for Electric Power Generation Systems: A Discrete-Event Simulation Model

Abstract

The problem of measuring the availability of generation units in electric power systems has been addressed in the literature by using a set of analytical equations and Monte Carlo Simulation (MCS). MCS, as a powerful simulation tool, is much easier to use than analytical approaches in measuring the availability of large applications. However, the simulated process using MCS entails deducing the operating state sequences for each component along the simulated time followed by combining all sequences for all components in order to deduce system availability. This simulated process is both lengthy and timeconsuming. This paper introduces a new method to measure the availability of generation units and optimize their utilization using Discrete Event Simulation (DES) model. The experimental result of a real case study illustrates the efficiency of using DES.

Stochastic Diffusion Process-based Multi-Level Monte Carlo for Predictive Reliability Assessment of Distribution System

“…Other methods for improving MCS have been explored, including the dagger sampling technique (Sun et al 2010), accelerated state evaluation approach (Shu et al 2014), and discrete event simulation (Aldhubaib and Kashef 2020). Among a different type of VRTs applied to power system reliability analysis, the beneficial methods are Quasi-Monte Carlo (Hou, Wang, and Guo 2017), Importance Sampling (Urgun 2019), Control Variates (Wang et al 2018), Stratified Sampling (Wang et al 2011, and Antithetic Variables (Benidris and Mitra 2014).…”