Multi-area reliability assessment based on importance sampling, MCMC and stratification to incorporate variable renewable sources

This paper presents a new methodology for Monte Carlo-based multi-area reliability assessment based on optimal Importance Sampling (IS), Markov Chain Monte Carlo (MCMC) and optimal stratification. The proposed methodology allows the representation of relevant features of variable renewable energy (VRE) resources such as intermittency, daily and seasonal variation and spatial correlation with other sources (e.g. correlation between wind and hydro). The effectiveness of the proposed methodology is illustrated with case studies based on the Saudi Arabia and Chilean systems, with speedups of 300 and 800 times compared with the standard Monte-Carlo simulation.

Automated summary

This paper introduces a new methodology for Monte Carlo-based multi-area reliability assessment using optimal Importance Sampling, Markov Chain Monte Carlo, and optimal stratification. The proposed methodology effectively represents features of variable renewable energy resources and has been demonstrated to achieve significant speedups in comparison with standard Monte-Carlo simulation through case studies based on the systems of Saudi Arabia and Chile.