The use of importance sampling in stochastic OPF

This paper presents the sequential-quadratic programming technique combined with the method of importance sampling in order to solve the stochastic optimal power flow (OPF). It is widely recognized that it is impossible to model all possible contingencies. Instead, we employ Monte Carlo importance sampling techniques to obtain an estimate of the expected value of multiple-contingency operating cost. Recent blackouts warn us that there is a need for clever stochastic algorithms able to assess multiple outage scenarios with potentially catastrophic consequences. The objective in importance sampling is to concentrate the random sample points in critical regions of the state space. In our case that means that single-line outages that cause the most ";trouble"; will be encountered more frequently in multiple line outage subsets.