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Monte Carlo based method for managing risk of scheduling decisions with dynamic line ratings

Dynamic line ratings have been shown as an attractive alternative to conventional congestion management methods that can potentially improve wind integration. However, the modelling of dynamic line ratings is dependent on effectively modelling the risk of thermal overload which usually has a high amount of uncertainty. This paper uses a sample average approximation method to model the uncertainty in risk function and determine how scheduling decisions are affected. It also presents a sensitivity analysis to determine the level of uncertainty in the risk function that can be managed and how the sampling process should be adjusted. Test cases indicate that there is a high level of confidence in scheduling decisions for sample sizes less than 100. A larger sample size can maintain the high level of confidence if there is a greater uncertainty associated with the risk function.