An importance sampling technique for probabilistic security assessment in power systems with large amounts of wind power

• An operating risk is defined as a probabilistic N-1 criterion.
• The operating risk considers uncertainty due to renewable energy sources.
• An importance sampling method is developed to estimate the operating risk.
• The method is tested in the IEEE 39 and IEEE 118 bus systems.
• Results show significant decreases in computation time.

Larger amounts of variable renewable energy sources bring about larger amounts of uncertainty in the form of forecast errors. When taking operational and planning decisions under uncertainty, a trade-off between risk and costs must be made. Today's deterministic operational tools, such as N-1-based methods, cannot directly account for the underlying risk due to uncertainties. Instead, several definitions of operating risks, which are probabilistic indicators, have been proposed in the literature. Estimating these risks require estimating very low probabilities of violations of operating constraints. Crude Monte-Carlo simulations are very computationally demanding for estimating very low probabilities. In this paper, an importance sampling technique from mathematical finance is adapted to estimate very low operating risks in power systems given probabilistic forecasts for the wind power and the load. Case studies in the IEEE 39 and 118 bus systems show a decrease in computational demand of two to three orders of magnitude.