Spinning reserve assessment via quasi-sequential Monte Carlo simulation with renewable sources

This paper presents a new methodology for assessing spinning reserve in generating systems with high penetration of renewable energy. A state-space model is proposed to represent the generation capacity failures and the intermittency of renewable sources based on historical scenarios. The uncertainty in the system supply is captured through risk indices that represent the probability of not meeting the short-term estimated demand. A security strategy associated with the probability distribution of reserve levels is also proposed to avoid the over-sizing of reserve capacity levels to handle unlikely extreme operating points. Risk indices are estimated via quasi-sequential MCS-CE (Monte Carlo Simulation via Cross-Entropy) method, where the corresponding parameters are optimally distorted based on CE concepts. The proposed method is applied to a modified version of the IEEE RTS-79 system to cope with renewable sources.