Probabilistic spinning reserve adequacy evaluation for generating systems using an Markov chain Monte Carlo-integrated cross-entropy method

Spinning reserve plays an important role in balancing generation and demand mismatch within a short time interval. Probabilistic spinning reserve adequacy evaluation (PSRAE) is useful to aid operators in monitoring the adequacy of system upward spinning reserve exposed to unforeseen disturbances and making risk-adverse decisions for unit dispatching. As a short time period, for example, 30 s-15 min, is usually considered in PSRAE, the generating system in question is commonly of high reliability, which renders naive non-sequential Monte Carlo methods inefficient. In this study, the high reliability phenomenon of generating systems is explained through a toy case study, thereafter, aiming to develop an efficient method for PSRAE, a simulation method based on the cross-entropy which has been widely applied in many areas to improve naive Monte Carlo methods for tackling rare-event simulation issues, integrated with Markov chain Monte Carlo is proposed. The indices of loss of load probability and expected demand not supplied are used to quantify the spinning reserve inadequacy risk. Through case studies conducted on the RTS-79, usefulness of the PSRAE is discussed and the proposed method is proven to be superior to the classical cross-entropy method and thus could be taken as a candidate tool for the PSRAE to practical systems.