A State-similarity-based Fast Reliability Assessment for Power Systems with Variations of Generation and Load
Zeyu Liu, Lewei Zhu, Kai Hou, Yue Zhou, Ruifeng Zhao, Hongjie Jia
Abstract
The increasing penetration of intermittent renewable generators and the uncertainty of loads have brought significant challenges to the power system reliability assessment, as large numbers of optimal power flow (OPF) tasks need to be repetitively solved considering these uncertainties. To deal with that, this paper proposes a state-similarity-based (SS) approach to replace the computationally demanding AC OPF. It is observed that the active constraints related to the least load curtailment typically remain consistent across varying system states. Following this, the active constraints are used to represent the state similarity, and then nonlinear equations are derived to replace the original AC OPF problems. Thereafter, an alternating iterative approach is developed to obtain the minimal load curtailment instead of calculating the time-consuming optimization. An optimality criterion is developed to exclude the majority of mismatched solutions, thereby minimizing potential inaccuracies in the reliability assessment. Case studies conducted on the RTS-79, IEEE 118-bus, and Brazilian systems demonstrate that the proposed methods can significantly enhance computational efficiency with minimal errors. In some instances, these methods can outperform traditional reliability assessment methods by over 10 times.