Open AccessRisk‐averse multi‐objective generation dispatch considering transient stability under load model uncertainty
Author(s) -
Xu Yan,
Xie Xuekuan,
Dong Zhao Y.,
Hill David J.,
Zhang Rui
Publication year - 2016
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2015.1502
Subject(s) - transient (computer programming) , electric power system , mathematical optimization , economic dispatch , stability (learning theory) , probabilistic logic , computer science , pareto principle , control theory (sociology) , power (physics) , mathematics , quantum mechanics , machine learning , artificial intelligence , operating system , physics , control (management)
Maintaining transient stability is one essential requirement for power system reliable operations. However, there is usually a conflict between the stability level and the economic objective. This study presents a new generation dispatch model to balance the two concerns. A risk‐based criterion is proposed to quantify system transient stability on a probabilistic basis, and a multi‐objective programming model is proposed to achieve best trade‐off between transient stability requirement and economic operation. In the meantime, the load dynamics have a substantial impact on the transient stability but has not well accounted in the generation dispatch stage. In this study, the dynamic load models and their uncertain variation are taken into account through a strategically selected set of load composition scenarios to approximate the whole uncertainty space. A multi‐objective evolutionary algorithm‐based hybrid solution process is then developed. The proposed method is verified on the New England 10‐machine 39‐bus system. Numeric results show that the model can effectively obtain Pareto solutions which are free from instability risk and robust to stochastic load composition variations.