Open AccessInteger quadratic programming model for dynamic VAR compensation considering short‐term voltage stability
Author(s) -
Wu Liang,
Guan Lin
Publication year - 2019
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.2018.6102
Subject(s) - mathematical optimization , quadratic programming , electric power system , stability (learning theory) , integer programming , control theory (sociology) , mathematics , quadratic equation , term (time) , integer (computer science) , ac power , linear programming , computer science , voltage , power (physics) , engineering , physics , geometry , control (management) , quantum mechanics , machine learning , artificial intelligence , electrical engineering , programming language
Dynamic VAR compensation (DVC) planning is important in power system planning to enhance short‐term voltage stability. However, since its optimisations models include differential algebraic equations (DAEs), existing methods cannot directly solve the problem as that in the optimal power flow problem. In this study, the authors design an integer quadratic programming (IQP) model for the DVC planning problem which can simultaneously optimise the sites and size of DVCs. On the basis of the defined dynamic correlation indices, a quadratic programming objective function is defined to minimise the total investment of DVCs as well as to avoid redundant installation of DVCs on adjacent buses. Subsequently, sensitivity coefficients about the control effect of DVCs are introduced to form the linear inequations as the substitute for DAEs in constraints. Then, an iterative process is applied to solve the IQP. The proposed method is applied on a modified NE 39‐bus system and a real power grid. Comparisons to the existing methods are carried out to show its superiority in model complexity and solving efficiency.