Locating optimal support generation units based on polynomial approximation of post‐contingency static stability and security regions boundaries
Author(s) -
Qiu Yiwei,
Luo Zhihao,
Chen Bo,
Zhao Qi,
Xia Bingqing,
Wu Hao,
Song Yonghua
Publication year - 2017
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2017.0653
Subject(s) - robustness (evolution) , contingency , electric power system , power flow , computer science , stability (learning theory) , mathematical optimization , polynomial , galerkin method , control theory (sociology) , power (physics) , mathematics , engineering , artificial intelligence , physics , control (management) , philosophy , biochemistry , quantum mechanics , mathematical analysis , finite element method , linguistics , machine learning , gene , chemistry , structural engineering
Post‐contingency support from the generation units is key to the long‐term stability and security of the electrical power system, thus, power companies hope to locate the optimal support generators, which will be planned for improving the ramping abilities to enhance the robustness of the system. To locate the optimal support generators, an extended security constrained optimal power flow model is proposed. Then, as the solution method, a static stability/security region‐based decomposition method is developed. Detailedly, by the polynomial approximation of static stability/security regions using a generalised Galerkin method, the post‐contingency stability and security constraints are transformed into simple polynomial expressions. Thus, the optimisation model for locating the optimal support generators can be easily formulated and solved. The method is verified using a case study in IEEE 118‐bus test system.
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