Open AccessN − 1 security assessment approach based on the steady‐state security distance
Author(s) -
Chen Sijie,
Chen Qixin,
Xia Qing,
Zhong Haiwang,
Kang Chongqing
Publication year - 2015
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.2015.0552
Subject(s) - contingency , margin (machine learning) , computer science , steady state (chemistry) , electric power system , contingency table , mathematical optimization , grid , index (typography) , operations research , reliability engineering , power (physics) , mathematics , engineering , machine learning , philosophy , linguistics , chemistry , physics , geometry , quantum mechanics , world wide web
The traditional N − 1 security assessment approaches give a limited perspective on the degree of power system security. This study proposes a novel security assessment approach using the index of post‐contingency steady‐state security distance (PCSSD). Two PCSSD models are established for contingencies causing or not causing system overloads, respectively. If the system is secure following a contingency, the PCSSD provides the shortest distance of the operating point (OP) to the post‐contingency steady‐state security region (PCSSR) boundaries, to indicate the OP's remaining security margin. If an overload condition arises after a contingency, the PCSSD provides the shortest distance of the OP to the PCSSR, to show the difficulty in eliminating power grid violations. With the index of the PCSSD, one can continuously quantify post‐contingency OPs whether they are secure or insecure and rank all possible contingencies according to their severity. The PCSSD model is a large‐scale non‐linear optimisation model which has to be calculated numerous times. A novel algorithm is proposed to improve the computational efficiency. It identifies the active boundaries of the PCSSR, and then approximates most of the PCSSDs by the corresponding ante‐contingency ones, reducing the number of optimisation problems to be solved. Case studies are performed to demonstrate the effectiveness of the proposed approach.