Open AccessVoltage stability in unbalanced power systems: a new complementarity constraints‐based approach
Author(s) -
Carpinelli Guido,
Caramia Pierluigi,
Russo Angela,
Varilone Pietro
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.2014.0990
Subject(s) - electric power system , probabilistic logic , control theory (sociology) , complementarity (molecular biology) , power flow , stability (learning theory) , continuation , mathematical optimization , operating point , computer science , interior point method , margin (machine learning) , maximum power principle , voltage , power (physics) , mathematics , engineering , electronic engineering , physics , control (management) , electrical engineering , quantum mechanics , artificial intelligence , machine learning , biology , genetics , programming language
Voltage stability (VS) has become a fundamental issue in the new, liberalised markets due to the fact that the new power systems are approaching more and more the stability limits. Then, several approaches were proposed in the relevant literature to find the critical conditions and recently the problem was faced also with reference to unbalanced three‐phase power systems. The unbalances, in fact, can be responsible of more critical stability conditions than in balanced power systems. Continuation power flow and optimal power flows were applied to analyse such conditions. This study deals with VS analysis in unbalanced power systems and proposes a new optimisation model to determine the critical point based on the use of complementarity constraints. In particular, the maximum stability margin is calculated by a single‐stage or a multistage procedure that accounts for the relationship between the actual operating point and the maximum loading point. In addition, the multistage maximum stability margin problem is formulated also in a probabilistic framework to account for the uncertainties affecting the input data (e.g. load powers). An application is presented on a test system highlighting the feasibility and the goodness of the proposed technique.