Premium
A complete decomposition and coordination algorithm for large‐scale hydrothermal optimal power flow problems
Author(s) -
Wang Chaoqun,
Wei Hua,
Tan Jiancheng
Publication year - 2017
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.22404
Subject(s) - convergence (economics) , mathematical optimization , decomposition , variable (mathematics) , scale (ratio) , benders' decomposition , flow (mathematics) , computer science , stability (learning theory) , newton's method , algorithm , power (physics) , mathematics , nonlinear system , quantum mechanics , machine learning , economics , biology , economic growth , ecology , mathematical analysis , physics , geometry
This paper presents a complete decomposition and coordination algorithm to solve large‐scale hydrothermal optimal power flow (HTOPF) problems. Based on the approximate Newton directions method, which decouples the first‐order Karush–Kuhn–Tucker conditions of the original problem, an HTOPF problem with cascaded hydro plants is decomposed into a thermal plant subproblem with independent optimal power flow solutions for each time period and a hydro plant subproblem combined with fixed and variable heads and cascaded plants issues. In order to verify the effectiveness of the proposed algorithm, numerical tests are performed on three large‐scale test systems with up to 3120 buses and 7 531 915 primal–dual variables over 168 time periods. Test results show that the proposed algorithm gives excellent performances in convergence and stability. It not only reduces memory usage significantly but also decreases CPU time by about 65–75%. With parallel computing, it is capable of achieving 10–20 times or even 1000 times speed without loss of optimality. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.