Open AccessNon‐deterministic optimal power flow considering the uncertainties of wind power and load demand by multi‐objective information gap decision theory and directed search domain method
Author(s) -
Rahmani Shima,
Amjady Nima
Publication year - 2018
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
ISSN - 1752-1424
DOI - 10.1049/iet-rpg.2018.5021
Subject(s) - robustness (evolution) , power flow , mathematical optimization , computer science , wind power , probabilistic logic , electric power system , demand response , power (physics) , mathematics , engineering , electricity , artificial intelligence , biochemistry , chemistry , physics , quantum mechanics , electrical engineering , gene
Optimal power flow (OPF) as an important operation function of wind power‐integrated power systems encounters the uncertainties of load demand and wind power. To cope with these uncertainty sources, various OPF models including deterministic OPF, probabilistic OPF, scenario‐based OPF, stochastic OPF, robust OPF and recently information gap decision theory (IGDT)‐based OPF have been presented in the literature. A multi‐objective IGDT‐based AC OPF model is presented which can simultaneously optimise various uncertainty horizons pertaining to load demands and wind powers considering the specified robustness level. Another main contribution of this study is presenting an effective directed search domain (DSD)‐based multi‐objective solution method to solve the proposed multi‐objective IGDT‐based AC OPF problem. The proposed OPF model and solution approach are tested on the IEEE 118‐bus test system and the obtained results are compared with the results of other OPF models and solution methods. These comparisons illustrate the effectiveness of the proposed multi‐objective IGDT‐based AC OPF model as well as the proposed DSD‐based multi‐objective solution method.