Open AccessIGDT‐based economic dispatch considering the uncertainty of wind and demand response
Author(s) -
Dai Xuemei,
Wang Ying,
Yang Shengchun,
Zhang Kaifeng
Publication year - 2019
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/iet-rpg.2018.5581
Subject(s) - demand response , mathematical optimization , robustness (evolution) , computer science , electric power system , operations research , wind power , economic dispatch , scheduling (production processes) , reliability engineering , power (physics) , electricity , engineering , mathematics , biochemistry , chemistry , physics , quantum mechanics , electrical engineering , gene
Integration of wind generation and demand response (DR) poses challenges for the power system operation due to their uncertain characteristics. In this study, an economic dispatching method considering the uncertainties of wind and DR is proposed. The method based on the information gap decision theory (IGDT) can be used to evaluate risks associated with uncertainties from risk averse (RA) and risk‐seeking (RS) perspectives. The RA IGDT‐based model can provide maximum tolerable robustness region for the required cost target. The RS IGDT‐based model can help achieve the lowest operation costs with desired uncertainties. The proposed model is bi‐level. The upper level subproblem aims to maximise (minimise) the allowable uncertainty level to satisfy the pre‐determined cost target, while the lower level subproblem is to maximise (minimise) possible cost considering the uncertainties. The bi‐level model is then transformed into a single level mixed integer linear programming problem that can be solved through commercial solves. Finally, the authors evaluate the performance of the IGDT‐based approach by simulations on the modified 6‐bus and IEEE 118‐bus systems. The results show that the proposed approach can provide suggestions for system operators to make appropriate scheduling plan based on the expected cost targets and risk preferences.