Visible to the public Robust optimization of risk for power system based on information gap decision theory

TitleRobust optimization of risk for power system based on information gap decision theory
Publication TypeConference Paper
Year of Publication2015
AuthorsLi, X., He, Z., Zhang, S.
Conference Name2015 5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT)
Date PublishedNov. 2015
PublisherIEEE
ISBN Number978-1-4673-7106-3
KeywordsDecision support systems, decision theory, IEEE-30 system, information gap decision theory, Non-probabilistic uncertainty, nonprobabilistic uncertainties, optimisation, Optimization, Power industry, power system management, power system risk control, power system security, Power systems, pubcrawl170107, pubcrawl170108, risk control optimization, risk management, risk optimization, robust optimization of power system, robust optimization operation method, Robustness, Uncertainty
Abstract

Risk-control optimization has great significance for security of power system. Usually the probabilistic uncertainties of parameters are considered in the research of risk optimization of power system. However, the method of probabilistic uncertainty description will be insufficient in the case of lack of sample data. Thus non-probabilistic uncertainties of parameters should be considered, and will impose a significant influence on the results of optimization. To solve this problem, a robust optimization operation method of power system risk-control is presented in this paper, considering the non-probabilistic uncertainty of parameters based on information gap decision theory (IGDT). In the method, loads are modeled as the non-probabilistic uncertainty parameters, and the model of robust optimization operation of risk-control is presented. By solving the model, the maximum fluctuation of the pre-specified target can be obtained, and the strategy of this situation can be obtained at the same time. The proposed model is applied to the IEEE-30 system of risk-control by simulation. The results can provide the valuable information for operating department to risk management.

URLhttps://ieeexplore.ieee.org/document/7432273
DOI10.1109/DRPT.2015.7432273
Citation Keyli_robust_2015