Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/82583
Title: Optimal power flow with worst-case scenarios considering uncertainties of loads and renewables
Authors: Ju, Chengquan
Wang, Peng
Keywords: Optimal power flow (OPF)
Renewable energy sources (RES)
Issue Date: 2016
Source: Ju, C., & Wang, P. (2016). Optimal power flow with worst-case scenarios considering uncertainties of loads and renewables. 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 1-7.
Abstract: The growing interest on RES gives traditional power systems an opportunity to evolve towards more sustainable and environmental entities, however the viability of RES would induce stability and reliability issues in power systems. In this paper, a DC optimal power flow (OPF) algorithm considering the worst-case scenario is proposed. It accounts for uncertainties brought by loads and renewable energy sources (RES), while in the meantime the highest reliability level of the system can be achieved. By assigning selected values with largest probabilities to random variables, the probabilistic OPF formulation is converted into a set of deterministic OPF problems in which the additional auxiliary constraints are implemented to represent the uncertain influences. The proposed OPF with the worst-case scenario is applied into an IEEE 14-bus and 57-bus benchmark power system. The results in the simulation along with other OPF techniques shows the validity and robustness of the algorithm.
URI: https://hdl.handle.net/10356/82583
http://hdl.handle.net/10220/42327
DOI: 10.1109/PMAPS.2016.7764128
Rights: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/PMAPS.2016.7764128].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers
ERI@N Conference Papers
IGS Conference Papers

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