Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/141505
Title: Probability-weighted robust optimization for distributed generation planning in microgrids
Authors: Zhang, Cuo
Xu, Yan
Dong, Zhao Yang
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Zhang, C., Xu, Y., & Dong, Z. Y. (2018). Probability-weighted robust optimization for distributed generation planning in microgrid. IEEE Transactions on Power Systems, 33(6), 7042-7051. doi:10.1109/TPWRS.2018.2849384
Journal: IEEE Transactions on Power Systems
Abstract: Distributed generation (DG) has been rapidly integrated into microgrids. However, uncertain power generation of intermittent DG such as wind turbines brings challenges to the DG planning problem. This paper proposes a novel probability-weighted robust optimization (PRO) method to allocate DG units including microturbines and wind turbines in microgrids, aiming to maximize the total profit over a long-term planning horizon. First, probability-weighted uncertainty sets are proposed to model uncertainties including wind turbine output and load demand during a long-term planning horizon which can fully cover the uncertainty spectrum and accurately represent the uncertainty probability distribution. Then, the PRO method optimizes DG sizing and locating under the worst uncertainty cases considering their occurrence probabilities. Therefore, the planning decision is robust against any possible uncertainty realization. Besides, a modified column-and-constraint generation algorithm is developed to solve the PRO problem. Simulation results show that the DG planning obtained by the proposed method can achieve full operating robustness.
URI: https://hdl.handle.net/10356/141505
ISSN: 0885-8950
DOI: 10.1109/TPWRS.2018.2849384
Rights: © 2018 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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