Probability-weighted robust optimization for distributed generation planning in microgrids

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 i...

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Main Authors: Zhang, Cuo, Xu, Yan, Dong, Zhao Yang
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2020
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Online Access:https://hdl.handle.net/10356/141505
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1415052020-06-09T01:54:25Z Probability-weighted robust optimization for distributed generation planning in microgrids Zhang, Cuo Xu, Yan Dong, Zhao Yang School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Distributed Generation Planning Microgrids 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. MOE (Min. of Education, S’pore) 2020-06-09T01:54:25Z 2020-06-09T01:54:25Z 2018 Journal Article 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 0885-8950 https://hdl.handle.net/10356/141505 10.1109/TPWRS.2018.2849384 2-s2.0-85048873552 6 33 7042 7051 en IEEE Transactions on Power Systems © 2018 IEEE. All rights reserved.
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Distributed Generation Planning
Microgrids
spellingShingle Engineering::Electrical and electronic engineering
Distributed Generation Planning
Microgrids
Zhang, Cuo
Xu, Yan
Dong, Zhao Yang
Probability-weighted robust optimization for distributed generation planning in microgrids
description 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.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Zhang, Cuo
Xu, Yan
Dong, Zhao Yang
format Article
author Zhang, Cuo
Xu, Yan
Dong, Zhao Yang
author_sort Zhang, Cuo
title Probability-weighted robust optimization for distributed generation planning in microgrids
title_short Probability-weighted robust optimization for distributed generation planning in microgrids
title_full Probability-weighted robust optimization for distributed generation planning in microgrids
title_fullStr Probability-weighted robust optimization for distributed generation planning in microgrids
title_full_unstemmed Probability-weighted robust optimization for distributed generation planning in microgrids
title_sort probability-weighted robust optimization for distributed generation planning in microgrids
publishDate 2020
url https://hdl.handle.net/10356/141505
_version_ 1681058350858502144