A Decomposition-Coordination Planning Method for Flexible Generation Resources in Isolated Microgrids
Low computational efficiency is a general drawback of the existing mixed integer programming (MIP)-based algorithms used for determining the size of flexible generation resources (FGRs), e.g., microturbines (MTs) and battery storage systems (BSs), for isolated microgrids ($\text{I}\mu $ Gs). The sim...
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doaj-63ccba92ca0f4968876493290b48c6762021-03-29T23:01:55ZengIEEEIEEE Access2169-35362019-01-017767207673010.1109/ACCESS.2019.29227568736230A Decomposition-Coordination Planning Method for Flexible Generation Resources in Isolated MicrogridsPing Liu0https://orcid.org/0000-0002-6829-1276Zexiang Cai1Peng Xie2Xiaohua Li3Yongjun Zhang4https://orcid.org/0000-0002-1135-6788School of Electric Power, South China University of Technology, Guangzhou, ChinaSchool of Electric Power, South China University of Technology, Guangzhou, ChinaSchool of Electric Power, South China University of Technology, Guangzhou, ChinaSchool of Electric Power, South China University of Technology, Guangzhou, ChinaSchool of Electric Power, South China University of Technology, Guangzhou, ChinaLow computational efficiency is a general drawback of the existing mixed integer programming (MIP)-based algorithms used for determining the size of flexible generation resources (FGRs), e.g., microturbines (MTs) and battery storage systems (BSs), for isolated microgrids ($\text{I}\mu $ Gs). The simulation of these algorithms can consume dozens of hours, with large quantities of stochastic scenarios considered. In this paper, a decomposition-coordination optimization method is proposed to determine the optimal capacities of the FGRs accurately and efficiently when more than hundreds of stochastic scenarios exist. For energy balancing of the $\text{I}\mu \text{G}$ , a worst-case scenario is selected from the stochastic scenarios to determine the feasible capacity range of the MT. Based on the idea to divide the stochastic scenarios into the power-deficiency and power-surplus scenario set, the two scenario sets are separately considered in the decomposition step to realizing power balancing for the $\text{I}\mu \text{G}$ . The coordination step adopts the pattern search (PS) technique to obtain the optimal capacities of the FGRs with the intent of minimizing the total capital cost of the $\text{I}\mu \text{G}$ . The simulations are performed to validate the accuracy of the proposed method. Relative to the general MIP model, the proposed method has nearly identical accuracy and better computational performance.https://ieeexplore.ieee.org/document/8736230/Battery storagedecomposition-coordination methodisolated microgridmicroturbineoptimal sizingrenewable energy |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ping Liu Zexiang Cai Peng Xie Xiaohua Li Yongjun Zhang |
spellingShingle |
Ping Liu Zexiang Cai Peng Xie Xiaohua Li Yongjun Zhang A Decomposition-Coordination Planning Method for Flexible Generation Resources in Isolated Microgrids IEEE Access Battery storage decomposition-coordination method isolated microgrid microturbine optimal sizing renewable energy |
author_facet |
Ping Liu Zexiang Cai Peng Xie Xiaohua Li Yongjun Zhang |
author_sort |
Ping Liu |
title |
A Decomposition-Coordination Planning Method for Flexible Generation Resources in Isolated Microgrids |
title_short |
A Decomposition-Coordination Planning Method for Flexible Generation Resources in Isolated Microgrids |
title_full |
A Decomposition-Coordination Planning Method for Flexible Generation Resources in Isolated Microgrids |
title_fullStr |
A Decomposition-Coordination Planning Method for Flexible Generation Resources in Isolated Microgrids |
title_full_unstemmed |
A Decomposition-Coordination Planning Method for Flexible Generation Resources in Isolated Microgrids |
title_sort |
decomposition-coordination planning method for flexible generation resources in isolated microgrids |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
Low computational efficiency is a general drawback of the existing mixed integer programming (MIP)-based algorithms used for determining the size of flexible generation resources (FGRs), e.g., microturbines (MTs) and battery storage systems (BSs), for isolated microgrids ($\text{I}\mu $ Gs). The simulation of these algorithms can consume dozens of hours, with large quantities of stochastic scenarios considered. In this paper, a decomposition-coordination optimization method is proposed to determine the optimal capacities of the FGRs accurately and efficiently when more than hundreds of stochastic scenarios exist. For energy balancing of the $\text{I}\mu \text{G}$ , a worst-case scenario is selected from the stochastic scenarios to determine the feasible capacity range of the MT. Based on the idea to divide the stochastic scenarios into the power-deficiency and power-surplus scenario set, the two scenario sets are separately considered in the decomposition step to realizing power balancing for the $\text{I}\mu \text{G}$ . The coordination step adopts the pattern search (PS) technique to obtain the optimal capacities of the FGRs with the intent of minimizing the total capital cost of the $\text{I}\mu \text{G}$ . The simulations are performed to validate the accuracy of the proposed method. Relative to the general MIP model, the proposed method has nearly identical accuracy and better computational performance. |
topic |
Battery storage decomposition-coordination method isolated microgrid microturbine optimal sizing renewable energy |
url |
https://ieeexplore.ieee.org/document/8736230/ |
work_keys_str_mv |
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1724190328591745024 |