Microgrid energy management with energy storage systems: a review
Microgrids (MGs) are playing a fundamental role in the transition of energy systems towards a low carbon future due to the advantages of a highly efficient network architecture for flexible integration of various DC/AC loads, distributed renewable energy sources, and energy storage systems, as well...
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sg-ntu-dr.10356-1696132023-07-28T15:40:03Z Microgrid energy management with energy storage systems: a review Liu, Xiong Zhao, Tianyang Deng, Hui Wang, Peng Liu, Jizhen Blaabjerg, Frede School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Architecture Energy Management Microgrids (MGs) are playing a fundamental role in the transition of energy systems towards a low carbon future due to the advantages of a highly efficient network architecture for flexible integration of various DC/AC loads, distributed renewable energy sources, and energy storage systems, as well as a more resilient and economical on/off-grid control, operation, and energy management. However, MGs, as newcomers to the utility grid, are also facing challenges due to economic deregulation of energy systems, restructuring of generation, and market-based operation. This paper comprehensively summarizes the published research works in the areas of MGs and related energy management modelling and solution techniques. First, MGs and energy storage systems are classified into multiple branches and typical combinations as the backbone of MG energy management. Second, energy management models under exogenous and endogenous uncertainties are summarized and extended to transactive energy management. Mathematical programming, adaptive dynamic programming, and deep reinforcement learning-based solution methods are investigated accordingly, together with their implementation schemes. Finally, problems for future energy management systems with dynamics-captured critical component models, stability constraints, resilience awareness, market operation, and emerging computational techniques are discussed. Published version This work was supported in part by the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources under Grant LAPS21002, in part by the National Natural Science Foundation of China under Grant 52061635102, in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2021A1515110583. 2023-07-26T03:01:58Z 2023-07-26T03:01:58Z 2023 Journal Article Liu, X., Zhao, T., Deng, H., Wang, P., Liu, J. & Blaabjerg, F. (2023). Microgrid energy management with energy storage systems: a review. CSEE Journal of Power and Energy Systems, 9(2), 483-504. https://dx.doi.org/10.17775/CSEEJPES.2022.04290 2096-0042 https://hdl.handle.net/10356/169613 10.17775/CSEEJPES.2022.04290 2-s2.0-85152621072 2 9 483 504 en CSEE Journal of Power and Energy Systems © 2022 CSEE. Published by IEEE. This is an open-access article distributed under the terms of the Creative Commons Attribution License. application/pdf |
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Microgrids (MGs) are playing a fundamental role in the transition of energy systems towards a low carbon future due to the advantages of a highly efficient network architecture for flexible integration of various DC/AC loads, distributed renewable energy sources, and energy storage systems, as well as a more resilient and economical on/off-grid control, operation, and energy management. However, MGs, as newcomers to the utility grid, are also facing challenges due to economic deregulation of energy systems, restructuring of generation, and market-based operation. This paper comprehensively summarizes the published research works in the areas of MGs and related energy management modelling and solution techniques. First, MGs and energy storage systems are classified into multiple branches and typical combinations as the backbone of MG energy management. Second, energy management models under exogenous and endogenous uncertainties are summarized and extended to transactive energy management. Mathematical programming, adaptive dynamic programming, and deep reinforcement learning-based solution methods are investigated accordingly, together with their implementation schemes. Finally, problems for future energy management systems with dynamics-captured critical component models, stability constraints, resilience awareness, market operation, and emerging computational techniques are discussed. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Liu, Xiong Zhao, Tianyang Deng, Hui Wang, Peng Liu, Jizhen Blaabjerg, Frede |
format |
Article |
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Liu, Xiong Zhao, Tianyang Deng, Hui Wang, Peng Liu, Jizhen Blaabjerg, Frede |
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Liu, Xiong |
title |
Microgrid energy management with energy storage systems: a review |
title_short |
Microgrid energy management with energy storage systems: a review |
title_full |
Microgrid energy management with energy storage systems: a review |
title_fullStr |
Microgrid energy management with energy storage systems: a review |
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Microgrid energy management with energy storage systems: a review |
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microgrid energy management with energy storage systems: a review |
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2023 |
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https://hdl.handle.net/10356/169613 |
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1773551306908106752 |