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CSEE Journal of Power and Energy Systems 2023, 9(2): 483-504 https://doi.org/10.17775/CSEEJPES.2022.04290
Open Access | Issue | Published: 09 December 2022

Microgrid Energy Management with Energy Storage Systems: A Review

Show Author's Information Xiong Liu1,  Tianyang Zhao1( ),  Hui Deng1,  Peng Wang2,  Jizhen Liu3,  Frede Blaabjerg4
Energy Electricity Research Center, Jinan University, Zhuhai 519070, China
School of Electrical and Electronic Engineering, Nanyang Technological University, 639798 Singapore
School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
Department of Energy Technology at Aalborg University, Denmark
Keywords:
optimization, energy management, Architecture, microgrids, energy storage systems, uncertainty models
Cite this article:
Liu X, Zhao T, Deng H, et al. Microgrid Energy Management with Energy Storage Systems: A Review. CSEE Journal of Power and Energy Systems, 2023, 9(2): 483-504. https://doi.org/10.17775/CSEEJPES.2022.04290
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Abstract Full text About this article

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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About this article

Microgrid Energy Management with Energy Storage Systems: A Review

Show Author's information Xiong Liu1, Tianyang Zhao1( ), Hui Deng1, Peng Wang2, Jizhen Liu3, Frede Blaabjerg4
Energy Electricity Research Center, Jinan University, Zhuhai 519070, China
School of Electrical and Electronic Engineering, Nanyang Technological University, 639798 Singapore
School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
Department of Energy Technology at Aalborg University, Denmark

Abstract

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.

Keywords: optimization, energy management, Architecture, microgrids, energy storage systems, uncertainty models

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Received: 29 June 2022
Revised: 07 October 2022
Accepted: 24 October 2022
Published: 09 December 2022
Issue date: March 2023

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