TY - JOUR
T1 - Microgrid Energy Management with Energy Storage Systems
T2 - A Review
AU - Liu, Xiong
AU - Zhao, Tianyang
AU - Deng, Hui
AU - Wang, Peng
AU - Liu, Jizhen
AU - Blaabjerg, Frede
N1 - DOI linket linker til PDF-filen
PY - 2023/3
Y1 - 2023/3
N2 - 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.
AB - 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.
KW - Architecture
KW - energy management
KW - energy storage systems
KW - microgrids
KW - optimization
KW - uncertainty models
UR - http://www.scopus.com/inward/record.url?scp=85152621072&partnerID=8YFLogxK
U2 - 10.17775/CSEEJPES.2022.04290
DO - 10.17775/CSEEJPES.2022.04290
M3 - Review article
SN - 2096-0042
VL - 9
SP - 483
EP - 504
JO - CSEE Journal of Power and Energy Systems
JF - CSEE Journal of Power and Energy Systems
IS - 2
M1 - 9979679
ER -