Energy Storage Capacity Optimization for Improving the Autonomy of Grid-Connected Microgrid

To support the autonomy and economy of grid-connected microgrid (MG), we propose an energy storage system (ESS) capacity optimization model considering the internal energy autonomy indicator and grid supply point (GSP) resilience management method to quantitatively characterize the energy balance an...

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Vydáno v:	IEEE transactions on smart grid Ročník 14; číslo 4; s. 2921 - 2933
Hlavní autoři:	Ma, Guolong, Li, Jianing, Zhang, Xiao-Ping
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.07.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Autonomy Computer simulation Costs decomposition-based multi-objective evolutionary algorithm (MOEA/D) direct load control (DLC) Distributed generation Energy storage Energy storage system (ESS) capacity optimization Evolutionary algorithms Fluctuations grid supply point (GSP) resilience management Internal energy internal energy autonomy Investment Multiple objective analysis Optimization Optimization models power fluctuation smoothing Real time Renewable energy sources Resilience Smoothing Storage capacity uncertainties Uncertainty Unit commitment
ISSN:	1949-3053, 1949-3061
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Abstract	To support the autonomy and economy of grid-connected microgrid (MG), we propose an energy storage system (ESS) capacity optimization model considering the internal energy autonomy indicator and grid supply point (GSP) resilience management method to quantitatively characterize the energy balance and power stability characteristics. Based on these, we establish a three-stage coupled model including investment decision, day-ahead operation strategy, and real-time power fluctuation smoothing control. Investment processes solve the balance of ESS investment and internal energy autonomy. Day-ahead operational scheduling mainly solves unit commitment problems in MG, which are constrained by the ESS capacities. For real-time power fluctuation smoothing, we propose a power fluctuation smoothing control strategy, coordinated by ESS and direct load control (DLC), to achieve GSP resilience management. Furthermore, we use Monte Carlo Simulations, stochastic scenario combinations, and uncertainty set to characterize the fluctuations of both supply and demand at the above different stages, respectively. To solve the established model with multi-objective, multi-uncertainties, and multi-stage coupling, a robust counterpart method has been used to convert the uncertainty problem to the deterministic one, and then decomposition-based multi-objective evolutionary algorithm (MOEA/D) to efficiently used to solve the problem. Finally, simulations are conducted to verify the effectiveness of the proposed model.
AbstractList	To support the autonomy and economy of grid-connected microgrid (MG), we propose an energy storage system (ESS) capacity optimization model considering the internal energy autonomy indicator and grid supply point (GSP) resilience management method to quantitatively characterize the energy balance and power stability characteristics. Based on these, we establish a three-stage coupled model including investment decision, day-ahead operation strategy, and real-time power fluctuation smoothing control. Investment processes solve the balance of ESS investment and internal energy autonomy. Day-ahead operational scheduling mainly solves unit commitment problems in MG, which are constrained by the ESS capacities. For real-time power fluctuation smoothing, we propose a power fluctuation smoothing control strategy, coordinated by ESS and direct load control (DLC), to achieve GSP resilience management. Furthermore, we use Monte Carlo Simulations, stochastic scenario combinations, and uncertainty set to characterize the fluctuations of both supply and demand at the above different stages, respectively. To solve the established model with multi-objective, multi-uncertainties, and multi-stage coupling, a robust counterpart method has been used to convert the uncertainty problem to the deterministic one, and then decomposition-based multi-objective evolutionary algorithm (MOEA/D) to efficiently used to solve the problem. Finally, simulations are conducted to verify the effectiveness of the proposed model.
Author	Li, Jianing Ma, Guolong Zhang, Xiao-Ping
Author_xml	– sequence: 1 givenname: Guolong orcidid: 0000-0002-3102-705X surname: Ma fullname: Ma, Guolong email: GXM039@student.bham.ac.uk organization: Department of Electronic, Electrical and Systems Engineering, School of Engineering, University of Birmingham, Birmingham, U.K – sequence: 2 givenname: Jianing orcidid: 0000-0003-4887-2732 surname: Li fullname: Li, Jianing email: jianing.li@ieee.org organization: Department of Energy Advisory, WSP UK, Birmingham, U.K – sequence: 3 givenname: Xiao-Ping orcidid: 0000-0003-0995-4989 surname: Zhang fullname: Zhang, Xiao-Ping email: x.p.zhang@bham.ac.uk organization: Department of Electronic, Electrical and Systems Engineering, School of Engineering, University of Birmingham, Birmingham, U.K
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SubjectTerms	Autonomy Computer simulation Costs decomposition-based multi-objective evolutionary algorithm (MOEA/D) direct load control (DLC) Distributed generation Energy storage Energy storage system (ESS) capacity optimization Evolutionary algorithms Fluctuations grid supply point (GSP) resilience management Internal energy internal energy autonomy Investment Multiple objective analysis Optimization Optimization models power fluctuation smoothing Real time Renewable energy sources Resilience Smoothing Storage capacity uncertainties Uncertainty Unit commitment
Title	Energy Storage Capacity Optimization for Improving the Autonomy of Grid-Connected Microgrid
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