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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Bibliographic Details
Published in:IEEE transactions on smart grid Vol. 14; no. 4; pp. 2921 - 2933
Main Authors: Ma, Guolong, Li, Jianing, Zhang, Xiao-Ping
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.07.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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.
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content type line 14
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2022.3233910