The control strategy for distributed energy storage devices using fully diffusion strategy

The distributed energy storage device units (ESUs) in a DC energy storage power station (ESS) suffer the problems of overcharged and undercharged with uncertain initial state of charge (SOC), which may reduce the service period of ESUs. To address this problem, a distributed secondary control based on diffusion strategy is proposed. In the first layer, each ESUs operates with its local controller by droop control. In the second layer controller, diffusion strategy coordinate the SOC of multiple distributed ESUs with uncertain initial SOC. Compared with centralized control, the proposed method contributes to a higher scalability and reliability. Diffusion strategy allows adjacent nodes to diffuse and cooperate information in real-time, and it includes a stochastic gradient term. Thus, diffusion strategy can achieve a higher convergence rate and lower mean-square-error than consensus strategies. For the second layer controller, first the coordinated control of ESUs is transformed into two optimization problems: the balanced output voltage and the balanced SOC of each ESU. Then, diffusion strategy is used to solve the optimization problems. Finally, a simulation model is built to verify the effectiveness of diffusion strategy. The results show that diffusion strategy always ensure the stability irrespective of the combination topology. Compared with the widely used consensus method, diffusion strategy shows a good performance in realizing demand response for distributed ESUs.

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Publication Details

Journal
Heliyon
Published
2025-01-23
DOI
https://doi.org/10.1016/j.heliyon.2025.e42175
Primary Topic
Microgrid Control and Optimization
Type
article
Field-Weighted Citation Impact
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article

The control strategy for distributed energy storage devices using fully diffusion strategy

Z. Liu, Cencen Hong, Lin Xu, Ping Yang et al.
Heliyon
Microgrid Control and Optimization
article

The control strategy for distributed energy storage devices using fully diffusion strategy

Z. Liu, Cencen Hong, Lin Xu, Ping Yang, Ziwei Fan
article en

Abstract

The distributed energy storage device units (ESUs) in a DC energy storage power station (ESS) suffer the problems of overcharged and undercharged with uncertain initial state of charge (SOC), which may reduce the service period of ESUs. To address this problem, a distributed secondary control based on diffusion strategy is proposed. In the first layer, each ESUs operates with its local controller by droop control. In the second layer controller, diffusion strategy coordinate the SOC of multiple distributed ESUs with uncertain initial SOC. Compared with centralized control, the proposed method contributes to a higher scalability and reliability. Diffusion strategy allows adjacent nodes to diffuse and cooperate information in real-time, and it includes a stochastic gradient term. Thus, diffusion strategy can achieve a higher convergence rate and lower mean-square-error than consensus strategies. For the second layer controller, first the coordinated control of ESUs is transformed into two optimization problems: the balanced output voltage and the balanced SOC of each ESU. Then, diffusion strategy is used to solve the optimization problems. Finally, a simulation model is built to verify the effectiveness of diffusion strategy. The results show that diffusion strategy always ensure the stability irrespective of the combination topology. Compared with the widely used consensus method, diffusion strategy shows a good performance in realizing demand response for distributed ESUs.

HeliyonVol. 11(3)
Huawei Technologies (China) (CN), Nanjing Institute of Technology (CN), China Southern Power Grid (China) (CN), Power Grid Corporation (India) (IN), South China University of Technology (CN)
Affordable and clean energy
Openalex Percentile: Top 99%
Microgrid Control and Optimization
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The control strategy for distributed energy storage devices using fully diffusion strategy — Z. Liu, Cencen Hong, et al. · Heliyon (2025) | TGRS Research Map | TGRS