A Bi-Level Capacity Configuration Model for Hybrid Energy Storage Considering SOC Self-Recovery

The configuration of a hybrid energy storage system (HESS) plays a pivotal role in mitigating wind power fluctuations and enabling primary frequency regulation, thereby enhancing the active power support capability of wind power integration systems. However, most existing studies on HESS capacity configuration overlook the self-recovery control of the state of charge (SOC), creating challenges in sustaining capacity during long-term operation. This omission can impair frequency regulation performance, increase capacity requirements, and shorten battery lifespan. To address these challenges, this study proposes a bi-level planning–operation capacity configuration model that explicitly incorporates SOC self-recovery control. In the operation layer, a variable-baseline charging/discharging strategy is developed to restore SOC by balancing positive and negative energy over a 24-h period, with the goal of maximizing daily operational benefits. In the planning layer, the annualized net life-cycle cost of the HESS is minimized by configuring storage capacity based on feedback from the operation layer. The two layers operate iteratively to achieve coordinated optimization of capacity sizing and control strategy. Case study results demonstrate the effectiveness of the proposed method. Compared with a configuration without considering SOC self-recovery, the proposed approach reduces the 1-min wind power fluctuation rate to 3.53%, lowers the mean squared frequency error to 0.000084, and decreases the annualized net life-cycle cost by 545,000 CNY/MWh.

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Publication Details

Journal
Energy Engineering
Published
2025-01-01
DOI
https://doi.org/10.32604/ee.2025.069346
Primary Topic
Microgrid Control and Optimization
Type
article
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0.00
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article

A Bi-Level Capacity Configuration Model for Hybrid Energy Storage Considering SOC Self-Recovery

Zhen-Jun Zhuang, Tianhui Zhang, Fan Chen, Zihan Ma et al.
Energy Engineering
Microgrid Control and Optimization
article

A Bi-Level Capacity Configuration Model for Hybrid Energy Storage Considering SOC Self-Recovery

Zhen-Jun Zhuang, Tianhui Zhang, Fan Chen, Zihan Ma, Qiang Zhang, Man Wang
article en

Abstract

The configuration of a hybrid energy storage system (HESS) plays a pivotal role in mitigating wind power fluctuations and enabling primary frequency regulation, thereby enhancing the active power support capability of wind power integration systems. However, most existing studies on HESS capacity configuration overlook the self-recovery control of the state of charge (SOC), creating challenges in sustaining capacity during long-term operation. This omission can impair frequency regulation performance, increase capacity requirements, and shorten battery lifespan. To address these challenges, this study proposes a bi-level planning–operation capacity configuration model that explicitly incorporates SOC self-recovery control. In the operation layer, a variable-baseline charging/discharging strategy is developed to restore SOC by balancing positive and negative energy over a 24-h period, with the goal of maximizing daily operational benefits. In the planning layer, the annualized net life-cycle cost of the HESS is minimized by configuring storage capacity based on feedback from the operation layer. The two layers operate iteratively to achieve coordinated optimization of capacity sizing and control strategy. Case study results demonstrate the effectiveness of the proposed method. Compared with a configuration without considering SOC self-recovery, the proposed approach reduces the 1-min wind power fluctuation rate to 3.53%, lowers the mean squared frequency error to 0.000084, and decreases the annualized net life-cycle cost by 545,000 CNY/MWh.

Energy EngineeringVol. 0(0)
Openalex Percentile: Top 69%
Microgrid Control and Optimization
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A Bi-Level Capacity Configuration Model for Hybrid Energy Storage Considering SOC Self-Recovery — Zhen-Jun Zhuang, Tianhui Zhang, et al. · Energy Engineering (2025) | TGRS Research Map | TGRS