A two-layer frequency control method for large-scale distributed energy storage clusters

Lin, Yujun; Li, Xing; Zhai, Bin; Yang, Qiufan; Zhou, Jianyu; Chen, Xia; Wen, Jinyu

doi:10.1016/j.ijepes.2022.108465

Cited by 8 publications

(1 citation statement)

References 39 publications

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“…For each BS, the power source capacity is 12 kW, and the charging/discharging power capacity and energy capacity of each BSBB are set to be 10 kW and 30 kWh, respectively [36]. The BSBB prices for operational reserve capacity, reserve deployment, and PFR/IR reserve capacity are set to be 12 $/MWh, 30 $/MWh, and 30 $/MW, respectively, and the maximum droop factor is set to be 50 [37], [38].…”

Section: A Basic Data Of Modified Ieee 14-bus Systemmentioning

confidence: 99%

Distributed Stochastic Scheduling of Massive Backup Batteries in Cellular Networks for Operational Reserve and Frequency Support Ancillary Services

Li,

Fang,

et al. 2024

Journal of Modern Power Systems and Clean Energy

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Base station (BS) backup batteries (BSBBs), with their dispatchable capacity, are potential demand-side resources for future power systems. To enhance the power supply reliability and post-contingency frequency security of power systems, we propose a two-stage stochastic unit commitment (UC) model incorporating operational reserve and post-contingency frequency support provisions from massive BSBBs in cellular networks, in which the minimum backup energy demand is considered to ensure BS power supply reliability. The energy, operational reserve, and frequency support ancillary services are co-optimized to handle the power balance and post-contingency frequency security in both forecasted and stochastic variable renewable energy (VRE) scenarios. Furthermore, we propose a dedicated and scalable distributed optimization framework to enable autonomous optimizations for both dispatching center (DC) and BSBBs. The BS model parameters are stored and processed locally, while only the values of BS decision variables are required to upload to DC under the proposed distributed optimization framework, which safeguards BS privacy effectively.Case studies on a modified IEEE 14-bus system demonstrate the effectiveness of the proposed method in promoting VRE accommodation, ensuring post-contingency frequency security, enhancing operational economics, and fully utilizing BSBBs' energy and power capacity. Besides, the proposed distributed optimization framework has been validated to converge to a feasible solution with near-optimal performance within limited iterations. Additionally, numerical results on the Guangdong 500 kV provincial power system in China verify the scalability and practicality of the proposed distributed optimization framework.

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Section: A Basic Data Of Modified Ieee 14-bus Systemmentioning

confidence: 99%