Voltage Profile Optimization of Active Distribution Networks Considering Dispatchable Capacity of 5G Base Station Backup Batteries

Zhou, Yiyao; Wang, Qianggang; Zou, Yao; Chi, Yuan; Zhou, Niancheng; Zhang, Xuefei; Li, Chen; Xia, Qinqin

doi:10.35833/mpce.2022.000453

Cited by 6 publications

(1 citation statement)

References 36 publications

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“…Besides, a rule-based mechanism is constructed to coordinate the spare capacity of massive distributed BSBB to provide secondary frequency regulation service [15]. Reference [16] considers 5G BSBBs as a new flexible resource and uses them to optimize the voltage profile of active distribution networks. Yet few studies have explored the utilization of dispatchable capacity of BSBB for providing operational reserve and frequency support ancillary services, which are of significant value for maintaining the power supply reliability and security in future power systems.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%