Exploring the Cellular Base Station Dispatch Potential Towards Power System Frequency Regulation

Yong, Pei; Zhang, Ning; Liu, Yuxiao; Hou, Qingchun; Li, Yaowang; Chen, Kang

doi:10.1109/tpwrs.2021.3124141

Cited by 15 publications

(3 citation statements)

References 12 publications

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“…Typically, most of 5G BSs are constructed with BSBBs for a backup duration of 3 hours to meet the reliability requirements for power supply [15]. Thus, the minimum backup energy demand of BSBB k at time instant t can be calculated:…”

Section: A Evaluating Dispatchable Capacity Of Bsbbsmentioning

confidence: 99%

“…Reference [14] further investigates the potential of the spare capacity of BSBB in stabilizing the photovoltaic (PV) output in 5G BS microgrids. 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.…”

Section: Introductionmentioning

confidence: 99%

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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 Evaluating Dispatchable Capacity Of Bsbbsmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…Literature [4] considers the dual uncertainties of battery swap stations and photovoltaic power generation, and proposes a day-ahead economic dispatch method for electric vehicle battery swap stations based on chance constrained programming and probability sequence operations, which effectively reduces operating costs. Literature [5] points out the impact of base station energy storage scheduling on power system frequency regulation, estimates the feasible scheduling boundary for each base station to participate in frequency regulation, and then proposes a framework to coordinate BSs to provide frequency support. By introducing large-scale distributed base stations, improved frequency performance of power systems.…”

Section: Introductionmentioning

confidence: 99%

Optimization strategy of base station energy consumption based on predictive control

Liu,

Wang,

Wang

et al. 2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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This article focuses on the optimized operation of communication base stations, especially the effective utilization of energy storage batteries. Currently, base station energy storage batteries are often idle and do not participate in power supply, resulting in resource waste and battery life issues. Therefore, this paper uses the charge and discharge control of energy storage batteries, combined with wind and solar resources and time-of-use electricity prices, to achieve "peak shaving and valley filling" of base station load power and significantly reduce operating costs. The article proposes an optimal dispatch model from two stages: day-ahead and intra-day. By calculating the base station load and wind and solar forecast, the day-ahead economic operation strategy is derived, and the intra-day dispatch plan is continuously adjusted according to the actual situation. Through numerical example analysis, the effectiveness of the method proposed in this article is verified.

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A Robust Satisficing Decision Model for Aggregator to Participate in Electricity Market Considering Users’ Willingness to Respond

Zhan,

Xu,

et al. 2024

Lecture Notes in Electrical Engineering

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Exploring the Cellular Base Station Dispatch Potential Towards Power System Frequency Regulation

Cited by 15 publications

References 12 publications

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

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

Optimization strategy of base station energy consumption based on predictive control

A Robust Satisficing Decision Model for Aggregator to Participate in Electricity Market Considering Users’ Willingness to Respond

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