ShiftGuard: Towards Reliable 5G Network by Optimal Backup Power Allocation

Tang, Guoming; Wang, Yi; Lu, Hongyu

doi:10.1109/smartgridcomm47815.2020.9303003

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…This actually leads to a relatively low utilization, as given in this work. In practice, the generated renewable energy could supply to multiple BSs [5], so that the ROI and utilization of the renewable energy could be further improved.…”

Section: Rois Of Different Scenariosmentioning

confidence: 99%

“…Building and operating such large-scale BSs require enormous investments and resources. According to a field survey in the cities of Guangzhou and Shenzhen, China, the full-load power consumption of a typical 5G BS is about 2 ∼ 3 times of that of a 4G one [5]. Considering the ultra-dense deployment of 5G BSs, it could lead to a tenfold increase in energy consumption.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

BESS Aided Renewable Energy Supply Using Deep Reinforcement Learning for 5G and Beyond

Yuan

Tang

Guo

et al. 2022

IEEE Trans. on Green Commun. Netw.

Self Cite

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The year of 2020 has witnessed the unprecedented development of 5G networks, along with the widespread deployment of 5G base stations (BSs). Nevertheless, the enormous energy consumption of BSs and the incurred huge energy cost have become significant concerns for the mobile operators. As the continuous decline of the renewable energy cost, equipping the powerhungry BSs with renewable energy generators could be a sustainable solution. In this work, we propose an energy storage aided renewable energy supply solution for the BS, which could supply clean energy to the BS and store surplus energy for backup usage. Specifically, to flexibly regulate the battery's discharging/charging, we propose a deep reinforcement learning based regulating policy, which can adapt to the dynamical renewable energy generations as well as the varying power demands. Our experiments using the real-world data on renewable energy generations and power demands demonstrate that, our power supply solution can achieve an cost saving ratio of 77.9%, compared to the case with traditional power grid supply.

show abstract

Section: Rois Of Different Scenariosmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

BESS Aided Renewable Energy Supply Using Deep Reinforcement Learning for 5G and Beyond

Yuan

Tang

Guo

et al. 2022

IEEE Trans. on Green Commun. Netw.

Self Cite

View full text Add to dashboard Cite

show abstract

“…This actually leads to a relatively low utilization, as given in this work. In practice, the generated renewable energy could supply to multiple BSs [7], so that the ROI and utilization of the renewable energy could be further improved.…”

Section: Rois Of Different City and Type Deploymentmentioning

confidence: 99%

“…Building and operating such large-scale BSs require an enormous investment and consume many resources (e.g., power consumption). According to field surveys in the cities of Guangzhou and Shenzhen, China, the full-load power consumption of a typical 5G BS is about 2 ∼ 3 times of that of a 4G BS [7]. Considering the ultra-dense deployment of 5G BSs, it could lead to a tenfold increase in energy consumption.…”

Section: Introductionmentioning

confidence: 99%

BESS Aided Reconfigurable Energy Supply using Deep Reinforcement Learning for 5G and Beyond

Yuan,

Tang,

Guo

et al. 2021

Preprint

Self Cite

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The year of 2020 has witnessed the unprecedented development of 5G networks, along with the widespread deployment of 5G base stations (BSs). Nevertheless, the enormous energy consumption of BSs and the incurred huge energy cost have become significant concerns for the mobile operators. As the continuous decline of the renewable energy cost, equipping the power-hungry BSs with renewable energy generators could be a sustainable solution. In this work, we propose an energy storage aided reconfigurable renewable energy supply solution for the BS, which could supply clean energy to the BS and store surplus energy for backup usage. Specifically, to flexibly reconfigure the battery's discharging/charging operations, we propose a deep reinforcement learning based reconfiguring policy, which can adapt to the dynamical renewable energy generations as well as the varying power demands. Our experiments using the real-world data on renewable energy generations and power demands demonstrate that, our reconfigurable power supply solution can achieve an energy saving ratio of 74.8%, compared to the case with traditional power grid supply.

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Optimal Backup Power Allocation for 5G Base Stations

Tang

Guo

2022

GreenEdge: New Perspectives to Energy Management and Supply in Mobile Edge Computing

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ShiftGuard: Towards Reliable 5G Network by Optimal Backup Power Allocation

Cited by 6 publications

References 7 publications

BESS Aided Renewable Energy Supply Using Deep Reinforcement Learning for 5G and Beyond

BESS Aided Renewable Energy Supply Using Deep Reinforcement Learning for 5G and Beyond

BESS Aided Reconfigurable Energy Supply using Deep Reinforcement Learning for 5G and Beyond

Optimal Backup Power Allocation for 5G Base Stations

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