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Power Demand Reshaping Using Energy Storage for Distributed Edge Clouds
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AI
DOI：10.1109/TPDS.2023.3347774.png)
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Abstract
En 中文
The booming edge computing market that is supported by the edge cloud (EC) infrastructure has brought huge operating costs, mainly the energy cost, to edge service providers. The energy cost in form of electricity bills usually consists of energy charge and demand charge, and the demand charge based on peak power may account for a large proportion of the energy cost given a significant fluctuating power curve. In this work, we investigate the backup battery characteristics and electricity charge tariffs at ECs and explore the corresponding cost-saving potential. Specifically, we transform the backup battery group into distributed battery energy storage system (BESS) and strategically schedule the BESS to minimize the energy cost of service providers. We then propose a deep reinforcement learning (DRL) based approach to BESS charging/discharging in coping with the dynamic power demand and BESS state at each EC. To enable better decision-making and speed up agent training, we further design the customized invalid action masking (IAM) method and apply the prioritized experience replay (PER) scheme. The experiment results based on real-world EC power traces show that the proposed approach can reduce the demand charge and overall electricity bill by up to 27% and 13%, respectively.
Keywords:
Costs
Batteries
Tariffs
Uninterruptible power systems
Energy storage
Real-time systems
Power demand
Demand charge
edge computing
energy storage
power shaving
distributed BESS
Journal
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5.2K
Citations:
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