Online Electricity Purchase for Data Center with Dynamic Virtual Battery from Flexibility Aggregation
<|reference_start|>Online Electricity Purchase for Data Center with Dynamic Virtual Battery from Flexibility Aggregation: As a critical component of modern infrastructure, data centers account for a huge amount of power consumption and greenhouse gas emission. This paper studies the electricity purchase strategy for a data center to lower its energy cost while integrating local renewable generation under uncertainty. To facilitate efficient and scalable decision-making, we propose a two-layer hierarchy where the lower layer consists of the operation of all electrical equipment in the data center and the upper layer determines the procurement and dispatch of electricity. At the lower layer, instead of device-level scheduling in real time, we propose to exploit the inherent flexibility in demand, such as thermostatically controlled loads and flexible computing tasks, and aggregate them into virtual batteries. By this means, the upper-layer decision only needs to take into account these virtual batteries, the size of which is generally small and independent of the data center scale. We further propose an online algorithm based on Lyapunov optimization to purchase electricity from the grid with a manageable energy cost, even though the prices, renewable availability, and battery specifications are uncertain and dynamic. In particular, we show that, under mild conditions, our algorithm can achieve bounded loss compared with the offline optimal cost, while strictly respecting battery operational constraints. Extensive simulation studies validate the theoretical analysis and illustrate the tradeoff between optimality and conservativeness.<|reference_end|>
@article{gao2024online,
    title={Online Electricity Purchase for Data Center with Dynamic Virtual Battery
  from Flexibility Aggregation},
    author={Kekun Gao, Yuejun Yan, Yixuan Liu, Endong Liu, and Pengcheng You},
    journal={arXiv preprint arXiv:2404.19387},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.19387},
    primaryClass={eess.SY cs.SY}
}