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Environmental concerns and rising grid prices have motivated data center owners to invest in on-site renewable energy sources. How- ever, these sources present challenges as they are unreliable and intermittent. In an effort to mitigate these issues, data centers are incorporating energy storage systems. This introduces the oppor- tunity for electricity bill reduction, as energy storage can be used for power market arbitrage. We present two supervised learning-based algorithms, LearnBuy, that learns the amount to purchase, and LearnStore, that learns the amount to store, to solve this energy procurement problem. These algorithms utilize the idea of "learning from optimal" by using the values generated by the offline optimization as a label for training. We test our algorithms on a general case, considering buying and selling back to the grid, and a special case, considering only buying from the grid. In the general case, LearnStore achieves a 10-16% reduction compared to baseline heuristics, whereas in the special case, LearnBuy achieves a 7% reduction compared to prior art.
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Online EV Scheduling Algorithms for Adaptive Charging Networks with Global Peak Constraints
Electricity bill constitutes a significant portion of operational costs for large scale data centers. Empowering data centers with on-site storages can reduce the electricity bill by shaping the energy procurement from deregulated electricity markets with real-time price fluctuations. This work focuses on designing energy procurement and storage management strategies to minimize the electricity bill of storage-assisted data centers. Designing such strategies is challenging since the net energy demand of the data center and electricity market prices are not known in advance, and the underlying problem is coupled over time due to evolution of the storage level. Using competitive ratio as the performance measure, we propose an online algorithm that determines the energy procurement and storage management strategies using a threshold based policy. Our algorithm achieves the optimal competitive ratio of as a function of the price fluctuation ratio. We validate the algorithm using data traces from electricity markets and data-center energy demands. The results show that our algorithm achieves close to the offline optimal performance and outperforms existing alternatives.%
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- PAR ID:
- 10132913
- Date Published:
- Journal Name:
- IEEE Transactions on Sustainable Computing
- ISSN:
- 2377-3790
- Page Range / eLocation ID:
- 1 to 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TSUSC.2020.2979854
