Learning from Optimal: Energy Procurement Strategies for Data Centers
Learning from Optimal: Energy Procurement Strategies for Data Centers
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DOI：
10.1145/3307772.3328308
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发表时间：
2019-06
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影响因子：
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通讯作者：
Sohaib Ahmad;Arielle Rosenthal;M. Hajiesmaili;R. Sitaraman
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文献类型：
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作者：
Sohaib Ahmad;Arielle Rosenthal;M. Hajiesmaili;R. Sitaraman
环境问题和电网价格上涨促使数据中心所有者投资现场可再生能源。然而，这些来源带来了挑战，因为它们不可靠且断断续续。为了缓解这些问题，数据中心正在整合能源存储系统。这为电费降低带来了机会，因为储能可用于电力市场套利。我们提出了两种基于监督学习的算法，LearnBuy，学习购买量，LearnStore，学习存储量，以解决这个能源采购问题。这些算法利用“从最优学习”的思想，通过使用离线优化生成的值作为训练的标签。我们测试我们的算法在一般情况下，考虑购买和出售回电网，和一个特殊的情况下，考虑只从电网购买。在一般情况下，与基线启发式方法相比，LearnStore实现了10- 16%的减少，而在特殊情况下，与现有技术相比，LearnBuy实现了7%的减少。
Environmental concerns and rising grid prices have motivated data center owners to invest in on-site renewable energy sources. However, 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 opportunity 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.