Pricing Data Center Demand Response
Demand response is crucial for the incorporation of renewable energy into the grid. In this paper, we focus on a particularly promising industry for demand response: data centers. We use simulations to show that, not only are data centers large loads, but they can provide as much (or possibly more) flexibility as large-scale storage if given the proper incentives. However, due to the market power most data centers maintain, it is difficult to design programs that are efficient for data center demand response. To that end, we propose that prediction-based pricing is an appealing market design, and show that it outperforms more traditional supply function bidding mechanisms in situations where market power is an issue. However, prediction-based pricing may be inefficient when predictions are inaccurate, and so we provide analytic, worst-case bounds on the impact of prediction error on the efficiency of prediction-based pricing. These bounds hold even when network constraints are considered, and highlight that prediction-based pricing is surprisingly robust to prediction error.
© ACM, Inc. This work was supported by NSF grants CCF 0830511, CNS 0911041, and CNS 0846025, DoE grant DE-EE0002890, ARO MURI grant W911NF-08-1-0233, Microsoft Research, Bell Labs, the Lee Center for Advanced Networking, and ARC grant FT0991594.
Accepted Version - DCDRpricing.pdf