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Increasing the power-grid&#x2019;s flexibility is essential for expanding the integration of renewable energy sources in modern power grids. This work presents a new rebate auction framework that allows power grids to use cloud datacenters as managed loads to provide upward/downward grid flexibility. Since the energy consumption of datacenters is proportional to their computational workload, this paper presents a rebate auction framework that can induce cloud workload migrations between datacenters to correct energy imbalances. Unlike existing datacenter-based power grid balancing approaches that have only focused on providing downward flexibility and only considered owner-operated datacenters, the proposed framework provides bidirectional flexibility and accommodates both owner-operated and public cloud datacenters. Thus, providing a general framework for creating workload migrations between datacenters to balance the power grid. Because workloads on public cloud datacenters are managed by end-users, the proposed framework uses monetary incentives (auctioned rebates) to encourage large-scale end-users to migrate their cloud workloads between datacenters to correct energy imbalances. The use of monetary rewards as an incentive hides the complexity of grid-balancing from auction participants, who only participate in the auction to lower their cost, while grid-balancing happens as a result of workload migrations. This paper presents and compares two auction implementations under the proposed framework, a strategy-proof implementation that guarantees truthful bidding as a dominant strategy, but has NP-hard computational complexity, and an alternative implementation that does not guarantee truthful bidding, but has polynomial time complexity. Simulation results show that the proposed framework is effective in incentivizing cloud workload migrations to achieve the grid balancing goal and provides positive utility to all participants.

Rebate Auction Mechanisms for Bidirectional Grid Balancing Using Cloud Workload Migrations