Joint Energy Trading and Computation Scheduling for Geo-Distributed Data Centers in Emergency Demand Response

The rapid growth of cloud computing has led to high energy consumption in data centers (DCs), significantly burdening the safe operation of the power grid. As DC loads are seen as emergency demand response (EDR) resources, they can be aggregated into virtual power plants (VPPs) for the energy market trading, enhancing the renewable energy consumption capacity and providing grid benefits. However, VPPs might opt to maintain the profits they gain from EDR as confidential, driven by self-interest. Additionally, the incompatibility between the EDR transactions and task scheduling can result in revenue loss for DCs. To address these challenges, we propose a joint energy trading and computation scheduling (JETCS) framework for geo-distributed DCs participating in the EDR of VPPs. We first formulate DCs' energy and revenue computation as a maxi-mization problem to balance task delay and energy consumption. Then, we employ contract theory to facilitate the energy trading between DCs and VPPs against the information asymmetry. Due to nonlinearity and the infeasibility of obtaining an analytical result, we propose the proximal Jacobian alternating direction method of multipliers (PJADMM) algorithm to find an optimal solution with low complexity. The simulation results demonstrate that our proposed framework successfully encourages VPPs to reveal their actual profits and enhances the benefits for both parties.