Gaussian process-based bilevel optimization with critical load restoration for system resilience improvement through data centers-to-grid scheme

Bilevel optimization problems can be used to represent the collaborative interaction between a power system and grid-connected entities. Meanwhile, internet data centers (IDCs), the newly emerged loads, can largely improve power systems’ resiliency owing to their flexibility on computing loads migration. However, most existing bilevel optimization techniques, for system restoration after disasters, assume that the entities’ behaviors are available to power systems for the decision-making, which may be untenable due to the independence and autonomy of IDCs. Thus, this work proposes a novel two-layer optimization framework based on Gaussian Process Regression to enhance power systems’ restoration capability after disasters by exploiting the potential of IDCs. The proposed two-layer model respects the information barriers between power systems and IDCs through a regression function representing IDCs’ response to power system decisions, which contribute to the protection of IDCs’ privacy and the significant improvement on the computational efficiency of the optimization problem without compromising accuracy. Moreover, compared to the conventional methods, the proposed restoration model considers the load-side operations and the varying load marginal values, such that the interests and physical properties of the lower layer can be closer to real scenarios. Two case studies validate the advantages of the proposed approach.