Abstract:

The increasing penetration of distributed energy resources (DERs) necessitates advanced hosting capacity (HC) assessments to ensure reliable grid operation. Traditional static HC methods and Dynamic Operating Envelopes (DOEs) often rely on instantaneous voltage constraints, leading to overly conservative estimates. In this paper, we first propose a deterministic hosting capacity optimization model with a novel voltage violation duration constraint. Then, we build a graph convolutional recurrent network (GCRN)-based stochastic hosting capacity optimization (S-HC) framework. Finally, we propose a Surrogate Lagrangian Relaxation (SLR)-based temporal decomposition scheme to improve solving efficiency. Numerical results demonstrate the framework’s effectiveness in enhancing operational HC assessment and maximizing DER integration.