Increasing PV Hosting Capacity With an Adjustable Hybrid Power Flow Model

Physical constraints must be enforced when distributed energy resources, such as PV, are integrated into distribution network. Hosting capacity (HC) is thus introduced to define the maximum renewable generation that distribution system can accommodate. When the grid is further pushed towards decarbonization, improving HC becomes even more important. This work presents a hybrid adjustable power flow model to increase the uncertainty-proof HC, aiming to securely and cost-effectively utilize flexible resources. We propose a novel hybrid relaxation approach to convexifying a two-stage AC model with variable uncertainty set. An iterative algorithm is designed to solve the problem. We perform the case study in a single-phase 141-node system and a three-phase 33-bus system. The proposed approach shows promising performance in accuracy, convergence, and robustness.

Automated summary

Physical constraints are necessary for integrating distributed energy resources, specifically PV, into the distribution network. The concept of hosting capacity (HC) is introduced to determine the maximum renewable energy generation that the distribution system can accommodate. This study proposes a hybrid adjustable power flow model to enhance the HC and effectively utilize flexible resources in a secure and cost-efficient manner. A novel hybrid relaxation approach is presented to convexify a two-stage AC model with variable uncertainty sets, and an iterative algorithm is developed to solve the problem. Case studies on a single-phase 141-node system and a three-phase 33-bus system demonstrate the promising performance of the proposed approach in terms of accuracy, convergence, and robustness.