Probabilistic Framework for Assessing Generation Interconnection Costs in Cluster-Based Queues

In addressing the dynamic complexities of grid interconnection for renewable energy sources, this paper introduces an innovative framework that transcends traditional deterministic models. Utilizing a case study from the Midcontinent Independent System Operator (MISO), the framework combines custom model building with transfer limit analysis, and introduces Monte Carlo simulations (MCS) to generate probabilistic interconnection cost assessments. This approach not only provides a more comprehensive understanding of cost implications but also suggests incorporating machine learning algorithms to enhance scenario-based studies. The resultant framework offers stakeholders a multifaceted view of potential costs, aligning closely with real-world uncertainties and enabling more nuanced, informed decision-making in generation interconnection.