Impacts of Uncertainty in Transmission Interconnection on Energy Transition

Description:

This paper investigates the impact of uncertainty in interconnection cost allocation on generators’ decision to stay in the process. The study delves into a three-stage interconnection process that electric transmission system operators mandate for generators seeking grid access. Recent years have seen a surge in interconnection requests alongside a persistent trend of high project withdrawals, leading to a challenging backlog. This backlog poses a significant threat to electric grid resilience and energy transition, which is essential to combat climate change. A key reason for the high withdrawal rates is the uncertainty associated with the interconnection cost allocation under current policy. To estimate this impact, the study employs a dynamic discrete choice model with Bayesian learning to capture the evolving means and variances of interconnection cost signals across different stages of the process, as cost uncertainty decreases with progress. Parameter estimates highlight that generators are more likely to stay when cost uncertainty is high, emphasizing the value of learning. Furthermore, the effect of network upgrade costs varies by fuel source, with wind generators being most sensitive. The paper presents two counterfactual analyses to inform discussions concerning FERC Order 2023. First, I show that interconnection studies should prioritize providing more accurate information early in the process to encourage early learning, thus facilitating early decision-making to alleviate backlog issues. Second, I find increasing withdrawal penalties may not significantly address backlog concerns. Additionally, subsidy policies regarding network upgrades may be most effective when tailored to specific fuel types.