Optimal Batching and Pricing for Power Grid Interconnection Queues

Severe congestion in U.S. power grid interconnection queues has slowed the deployment of new generation. Recent operational reforms propose cluster studies and higher financial deposits to improve throughput, yet operational trade-offs remain unclear. We develop a queueing model that captures these levers by modeling cluster studies as batch service with economies of scale and financial deposits as admission prices that regulate both entry and project retention. A key feature of the model is a batch-level feedback mechanism. If any project in a cluster withdraws after a study, changes in network impacts and cost allocations force the remaining projects to be re-evaluated, generating endogenous rework. We analyze the system using a fluid approximation and show that it arises as a bona fide limit of the stochastic model under an appropriate scaling and yields asymptotically optimal batching and pricing decisions. The fluid analysis shows that batching alone improves throughput primarily in heavily overloaded systems, but can be counterproductive, even when the system is overloaded, if withdrawal-induced rework dominates economies of scale. Moreover, pricing can either complement or substitute for batching depending on system load, but in heavily congested regimes, it always complements rather than substitutes for batching, which strengthens the effectiveness of larger clusters. A case study calibrated to PJM, the largest regional transmission organization in the U.S., shows that batching alone increases throughput by up to 30% in highly congested stages, while jointly optimizing batching and pricing delivers additional gains exceeding 25% relative to batching alone in plausible regimes.