Energy Economics • AI Infrastructure • Applied Econometrics
Alan Lamb. Applied economist working on energy markets, electricity demand, and FERC regulatory policy.
M.S. Applied Economics, University of Maryland. Expected December 2026.
Between 2019 and 2025, electricity demand in ERCOT grew 27 percent while PJM and MISO were essentially flat. This paper estimates how much of that divergence data center investment can explain, using a four-layer identification strategy across three balancing authorities. The headline result is a 34.8 index-point divergence in ERCOT minimum hourly demand above a synthetic counterfactual. The paper documents the absence of public load-side queue data as a policy finding.
Read on SSRN • GitHub • Replication Data (Dataverse) • Interactive Dashboard
April 2026 • University of Maryland • Synthetic Control • DiD • Panel Regression • ARIMA • XGBoost
A policy brief arguing that FERC should require grid operators to publish large load interconnection queue data on the same terms currently applied to generation interconnection. Grounded in the empirical results of the companion paper, it traces thirty years of FERC precedent and proposes a specific regulatory mechanism that does not require new legislation.
April 2026 • University of Maryland • Energy Policy • FERC • Interconnection Queue
Classification of 7,660 generating units in the PJM footprint using the 2024 EIA-860 survey. Seven models from logistic regression to XGBoost. The random forest reaches a test AUC of 0.979 and an F-score of 0.866, and the retired profile is old, small, and fuel-inflexible. An extension scores the operating fleet to identify where today's capacity most resembles units the market has already pushed out.
Summer 2026 • EIA-860 • Logit • GAM • Random Forest • XGBoost • Python