Budgeted Day-Ahead Reserve Allocation from Conformal Forecasts under Delayed Feedback

Abstract Forecast-driven intelligent systems must allocate limited interventions before the outcomes needed for feedback become available. We study this problem through day-ahead reserve allocation from multivariate conformal load forecasts under a fixed annual budget of committed days. A shortfall-sum rule uses released coverage feedback to place commitments, while a calendar-based contract enforces the two-day feedback delay, handles missing observations, and preserves the nonreplenishable budget. Local deficit accounting gives a window bound with an explicit budget-blocking term and connects modeled coverage service to finite zonal adequacy. On seven annual New York origins, the rule raises the lowest seven-day mean modeled service from 0.582 to 0.713 and achieves positive paired mean effects against five comparators sharing the same annual cap, using 44.29 of the 55 available commitment days per year. With extra zonal capacity set to the reference-year 95th percentile of positive corrected residuals, the same action path reduces annual uncovered energy by 25.9% and the worst-seven-day deficit by 14.8%, while booking 1.12 million MW$\cdot$h of additional capacity-time per year. Response and price sensitivity quantify the capacity-price range in which the energy reduction justifies its reservation. The framework links calibrated forecasting uncertainty to reproducible budgeted decisions and measures their local service, physical protection, and capacity cost.