Physical Modeling and Optimal Sizing of Photovoltaic Generation Systems for Estimating Firm Energy Under Colombia’s Reliability Charge (ENFICC)

Photovoltaic (PV) plants participating in reliability mechanisms require design methods that assess both energy production and performance under critical resource conditions. This paper presents a computational methodology for physical modeling and optimal sizing of grid-connected PV systems to estimate Firm Energy under Colombia’s Reliability Charge (ENFICC). The workflow integrates a ten-year NASA POWER series, component catalogs with explicit preprocessing assumptions, a sequential conversion model, and a mixed discrete–continuous genetic algorithm (GA). A computational approximation of the documented La Tebaida system uses an explicitly assumed linear inverter representation. The corrected application report, independently checked against its exported hourly series using monthly coverage screening, gives a critical-month equivalent daily energy of 531.72 kWh/day and a final estimate of 425.38 kWh/day after applying the secondary-data factor fsec=0.8. This case-study assessment is distinct from validation against measured generation. The GA enforces implemented land-use, electrical, capacity, DC/AC, and monthly coverage conditions and ranks feasible candidates by final ENFICC. Thirty independent physical-data runs produced estimates from 341.5 to 390.6 kWh/day, with a mean of 362.9 kWh/day and a coefficient of variation of 3.72%. A paired 30-run ANN–MLP sensitivity experiment produced a mean of 320.6 kWh/day, 11.6% below the physical-data batch. The reported best GA solutions passed the implemented checks and computational recalculation. The contribution is a reproducible sizing workflow whose results remain conditional on the meteorological record, equipment representations, and screening assumptions.