Phase-Adaptive Constrained Active Sampling for Simulation-Verified Planning of Renewable Energy Bases

Planning renewable energy bases with chronological source-grid-storage simulation makes exhaustive capacity screening impractical. This study develops a phase-adaptive constrained active-sampling framework whose core mechanism is a simulator-verified phase switch: a probability-of-feasibility-weighted lower confidence bound (PoF-LCB) directs the search until the first verified feasible plan is found, after which constrained expected improvement (CEI) directs economic refinement, supplemented by bounded optimal-neighborhood and constraint-boundary ranking refinements. Gaussian-process surrogates decide only the evaluation order; the reported objective and all four engineering constraints—photovoltaic curtailment, loss-of-load energy, capacity credit, and flexibility scarcity—are verified exclusively by the original 8760 h simulator. In a paired 2 × 2 factorial experiment over 30 common initial designs on a 125-candidate pool, the phase switch raised exact-optimum recovery from 24/30 to 30/30 (exact McNemar p = 0.03125), and the full rule maintained 30/30 under two unseen profile seeds where CEI achieved 24/30 and 22/30. The full rule further recovered the exact optimum of a 1224-candidate pool in 30/30 runs within 20 evaluations and of a six-variable 729-point grid within 75 evaluations, using roughly 2–10% of the exhaustive simulation budget. Constraint-slack and guard-band reporting, candidate-domain audits, and repeated wall-clock measurements turn the recommendation into auditable planning decisions, with all evidence drawn from a reproducible synthetic benchmark.