A Machine-Learning Surrogate Framework for Designing and Optimizing the Char Nizam Off-Grid Hybrid Power System in Bangladesh

1)A cost-minimal, reliable system design for Char Nizam: a feasible configuration (35.8 kW PV, two 10 kW turbines, battery, and diesel backup) that serves the load with < 0.1% unmet energy at an LCOE of $0.228/kWh, a 27.8% lower cost than a comparable grid search finds, demonstrably meeting objectives (i) and (ii).
2)A green, self-sufficient operating point: the design draws 50.6% of its energy from carbon-free renewables under an explicit renewable-share constraint, displacing diesel and its emissions while remaining fully islanded, meeting objectives (iii) and (iv).
3)The enabling method: a surrogate-and-search frame- work that replaces the exhaustive dispatch loop with a learned cost model. An open-source hourly simula- tor provides ground truth; a gradient-boosted surrogate predicts LCOE, renewable fraction, and feasibility in 0.13 ms (R2 ≥0.998, 36× faster than a full simulation); and differential evolution searches it, giving a measured O(g4) scaling gap and a measured 322× speedup over a g = 20 grid.
4)A justified model choice, and an audit of the framework’s own claims: we benchmark five regression families and select gradient boosting on the joint accuracy– latency criterion; separate the discretization advantage from the surrogate’s own contribution by running the same continuous search directly on the simulator, then close most of the remaining gap by adaptive refinement (7.1% → 3.4% for 100 extra simulations); cross-check the simulator against a published HOMER Pro study of this island; verify optimizer stability over 30 seeds; and decompose the LCOE band, which shows fuel price carries essentially all the risk.