How Are Optimization Strategies Shaping the Evolution of Renewable Fuel Resource-Based Thermodynamic Systems? A Structured Critical Review

Renewable-fuel systems couple variable energy resources with electrolysis, gasification, fuel synthesis, storage, fuel cells, and heat recovery. Their optimization involves nonlinear and nonconvex models, physical constraints, and uncertain operating conditions. This structured critical review combines thermodynamic analysis with descriptive quantitative evidence synthesis. A Scopus search in March 2026, supplemented by citation chasing, yielded a main analytical corpus of 113 records: 45 core renewable-fuel records and 68 supporting methodological records. Among 73 primary studies, 49 (67.1%) were coded as deterministic and two (2.7%) as stochastic; none was explicitly coded as robust or distributionally robust. Model-based simulation accounted for 64 studies, and no pilot- or field-deployment study was identified within this fixed corpus. These frequencies describe the selected records rather than field-wide prevalence. Additional literature provides hydrogen-related hardware-in-the-loop evidence and clarifies the scope of the validation gap. This review develops a seven-axis taxonomy with independent physical-constraint handling, a dated software capability audit, and a method-selection framework based on mathematical structure and thermodynamic fidelity. The resulting roadmap links surrogate error control, uncertainty propagation, multiscale modeling, and real-time computation to staged hardware validation. Progress requires physically feasible decisions, transparent benchmarking, and evidence that computational improvements survive implementation.