Reinforcement learning-driven stochastic optimization and blockchain-enabled demand response to determine the techno-economic feasibility of hydrogen fueling stations in Australia

This study aims to assess the feasibility of hydrogen refueling in the Central Business District (CBD) corridors of Australia, utilizing a reinforcement-learning-based stochastic framework integrated with blockchain-enabled demand response, with the objective of ensuring reliable dispatch of hybrid PV-wind-battery-electrolyze systems. The aim is to assess the techno-economic viability amidst tariff and weather uncertainties across six locations—North Terrace (Adelaide), Elizabeth Quay (Perth), Salamanca Place (Hobart), Southbank (Melbourne), Eagle Street (Brisbane), and Martin Place (Sydney)—utilizing district-scale solar and wind projections, performance-based costing, and multi-vector integration. This contribution integrates reinforcement learning (RL) control, digitally auditable demand response (DR) settlement, and spatially resolved siting into a cohesive decision framework, validated through physics-constrained simulations and bio-inspired optimizers, facilitating geography-aware configuration options and risk-sensitive investment evaluation. The scope encompasses PV–WT–Battery, PV–Battery, and WT–Battery configurations; electrolyze and tank dimensioning; demand scenarios; and sensitivity-robustness assessments. Highlights indicate that PV–WT–Battery consistently reduces lifespan metrics while achieving RF = 100 %. Elizabeth Quay represents the cost frontier (NPC ≈ $28.04 ×10 ³; LCOE ≈ $0.01747/kWh; LCOH ≈ $0.603/kg), with Eagle Street in proximity, North Terrace and Southbank at moderate elevations, and Martin Place at the upper limit; wind-dominant Salamanca Place displays a configuration inflection where 750 kW PV surpasses 1000 kW due to wind-solar covariance and part-load penalties. Reinforcement learning combined with dynamic pricing reduces LCOH variability by synchronizing electrolyze setpoints, compressor operations, and battery-tank buffers during low-marginal-cost periods, while blockchain settlement provides verifiable green-hydrogen credentials. Demand elasticity assessments reveal that −15 % scenarios yield around 10–12 % decreases in NPC/LCOE/LCOH and surpluses of about 25–30 %, whereas + 15 % scenarios result in cost increases of approximately 5–10 %. The grouping of hydrogen valleys near ports and transit hubs improves utilization and financial viability; alternative logistics (LH₂, LOHC) and subterranean storage expand site possibilities for densely populated urban areas. The strategic considerations support PV-biased trihybrids in the sun-drenched western and subtropical regions, diversified portfolios for Adelaide, Melbourne, and Sydney, and optimized wind-driven operations in Hobart, providing a replicable framework for achieving verifiable, net-zero-aligned HRS in Australian cities. Results are applicable across various climates, precinct types, and market conditions.