Grid-integrated green ammonia production system design optimisation under renewable energy supply uncertainty

Green hydrogen (GH) and green ammonia (GA) are promising solutions for decarbonising hard-to-abate sectors and enabling large-scale renewable energy storage and transportation. As interest in GH increases, optimising production facilities facing intermittent renewable energy supply is essential for economic viability. Existing studies often oversimplify uncertainty in scenario probabilities for renewable generation and ignore interactions with electricity markets. To address this gap, this study develops a stochastic optimisation framework for designing grid-connected GH–GA systems and extends it to a distributionally robust optimisation formulation to hedge against ambiguity in those scenario probabilities. Market participation is captured through a pay-as-cleared electricity market mechanism. A hyperparameter calibration for scenario count and divergence budget validated through out-of-sample testing shows improved reliability compared to conventional stochastic methods. A case study on Canada's Eastern coast identifies a hybrid wind-solar system with grid backup a cost-effective configuration, achieving hydrogen and ammonia costs of $4.17/kg and $0.72/kg, respectively. Sensitivity analyses show that rising electricity prices favour increased on-site wind capacity and reduced grid reliance. Results highlight that optimal system design and market strategies are region-specific, project economics remain stable across wide ranges of market pricing and grid accessibility, and accounting for uncertainty improves economic performance and production stability.