A multistage stochastic programming approach for renewable ammonia supply chain network design

This paper considers the effect of ammonia market price uncertainty across multiple years on the deployment of renewable ammonia production facilities in existing ammonia supply chain networks. We use an ammonia supply chain transition optimization model to investigate the effect of this uncertainty. Specifically, we formulate a multistage stochastic programming problem to determine the optimal investment policy for new renewable ammonia production over a multi-year transition horizon such that ammonia demand is satisfied and the total supply chain cost is minimized. The proposed approach is used to analyze the transition of the ammonia supply chain for the state of Minnesota. The results show that the trajectory of the price over time determines the degree to which renewable ammonia production facilities are adopted. In a broad sense, considering the possibility of higher-than-average conventional ammonia market prices through a multistage stochastic problem leads to a wider adoption of renewable production relative to a deterministic problem, which only considers the average market price in an economically optimal supply chain transition. Comparison with a two-stage stochastic programming approach from prior work shows that accounting for price uncertainty across time leads to 4.4% reduction in the cost. For a full transition to renewable production, the multistage stochastic framework results, on average, in a slightly slower transition than the deterministic problem due to scenarios which include lower-than-average market prices.