461735 Optimal Scheduling of a Renewable Ammonia Plant with Wind Generation Under Variable Electricity Pricing
Recently, a first of its kind renewable ammonia plant has been developed in Morris, MN, which uses wind energy to electrolyze water, separate air, and produce ammonia in a Haber-Bosch reactor. We have shown previously that such a plant can be cost-competitive in the ammonia supply chain when scaled up to 140,000 t/y . However, it is envisioned that renewable plants be implemented on a smaller, more distributed scale to promote local and sustainable fertilizer production for farming. To help achieve this goal, it is hypothesized that the economics of the renewable ammonia plant can be improved by implementing a scheduling program that takes advantage of variable electricity pricing both through the chemical process flexibility and through the integration of the wind turbine with the grid.
This work develops a framework for the optimal scheduling for plants which contain both chemical processes and renewable power generation under variable electricity pricing. The scheduling problem is formulated as a mixed integer program (MIP) that minimizes the operating costs of the system over some time horizon by determining set points for the chemical processes and grid interaction such that product demands are met. Such a framework will be useful for the industrial sector to play its part in being amenable to increasing renewable power penetration. As a case study, this framework will then be applied to the renewable ammonia system. In this study, we consider hourly scheduling over a 24 hour rolling horizon. The scheduling optimization problem takes advantage of the variable pricing, producing extra hydrogen and nitrogen when electricity prices are low and selling excess wind energy when prices are high. As such, we establish that the economic feasibility of smaller scale renewable ammonia plants is improved when considering variable power pricing.
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 A. Allman and P. Daoutidis, "Ammonia supply chains: a new framework for renewable generation with a case study for Minnesota," Comput. Aided Process Des., Accepted, 2016.