(537d) Accounting for Uncertainty Via Scheduling Informed Optimal Design: A Renewable Ammonia Case Study
In this work, we consider the case study of ï¬nding the optimal design of a wind-powered ammonia generation system operating in the aforementioned market structure. To keep the problem tractable, we propose a method where the optimization problems for scheduling and design are decoupled. To do so, we develop a receding horizon formulation for scheduling a system with a wind turbine which services a renewable ammonia plant as well as other uncertain but uncontrollable loads. These problems make hourly decisions about the operating state of the system to minimize operating cost based on uncertain forecasts of weather and power load over the next 48 hours. We then simulate many yearlong scheduling problems for wind-powered ammonia plants of various designs to obtain annual operating costs. From this data, we use a method similar to that described in  to identify a small number of key design parameters on which operating cost depends and develop simple correlations relating operating cost to these parameters. We repeat this analysis for different values of the electricity market parameters and obtain different sets of correlations for tight, loose, and no market parameters.
The correlations found by scheduling the renewable ammonia plant are then embedded in a design optimization problem attempting to ï¬nd unit sizes which can meet a given power and ammonia demand at minimal net present cost. Because scheduling is embedded in the correlations for operating cost, the design optimization problem no longer needs to include variables for operating states, making the resulting optimization problem much smaller and allowing more accurate nonlinear capital cost functions to be included. Our results show how the optimal design of the ammonia system can change based on the market parameters, as a tight market structure where one cannot deviate much from their power exchange commitment requires a design that can be operated more ï¬exibly than a loose market structure. We also compare our results with those obtained from attempting to design without taking into account uncertainty, and show that the deterministic optimal design is not as economical in the electricity market structure.
 Zachar, M. and Daoutidis, P. Microgrid/macrogrid energy exchange: A novel market structure and stochastic scheduling. IEEE Trans. Smart Grid (8), 2017, pp. 178-189.
 Allman, A. and Daoutidis, P. Optimal scheduling for wind-powered ammonia generation: Eï¬ects of key design parameters. Chem. Eng. Res. Des., 2017, in press.