The Integrated Gasification Combined Cycle (IGCC) possesses a number of benefits over traditional power generation plants, ranging from increased energy conversion efficiency to flex-fuel and carbon capture opportunities. Unfortunately, the large capital cost associated with an IGCC plant seems to outweigh its efficiency gain, as evidenced by the low interest in IGCC plant construction within the United States. A less known benefit of the IGCC configuration is the ability to load track electricity market demands. The idea being that the hydrogen product of the gasification unit can be stored during periods of low power demand. Then as demand increases along with the value of electric power, the stored hydrogen can be converted in combustion turbines. Exploitation of this load tracking feature will certainly increase profit margins and may push the IGCC process into economic viability. The objective of the current project is to size the hydrogen / energy storage unit as well as the set of combustion turbines used for hydrogen conversion.
The effort begins with the design of a high level (supervisory) control system that is capable of market responsiveness. That is, design a controller that uses the electricity spot price as a disturbance input to the decision making process. However, in contrast to traditional controller designs, where the objective is to attenuate disturbances, the Market Responsive Controller should judiciously amplify the spot price disturbance. We have found that a Linear Quadratic Gaussian controller can be forced to have such behavior if the cross-term weights of the objective function are utilized. However, it is a non-trivial task to select weights such that profit is maximized (by producing electricity when the price is high and storing when low), while observing the equipment limitations of the storage and conversion units. Thus, a core result of the paper is the development of a profit maximizing scheme for the selection of LQG weights subject to process equipment limitations. The resulting optimization problem will be shown to be convex and thus will yield a globally optimal solution.
Now turn to the question of sizing the storage and conversion equipment. Intuition suggests the following: Larger storage capacity coupled with larger conversion throughput will enhance the ability of the previous control scheme to exploit price swings. However, at some point the capital cost associated with ever larger equipment will begin to outweigh the increase in operating profit, resulting in a sign change in the levelized profit. Thus, the main result of the paper is the development of a levelized profit maximization scheme for the sizing of storage and conversion equipment. The resulting optimization problem is shown possess a linear objective function and convex constraints along with a small number of scalar reverse-convex constraints. Based on this formulation it will be shown that the optimization problem will yield a globally optimal solution with the aid of a branch and bound algorithm.