Embedding dynamics and control considerations in operational optimization of process and energy systems
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Embedding dynamics and control considerations within operational optimization decisions can result in improved performance of processes and energy systems. These efforts are motivated by modern sustainability initiatives, in particular demand response and demand management strategies for improving the efficiency of the electric grid. In these scenarios residential, commercial, and industrial electricity consumers are provided with a financial incentive to shift their demand such that the total load on the grid can be satisfied using efficient generation technologies and renewable energy sources. The financial incentive is typically a time-dependent price structure, where rates reflect the demand level and stress on the grid. Reacting to such fast-changing energy markets requires that process and energy systems be highly flexible, which is a significant departure from traditional steady state operation under fixed market conditions. In this context, flexibility means the ability to make frequent changes to the system operation (e.g., production setpoints, constraint levels, etc.) while still maintaining stability and satisfying operating constraints at all times. This necessitates the development of advanced control and decision making strategies which are aware of system dynamics. Accounting for dynamics by incorporating detailed, first-principles models of a system into optimization-based controllers or scheduling calculations would provide ample dynamic information. However, the resulting dynamic optimization formulations would be plagued by a large problem size, numerical difficulties associated with stiff equations and multiple time scales, and the presence of integer decisions. In this dissertation, we address these challenges through hierarchical controller designs and novel scheduling (and rescheduling) formulations including low-order models of relevant system dynamics, which are identified through an appropriate model reduction or system identification procedure. Case studies involving the built environment and chemical processes are used to demonstrate the proposed methods.