Integration of Design, Scheduling, and Control of Combined Heat and Power Systems: A Multiparametric Programming Based Approach

Baris Burnak (1,2), Justin Katz (1,2), Nikolaos A. Diangelakis (2), Efstratios N. Pistikopoulos (1,2)

1. Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station TX, 77840

2. Texas A&M Energy Institute, Texas A&M University, College Station TX, 77840

Email : baris.burnak@tamu.edu

Abstract

 

The operation of multiproduct/multipurpose processes involves decisions at different time scales; such as short-term for control, medium-term for scheduling, and long(er)-term for design. At all these scales, uncertainty plays a key role with fluctuations in product demand, prices, availability of units, as well as raw materials and product specifications. While it has been widely accepted that simultaneous decision making at these three layers are expected to deliver cost-effective and intensified/integrated processes; such an integration still poses formidable challenges due to the order of magnitude differences in the time-scale, the often conflicting objectives at the different layers, etc. However, recent advances in this area provide a sound basis for further development.

In this work, we present a unified framework towards this direction for the case of multiproduct/multipurpose processes. Based on the PAROC (PARametric Optimization and Control) framework featuring (i) a single high-fidelity model, (ii) multi-parametric Rolling Horizon Optimization (mpRHO) policies to readjust for the changing market structures, (iii) multi-parametric Model Predictive Control (mpMPC) for efficient set point tracking and, (iv) a surrogate model formulation to bridge the time gap between mpRHO and mpMPC, we derive (i) design dependent and scheduling aware control strategies, and (ii) scheduling strategies that are design dependent and control aware. The multi-parametric formulation of the integrated scheduling and control schemes yields offline maps of optimal actions that enable design and operational optimization under uncertainty. The framework is illustrated on a combined heat and power (CHP) system, involving two CHPs operating in tandem to satisfy the time-variant heat and electricity demand from multiple residential units. We utilize the proposed methodology to simultaneously determine (i) the optimal size of the internal combustion engine, (ii) the decentralized control schemes for different operating policies, and (iii) the coordination of the parallel operations of the CHP units.


Keywords: Process design, Model based control, Scheduling, Parametric programming

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