An optimal hierarchical control scheme for smart generation units: An application to combined steam and electricity generation. (October 2020)

• Record Type: Journal Article
• Authors: Spinelli, Stefano; Farina, Marcello; Ballarino, Andrea
• Is Part Of: Journal of process control. Volume 94(2020)
• Page Start: 58
• Page End: 74
• Publication Date: 2020-10
• Subjects: Hierarchical control -- Model predictive control -- Optimal management -- Thermal and energy grids; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2020.08.006
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: Optimal management of thermal and energy grids with fluctuating demand and prices requires to orchestrate the generation units (GU) among all their operating modes. A hierarchical approach is proposed to control coupled energy nonlinear systems. The high level hybrid optimization defines the unit commitment, with the optimal transition strategy, and best production profiles. The low level dynamic model predictive control (MPC), receiving the set-points from the upper layer, safely governs the systems considering process constraints. To enhance the overall efficiency of the system, a method to optimal start-up the GU is here presented: a linear parameter-varying MPC computes the optimal trajectory in closed-loop by iteratively linearizing the system along the previous optimal solution. The introduction of an intermediate equilibrium state as additional decision variable permits the reduction of the optimization horizon, while a terminal cost term steers the system to the target set-point. Simulation results show the effectiveness of the proposed approach. Highlights: Hierarchical scheme for optimal management and control of the generation units of a smart thermal energy grid. High-level hybrid optimization for the unit commitment. Low-level dynamic Model Predictive Control (MPC). Nonlinear MPC for the start-up by linear parameter varying MPC.