An optimal hierarchical control scheme for smart generation units: An application to combined steam and electricity generation

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 linearising 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.

Optimal Management and Control of Smart Thermal-Energy Grids

“…The lower level is a dynamic MPC that operates on each individual system to guarantee that the process constraints are fulfilled, with a faster sampling time. For the dynamic control of the sub-system, specific controllers can be designed for the different operating modes: a linear MPC is implemented for normal production modes (see [5]; or [6] where multi-rate approach is adopted), while to address the nonlinearity of the system in the start-up phase, a linear parameter-varying MPC approach [5] is proposed for the optimization of a nonlinear system subjected to hard constraints. This method, exploiting the linearization of the system along the predicted trajec- tory, is able to address nonlinear system with the advantage of the computational time with respect to NMPC strategy, as an approximation of the SQP optimization stopped at the first iteration.…”

“…The scheme has been validated on an industrial use-case. An extensive presentation is reported in [5].…”