Alec Stothert

Model Predictive Control and the Optimization of Power Plant Load while Considering Lifetime Consumption

This paper describes a decision support system that indicates to a power plant operator the effect of daily operation on plant lifetime consumption and recommends short-term operating strategies that optimize plant economic performance. The recommended operating strategy is based on the optimization of an objective function that includes terms for revenues from energy sales, production costs, and plant ageing. Plant ageing is based on models that are directly load dependent and incorporate a memory aspect-a feature that is missing from common lifetime modeling techniques. The optimization results in a trade-off between maximization of immediate profits (i.e., earnings achieved by selling heat and power) and minimization of lifetime consumption. Model predictive control and the mixed logical dynamic (MLD) approach are used to solve the posed optimization problem.

Anwendung von modellbasierter prädiktiver Regelung und Methoden der hybriden Systeme zur optimalen Produktionsplanung (Using Model Predictive Control and Hybrid Systems for Optimal Scheduling of Industrial Processes)

Abstract Optimal (re)scheduling of production in industrial processes increases economic efficiency through timely and optimal use of limited resources. In this article an approach to scheduling based on the use of hybrid systems and model predictive control is presented. Modelling flexibility, acceptable computational times, and optimal disturbance rejection are the key advantages of the approach. The main ideas are illustrated with two industrial applications.

MODELLING AND CONTROL OF CO-GENERATION POWER PLANTS UNDER CONSIDERATION OF LIFETIME CONSUMPTION: A HYBRID SYSTEM APPROACH

In this paper the load optimization of a combined cycle power plant under consideration of the real cost of lifetime usage is accomplished by exploiting hybrid systems, i.e., systems evolving according to continuous dynamics, discrete dynamics, and logic rules. The possibility of turning on/off the gas and steam turbines, the operating constraints (minimum up and down times) and the different types of start up of the turbines characterize the hybrid behavior of a combined cycle power plant. In order to model both the continuous/discrete dynamics and the switching between different operating conditions we use the framework of Mixed Logic Dynamical systems. Next, we recast the economic optimization problem as a Model Predictive Control (MPC) problem, that allows us to optimize the plant operations by taking into account the time variability of both prices and electricity/steam demands. Because of the presence of integer variables, the MPC scheme is formulated as a mixed integer linear program that can be solved in an efficient way by using commercial solvers.