Marc Antoine

Modeling and control of co-generation power plants: a hybrid system approach

In this paper, the short-term scheduling optimization of a combined cycle power plant is accomplished by exploiting hybrid systems, i.e., systems evolving according to continuous dynamics, discrete dynamics, and logic rules. Discrete features of a power plant are, for instance, the possibility of turning on/off the turbines, operating constraints like minimum up and down times and the different types of start up of the turbines. On the other hand, features with continuous dynamics are power and steam output, the corresponding fuel consumption, etc. The union of these properties 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 (MLD) systems. Then, 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 via dedicated software.

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.

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.

Scheduling of gas turbine compressor washing

This paper describes an estimation method for gas turbine compressor degradation and an economical optimisation model for determining the optimal compressor washing cycles. The optimisation model aims at minimising fuel consumption and emissions in combined-cycle power plants. The results presented are of significant importance for power plants operators that have the possibility of frequently connecting and disconnecting to the power grid. By optimising power generation periods and levels, downtimes and maintenance scheduling, the operators ensure that the plant operates at a high efficiency level in periods when fuel prices are high. High efficiency levels ensure low fuel consumption and emission levels. The work presented in this paper was implemented as a product prototype at ABB Utility Automation in 2003 and patented in 2005, [1]. Copyright