Damien Faille

An Optimization Procedure of the Start-Up of Combined Cycle Power Plants

Abstract This paper presents an optimization procedure for the definition of the gas turbine load profile during the hot start-up of Combined Cycle Power Plants (CCPP). First a dynamic model of CCPP is briefly described, together with its implementation in the Modelica language. Then, an identification procedure is developed to determine a simplified model to be implemented in Matlab/Simulink and to be used for the solution of the optimization problem. This simplified model is built by interpolating a number of linear estimated models with local validity. The load profile is assumed to be described by a suitable function, whose parameters are optimized by solving a minimum time problem subject to the plant (simulator) dynamics and to a number of constraints to be imposed on the main plant variables, such as temperatures, pressures, thermal and mechanical stresses. A number of simulation experiments is reported to witness the performance of the proposed approach.

Model Based Start-up Optimization of a Combined Cycle Power Plant

Abstract This paper deals with the modeling and the optimization of the start-up of a combined cycle. It decomposes the transient in several phases that are optimized separately. For each phase a simple model is used to minimize its duration under constraints. Several state and control input constraints are given in the paper to impose a trajectory that respects the stress limitation and ensures the damping of the responses. Each constrained minimum time optimization problem is solved by a SQP algorithm available in Matlab™. The optimal trajectories are tested on a complete dynamic model which consists of a process modeled by first principle equations and a sequence described by a finite state machine. This validation model is developed in Simulink™ and Stateflow™.

Decentralized-coordinated model predictive control for a hydro-power valley

This paper aims at improving control systems for hydro-power production, by combining model predictive control techniques with decomposition-coordination methods for a global optimization over a whole hydro-power valley. It first recalls the model predictive control formulation for a centralized solution presented as the reference for comparison, and emphasizes the possible use of explicit solutions in the considered problem, making easier its practical use. It then highlights the further interest of such solutions in a decomposition-coordination approach, allowing to reduce the computational cost even more with a purpose of real-time implementation, and at the same time to take advantage of the distributed nature of the considered system. The results are illustrated on the basis of a real-data-based case-study provided by EDF group.

Hierarchical Model Predictive Control Approach for Start-up Optimization of a Combined Cycle Power Plant

Due to their numerous advantages, Combined Cycle Power Plants (CCPPs) have become an important technology for power generation. The participation of the CCPPs on the power production market involves frequent start-up/shutdown operations. In this context, optimizing their start-up procedure is of high interest. The optimization objective corresponds to problems that must be solved in real time while fulfilling operating constraints and in the mean time minimizing costs. In this paper a hierarchical model predictive control (H-MPC) structure is proposed to improve the start-up performances and to reduce the computation time. The structure includes two layers, each having a different time scale. The control problem at each level is formulated and based on a model developed in the modeling language Modelica and it aims at determination of an optimal profile of the gas turbine (GT) load. The CCPP model is adapted for optimization purposes and the load profile is assumed to be describable by parameterized functions, whose parameters are computed by solving a constrained optimal control problem. Numerical results prove the potential advantages of the proposed approach.

Explicit coordination for MPC-based distributed control with application to Hydro-Power Valleys

This paper discusses a decomposition-coordination control approach for large-scale systems, based on distributed MPC controllers and a specific coordination, with an application to the control of a so-called Hydro-Power Valley. The coordination strategy here explored can be characterized as an explicit interaction-prediction method, in the sense that the coordinator distributes predicted interactions to each subsystem on the basis of the information collected from those subsystems on the one hand, and takes advantage of explicit solutions for linear MPC control to globally update those predictions on the other hand. It is emphasized in the paper how this makes the approach suitable for real-time implementation, constraint handling, and communication limitations. In particular promising simulation results are provided for an industrial based Hydro-Power Valley case-study, chosen for the purpose of illustration, but using real data from French main electricity provider EDF.

Hybrid dynamic optimization of power plants using sum-up rounding and adaptive mesh refinement

This paper deals with dynamic optimization of hybrid differential algebraic systems (Hybrid DAE) with explicit transitions, by means of direct collocation. Modes are described by binary variables that are treated as continuous variables during the dynamic optimization, then a specific rounding procedure coupled with an adaptive mesh refinement is applied in an iterative way. The method is applied to a benchmark hybrid model of a Combined Heat and Power plant with heat storage.

Hierarchical nonlinear model predictive control for combined cycle start-up optimization

A hierarchical model predictive control (H-MPC) structure is proposed to improve the start-up performances of Combined Cycle Power Plants (CCPPs) start-up. The structure includes two layers. At each layer, the control problem aims at deriving the profile of the gas turbine (GT) load at different time scales. To achieve this, the profile is assumed to be described by parameterized functions, whose parameters are computed by solving an optimal time control problem, based on a model developed in the modeling language Modelica and subject to a number of constraints on the plant variables. Numerical results prove the potential advantages of the proposed approach.

MODELLING AND OPTIMIZATION OF A MICRO COMBINED HEAT AND POWER PLANT

Abstract Most electrical power in developed countries today is produced by large centralized power plants. With the technology progress, micro combined heat and power generators (ranging from 1 to 10 kW) are becoming available. Tomorrow, they will produce electricity and heat at home, locally. This paper presents a method of handling these new kind of power plants. The solution based on dynamic programming schedules the use of the micro CHP and of the hot water tank in order to minimize the operating costs. An on-line implementation of the algorithm is proposed and tested on a validation model.

Combined feedback linearization and MPC for wind turbine power tracking

The problem of controlling a variable-speed-variable-pitch wind turbine in non conventional operating points is addressed. We aim to provide a control architecture for a general active power tracking problem for the entire operating envelope. The presented control enables to cope with system non linearities while handling state and input constraints, and avoiding singular points. Simulations are carried out based on a 600 kW turbine parameters. Montecarlo simulation shows that the proposed controller presents a certain degree of robustness with respect to the system major uncertainties.

Hierarchical Model Predictive Control applied to Hydro Power Valley

Abstract Hydro Power Valleys are large scale systems made of several power plants equipped with pumps and turbines located along the river or at the lower part of the penstocks. These plants are linked together by the electrical grid, and also by the water network. The latter is characterized by a significant time delay. The plant operations have also to comply with technical and environmental requirements concerning the level and flow rate variations. A HD-MPC solution is proposed to manage the large scale constrained and time delayed Hydro Power Valleys. The control consists of two layers. The upper layer optimizes the power profiles on a one-day horizon with a coarse step size. The lower level refines the control for shorter horizon and finer step size. Platform tests in simulation show that the coordination is able to improve the maneuverability of the Hydro Power Valley.