An optimization procedure of the start-up of Combined Cycle Power Plants

Casella, Francesco; Farina, Marcello; Righetti, Fabio; Faille, Damien; Ticǎ, Adrian; Guéguen, Hervé; Scattolini, Riccardo; Davelaar, Frans; Dumur, Didier

doi:10.3182/20110828-6-it-1002.00604

Cited by 26 publications

(16 citation statements)

References 15 publications

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“…In (Casella et al, 2011a) a simplified model is used. The contribution (Casella et al, 2011b) reports on a solution using JModelica.org, CppAD for automatic differentiation, and Ipopt for the NLP solution. The integration of CasADi and JModelica.org is described in (Andersson et al, 2011), where the CCPP system is also used as a benchmark system, but the solution is achieved by a direct collocation approach.…”

Section: Combined Cycle Power Plant Start-up Control Problemmentioning

confidence: 99%

A Toolchain for Solving Dynamic Optimization Problems Using Symbolic and Parallel Computing

Lazutkin

Hopfgarten

Geletu

et al. 2015

Linköping Electronic Conference Proceedings

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Significant progresses in developing approaches to dynamic optimization have been made. However, its practical implementation poses a difficult task and its realtime application such as in nonlinear model predictive control (NMPC) remains challenging. A toolchain is developed in this work to relieve the implementation burden and, meanwhile, to speed up the computations for solving the dynamic optimization problem. To achieve these targets, symbolic computing is utilized for calculating the first and second order sensitivities on the one hand and parallel computing is used for separately accomplishing the computations for the individual time intervals on the other hand. Two optimal control problems are solved to demonstrate the efficiency of the developed toolchain which solves one of the problems with approximately 25,000 variables within a reasonable CPU time.

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Section: Combined Cycle Power Plant Start-up Control Problemmentioning

confidence: 99%

A Toolchain for Solving Dynamic Optimization Problems Using Symbolic and Parallel Computing

Lazutkin

Hopfgarten

Geletu

et al. 2015

Linköping Electronic Conference Proceedings

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“…The parameter set with the best objective value with seven parameters, p 6,13,16,17,22,24,47 , is called C 3 and is shown in Figure 3. The parameters in the set consist of four k out parameters for SH1, SH3 and all RHs, one valve parameter and two sensor parameters.…”

Section: Calibrationmentioning

confidence: 99%

“…The model has previously been used in optimizations [2,3]. The startup of combined cycle power plants has been optimized in several studies before [4,5,6,7,8].…”

Section: Introductionmentioning

confidence: 99%

Parameter Selection in a Combined Cycle Power Plant

Andersson

Åkesson

Link

et al. 2014

Linköping Electronic Conference Proceedings

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A combined cycle power plant are modeled and considered for calibration. The dynamic model, aimed for start-up optimization, contains 64 candidate parameters for calibration. The number of parameter sets that can be created are huge and an algorithm called subset selection algorithm is used to reduce the number of parameter sets. The algorithm investigates the numerical properties of a calibration from a parameter Jacobean estimated from a simulation of the model with reasonably chosen parameter values. The calibrations were performed with a Levenberg-Marquardt algorithm considering the least squares of eight output signals. The parameter value with the best objective function value resulted in simulations in good compliance to the process dynamics. The subset selection algorithm effectively shows which parameters that are important and which parameters that can be left out.

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“…With a free energy market even an intermittent use of the plant should become remunerative, considering fluctuations of the energy price. This is one of the most cited reasons to optimize, as much as possible, the control and behaviour of power plants during the start-up phase [6].…”

Section: Introductionmentioning

confidence: 99%

Preliminary design and validation of a Real Time model for hardware in the loop testing of bypass valve actuation system

Pugi

Galardi

Carcasci

et al. 2015

Energy Conversion and Management

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Hardware in the loop Turbine bypass controller a b s t r a c tDuring the start-up and shut-down phases of steam power plants many components are subjected to pressure and temperature transients that have to be carefully regulated both for safety and reliability reasons. For this reason, there is a growing interest in the optimization of turbine bypass controllers and actuators which are mainly used to regulate the plant during this kind of operations. In this work, a numerically efficient model for Real Time (RT) simulation of a steam plant is presented. In particular, a modular Simulink™ library of components such as valves, turbines and heaters has been developed. In this way it is possible to easily assemble and customize models able to simulate different plants and operating scenarios. The code, which is implemented for a fixed, discrete step solver, can be easily compiled for a RT target (such as a Texas Instrument DSP) in order to be executed in Real Time on a low cost industrial hardware. The proposed model has been used for quite innovative applications such as the development of a Hardware In the Loop (HIL) test rig of turbine bypass controllers and valve positioners. Preliminary experimental activities and results of the proposed test rig developed for Velan ABV are introduced and discussed.

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An optimization procedure of the start-up of Combined Cycle Power Plants

Cited by 26 publications

References 15 publications

A Toolchain for Solving Dynamic Optimization Problems Using Symbolic and Parallel Computing

A Toolchain for Solving Dynamic Optimization Problems Using Symbolic and Parallel Computing

Parameter Selection in a Combined Cycle Power Plant

Preliminary design and validation of a Real Time model for hardware in the loop testing of bypass valve actuation system

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