Dynamic Optimization of Startup and Load-Increasing Processes in Power Plants—Part II: Application

Bausa, Jens; Tsatsaronis, George

doi:10.1115/1.1286729

Cited by 17 publications

(2 citation statements)

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“…For this reason, both in academia and in industry, many efforts have been devoted to determine procedures reducing the start-up time while keeping the life-time consumption of the most critically stressed components under control, see e.g. (Franke et al, 2003, Krüger et al, 2001, 2004, Bausa and Tsatsaronis, 2001a, 2001b, Smith et al 1996, Xu et al 2000, Ferrari-Trecate et al, 2002, Casella and Pretolani, 2006, Albanesi et al 2006, Faille and Davelaar, 2009. In this paper, a novel model-based approach to the optimization of the Gas Turbine (GT) load profile to be used in the start-up phase is presented.…”

Section: Introductionmentioning

confidence: 99%

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

Casella¹,

Farina²,

Righetti³

et al. 2011

IFAC Proceedings Volumes

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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.

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Section: Introductionmentioning

confidence: 99%

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

Casella¹,

Farina²,

Righetti³

et al. 2011

IFAC Proceedings Volumes

View full text Add to dashboard Cite

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“…A genetic algorithm with enforcement operation to search along the edges of the operational constraints has been proposed (3) . Furthermore, a dynamic optimization technique using a simultaneous nonlinear programming formulation has been applied (4), (5) . However, all these studies have aimed to optimize a single-objective function (e.g., only the start-up time is minimized under the operational constraints).…”

Section: Introductionmentioning

confidence: 99%

Intelligent Start-Up Schedule Optimization System for a Thermal Power Plant

Shirakawa

Arakawa

Nakayama³

2007

JAMDSM

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This paper proposes an intelligent start-up schedule optimization system for a thermal power plant. This system consists of a dynamic simulation, a neural network, and an interactive multi-objective programming technique. In this study, a new intelligent optimization method using the neural network and a genetic algorithm to realize a satisficing trade-off method, which is an interactive multi-objective programming technique, has been developed and introduced into this system. The features of this system are as follows. (1) The start-up schedule can be optimized based on multi-objective evaluation and (2) an optimal and flexible start-up schedule can be determined with a reasonable computing time and calculation accuracy through human-computer interactions. The system is applied to a simulation for a combined cycle power plant, and to optimize from among multiple objectives, based on varying daily requirements. The application results show that optimal and flexible start-up schedules can be obtained within a reasonable computing time and with acceptable calculation accuracy.

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