Modeling and Control of Co-generation Power Plants: A Hybrid System Approach
Author(s) -
Giancarlo FerrariTrecate,
Eduardo Gallestey,
Paolo Letizia,
Matteo Spedicato,
Manfred Morari,
Marc Antoine
Publication year - 2002
Publication title -
lecture notes in computer science
Language(s) - English
Resource type - Book series
SCImago Journal Rank - 0.249
H-Index - 400
eISSN - 1611-3349
pISSN - 0302-9743
ISBN - 3-540-43321-X
DOI - 10.1007/3-540-45873-5_18
Subject(s) - cogeneration , model predictive control , hybrid system , control theory (sociology) , control engineering , mathematical optimization , power station , combined cycle , system dynamics , computer science , electricity generation , engineering , power (physics) , control (management) , mathematics , turbine , machine learning , physics , artificial intelligence , electrical engineering , mechanical engineering , quantum mechanics
In this paper the 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. The possibility of turning on/off the gas and steam turbine, the operat- ing 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 dy- namics 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 de- mands. 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.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom