CCPP Startup Presentation

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IFAC World Congress - Milano 2011 Workshop on Hierarchical and Distributed MPC: Algorithms and Applications Start-Up of Combined Cycle Power Plants A. Tica, H Gu´ eguen, D. Dumur - SUPELEC D. Faille, F. Davelaar - EDF HD-MPC SUPELEC

Transcript of CCPP Startup Presentation

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IFAC World Congress - Milano 2011Workshop on Hierarchical and Distributed MPC:

Algorithms and Applications

Start-Up of Combined Cycle Power Plants

A. Tica, H Gueguen, D. Dumur - SUPELECD. Faille, F. Davelaar - EDF

HD−MPC

SUPELEC

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Introduction 2/23

General context

Diffusion of Combined Cycle Power Plants (CCPP)

EfficiencyLower pollutant emissions

Production to consumption fit

Partial load (ancillary services)Frequent start-up and shut-down

Flexibility Improvement

Reduction of start-up and shut-down timeAvoidance of start-up failureMinimization of life-time consumption

CCPP are complex plants with numerous systems and sub-systems

Start-Up of Combined Cycle Power Plants — H. Gueguen HD-MPC

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Introduction 3/23

Objectives

How can MPC control help to reduce start-up time whilesaving life-time consumption?

How can Distribution and Hierarchy help to design andimplement control?

How can design models (Modelica) of the plant be used foroperationnal phases?

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Combined Cycle Power Plants 4/23

Schematic view

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Combined Cycle Power Plants 5/23

Start-up procedure

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Combined Cycle Power Plants 6/23

Modelica modelPolitecnico di Milano (F Casella)

1 1 1 CCPP with 3 levels of pressure

ThermoPower Library

can be used from low load to high load

Simplified model:

gas turbinelow pressure components

Stress model of critical components

high and intermediate pressure superheated steam headershigh and intermediate pressure steam turbine rotor

Start-Up of Combined Cycle Power Plants — H. Gueguen HD-MPC

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Case Study 7/23

1-1-1 CCPP with 1 level of pressure

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Case Study 8/23

Increasing Load Phase

Initial state

Turbine generators are connected to the grid

Gas turbine load is around 15%

Pressure is around 60 e5 Pa

Steam is admitted in steam turbine (Bypass valve is closed)

Aim: full load

GT load near 100%

admission valve: fully open

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Case Study 9/23

Local control

Gas turbine control

Feed water flow by drum level control loop

Steam turbine admission valve by control wrt gas turbine load

when GT load ≤ 50%: open-loop control of pressurewhen GT load > 50%: fully open

Control

Minimize start-up time

Constraints on stress level

Control variables: GTload, admission valve

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Case Study 10/23

Modelica Model

ThermoPower library

Stress model (ASME)

Header stress: combination of mechanical and thermal stressRotor stress: thermal stress

Complexity

≈2400 equations42 state variablessimulation time for increasing load phase: 12.4s (PC with 2GHz CPU)

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Overview 11/23

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Overview 11/23

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Overview 11/23

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Smooth Model 12/23

Aim:elimination of discontinuities

if clausespiecewise affine functionssteam/water tables

reduction of simulation time

Constraint: keep the structure of the ThermoPower model

corresponding componentsnew media functions for steam and water

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Smooth Model 13/23

simplification/specialization of some components (e.g. reverseflow elimination)

discontinuities approximations

Discontinuity approximation

∀x ∈ R H(x) =

{0 x < 01 x ≥ 0

∀x ∈ R Hk(x) =1

1 + e−kx

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Smooth Model 13/23

simplification/specialization of some components (e.g. reverseflow elimination)

discontinuities approximations

Discontinuity approximation

∀x ∈ R H(x) =

{0 x < 01 x ≥ 0

∀x ∈ R Hk(x) =1

1 + e−kx

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Smooth Model 14/23

Steam/Water functions approximation

piecewise polynomial approximations of the Modelica.Mediafunctionse.g. T = f (P, h)

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Smooth Model 15/23

Complexity

≈2000 equations

42 state variables

simulation time forincreasing load phase: 1.4s(PC with 2 GHz CPU)

temperature

header stress

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Overview 16/23

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Overview 16/23

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Overview 16/23

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BB optimization 17/23

Profile optimization.

Choice of parametrized profiles: L(t) = Lp(t, q), e.g.

L2H(t, q) = Lm + (Li − Lm)th

th + kh+ (LM − Li )

tp

tp + rp

Optimization problem

mintf ,q(J), J =

∫ t0

tf

dt

subject to the constraints

x = f (x , Lp(t, q))

Lp(tf , q) ≥ LM − ε1

‖f (x(tf ), Lp(tf , q))‖ ≤ ε2

h (x(t)) ≤ 0

(1)

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BB optimization 18/23

Example: L2H(t, q) gains wrt ramp: time 20%; consumption 20%

gains wrt ramp (classical): time 20%; consumption 20%Start-Up of Combined Cycle Power Plants — H. Gueguen HD-MPC

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BB optimization 19/23

Gas turbine load

2 hills functions; start-up time: 4790s (-20%)spline functions (3); start-up time: 4530s (-25%)

Gas turbine load and steam turbine admission valve

spline functions (2); start-up time: 4440s (-26%)

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MPC 20/23

Control variable: gas turbine load

Every computation time (TC )

profile computation for the next N.TC

Lagrange polynomials (N)

minimization of J =∫ t0+N.Tc

t0‖LLN

(t, q)− L0 (L(t0))‖2 dt

Start-up time: 3400s (-43%) [TC = 60s,N = 5]

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MPC 20/23

Control variable: gas turbine load

Every computation time (TC )

profile computation for the next N.TC

Lagrange polynomials (N)

minimization of J =∫ t0+N.Tc

t0‖LLN

(t, q)− L0 (L(t0))‖2 dt

Start-up time: 3400s (-43%) [TC = 60s,N = 5]

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Hierarchy and Distribution 21/23

Hierarchical MPC control

Robustness of control

Introduction of variations into the model?Simulation on sets?

Distributed control

Gradient based methods?

Robustness?

Range of admissible input signals

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Hierarchy and Distribution 21/23

Hierarchical MPC control

Robustness of control

Introduction of variations into the model?Simulation on sets?

Distributed control

Gradient based methods?

Robustness?

Range of admissible input signals

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Hierarchy and Distribution 22/23

Example

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Hierarchy and Distribution 22/23

Example

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Conclusion 23/23

Smooth Modelica model for a 1-1-1 1 pressure level CCPP

new components / media consistent with ThermoPowersystematic design of the optimisation model

Start-up profile optimization

reduction of start-up timeimportance of profile functions

Such approach for such plants is still challenging

optimization tools / model developmentsimulation tools: admissible state and feasible trajectoriesdistributed approaches: steam interactions

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Conclusion 23/23

Smooth Modelica model for a 1-1-1 1 pressure level CCPP

new components / media consistent with ThermoPowersystematic design of the optimisation model

Start-up profile optimization

reduction of start-up timeimportance of profile functions

Such approach for such plants is still challenging

optimization tools / model developmentsimulation tools: admissible state and feasible trajectoriesdistributed approaches: steam interactions

Start-Up of Combined Cycle Power Plants — H. Gueguen HD-MPC