study of supercritical coal fired power plant dynamic
TRANSCRIPT
Study of Supercritical Coal Fired Power Plant Dynamic Responses and Control
for Grid Code Compliance Prof Jihong Wang, Dr Jacek D Wojcik (University of Warwick)
Dr Yali Xue (Tsinghua University)
THE UNIVERSITY of BIRMINGHAM
Mathematical Modelling and Simulation of Power Plants and CO2 Capture
WORKSHOP
University of Warwick, 20th-21st March 2012
Outline
• Overview of the EPSRC Project (J Wang)
• Power Plant Modelling (J Wojcik)
• Power Plant Simulation (Y L Xue)
• Summary (J Wang)
THE UNIVERSITY of BIRMINGHAM
Outline
• Overview of the EPSRC Project
• Power Plant Simulator
• Power plant modelling
• Summary
THE UNIVERSITY of BIRMINGHAM
Supercritical technology
Subcritical (conventional)
Supercritical Ultra supercritical
Temperature (°C)
500 – 550 500 – 600 550 – 600, (600 – 700)*
Pressure (MPa) 16 – 17 24 – 26 27 – 32, (40 – 42)*
Features Drum: single reheat
Once through: single reheat
Once through: double reheat
Efficiency cycle (%)
33 - 35 40-45 42 – 47, (50 – 55)*
THE UNIVERSITY of BIRMINGHAM
Future new power plants in the UK - SUPERCRITICAL
THE UNIVERSITY of BIRMINGHAM
Power generation responses to the demand changes Fast enough to satisfy the grid specification
Subcritical water-steam cycle Drum – energy storage
Supercritical water-steam cycle (no phase change) Once-through operation – no energy storage
Challenges: Can supercritical power generation responses to the demand changes fast enough to satisfy BG Grid Code requirement?
Subcritical Supercritical
THE UNIVERSITY of BIRMINGHAM
Project Objectives
Through study supercritical coal fired power plant mathematical modelling and simulation: • to understand the dynamic responses of supercritical power plants • to investigate the possible strategies for improvement
THE UNIVERSITY of BIRMINGHAM
List of UK power stations
- All Subcritical (~33% efficiency)
THE UNIVERSITY of BIRMINGHAM
Station Name Representation Company Address Capacity in MW
Aberthaw B National Ash RWE Npower The Leys Aberthaw, Barry South Glamorgan
CF62 42W 1,489
Cockenzie ScotAsh Scottish Power Prestopans East Lothian 1,152 Cottam EDF Energy EDF Energy Cottam Power
Company, PO Box 4, nr Retford
Nottinghamshire DN22 0ET 1,970
Didcot A National Ash RWE Npower Didcot Nr Oxford OX11 7HA 2,020 Drax Hargreaves CCP Drax Power Limited Drax Selby North Yorks YO8 8PQ 3,870
Eggborough British Energy British Energy Eggborough Goole North Humberside
DN14 0BS 1,960
Ferrybridge C Keadby generation Ltd
Scottish & Southern Energy plc
PO Box 39, Stranglands Lane
Knottingley West Yorkshire
WF11 8SQ 1,955
Fiddlers Ferry Keadby generation Ltd
Scottish & Southern Energy plc
Widnes Road Cuerdley Warrington WA5 2UT 1,961
Ironbridge EON UK PowerGen Buildwas Road Telford Shropshire TF8 7BL 970 Kingsnorth EON UK PowerGen Hoo Saint Werburgh Rochester Kent ME3 9NQ 1,974 Longannet ScotAsh Scottish Power ScotAsh Ltd, Kincardine-
on-Forth Fife FK10 4AA 2,304
Lynemouth Alcan Alcan Primary Metal - Europe
Ashington Northumberland NE63 9YH 420
Ratcliffe EON UK Powergen Ratcliife on Soar Nottingham NG11 0EE 2,000 Rugeley International Power International Power Rugeley Power Station Armitage Road Rugeley WS15 1PR 976 Tilbury B National Ash RWE Npower Fort Road Tilbury Essex RM18 8UJ 1,020 West Burton EDF Energy EDF Energy West Burton Power
Company, Retford Nottinghamshire DN22 9BL 1,932
Wilton Hargreaves CCP ICI PO Box 1985, Wilton International
Middlesborough TS90 8WS 100
Collaboration Mathematical
modelling, simulation study, dynamic
response analysis, optimal control,
Grid Code studies
Supercritical water, test rig evelopment, Experimental tudies, data collection and
analysis
Industrial scale power plant modelling and
simulation, software
development, verification
Power plant control, intelligent
algorithms,
Consortium interactions
Exchange of materials, data,
information
Integrated testing
programme
Shared models, software and simulations
Team
University of Warwick: Prof J Wang, Dr J Wojcik Mr M Draganescu, Mr S Guo
University of Birmingham: Dr B Al-Duri, Mr O Mohamed
Tsinghua University Prof. J F Lv, Prof Q R Gao, Dr Y L Xue
North China Electric Power University Prof X J Liu, Prof G L Hou
THE UNIVERSITY of BIRMINGHAM
THE UNIVERSITY of BIRMINGHAM
Frequency in the Power System - Mr M Draganescu
Definition: Power System Frequency can be defined as a measure of the electrical speed of the synchronous generators connected to the grid; this is a common value at every point in the grid.
Frequency – constant value
Electricity Generation
Electricity Demand at all time.
Electricity Generation
Electricity Demand
Frequency Deviations
Power System Instability
Total Outage (Blackout)
PGen PDem
THE UNIVERSITY of BIRMINGHAM
UK Power System
Transmission System Operators (TSOs): • National Grid • Scottish and Southern Energy • Scottish Power
System Data:
TSO Circuit Voltage [kV]
Circuit Length [km]
National Grid 400, 275 ~14,000
Scottish and Southern Energy 275, 132 ~5,000
Scottish Power 400, 275, 132 ~4,000
Electricity Supplied by Fuel Type in 2010:
THE UNIVERSITY of BIRMINGHAM
UK Power System — The Grid Code —
Frequency Control Strategies Type of Frequency Control
Strategy Response Time
Primary Frequency Response active power increase within 10 s and maintained for another 30 s
Secondary Frequency Response active power increase within 30 s and maintained for another 30 min
High Frequency Response active power decrease within 10 s and maintained thereafter
Nominal Frequency: 50 Hz
Frequency Variation Interval [Hz] Normal
Operation Critical Situations
49.5 – 50.5 47.0 – 52.0
THE UNIVERSITY of BIRMINGHAM
Test: A frequency ramp decrease/ increase of 0.5 Hz over a period of 10 s.
Frequency Response Capability of a Generating Unit UK Power System — The Grid Code —
Outline
• Overview of the EPSRC Project (J Wang)
• Power Plant Modelling (J Wojcik)
• Power Plant Simulation (Y L Xue)
• Summary (J Wang)
THE UNIVERSITY of BIRMINGHAM
Exact mathematical model of SCPP consists of: • Coal mill (Pulverised-Fuel) • Supercritical Boiler • Steam Turbine • Synchronous Generator (SG) and Electric Power System (EPS) • Excitation System – Auto Voltage Regulator (AVR) and Exciter • Governor (GOV) • Boiler Control System
Power Plant Modelling
WC ΔPpa Tin
SC BOILER
WFWF Steam Turbine
Synchronous
Generator COAL MILL
Electrical Power System WPF
PM
Excitation System
EFD
PMSP
CVArea IVArea Pe Pe
Pg Qg Ug
δ Eqb Edb Ig
Ig Ug
Governor Δω
WSC PRH
B+jG
Single-Machine Infinitive Bus
Control System
Mathematical model of Supercritical Power Plant - Block diagram.
Mathematical modelling of Supercritical Power Plant in Matlab®/Simulink® software environment
Two different types of pulverised coal mill in power plants
Power Plant Modelling Coal Mill Model Implementation in Matlab®/Simulink® software environment
Vertical Spindle mill
Tube-Ball mill
s1cM
pfM
Kf Ap1 Ap2 Wc
K1,K2, K3,K4,K5,K6,K7,K8,K14, K17 K16
ΔPout, Mpf_initial
Wpf
Mc_initial K15
Mc
Mpf
ΔPout , ΔPin , Tin , Tout
P
T
s1
Model based on mass balance and heat balance:
Block diagram of coal mill model.
Graphical Unit
Interface
‘On-line Condition and Safety Monitoring of Pulverised Coal Mills Using a Model Based Pattern Recognition Technique’
Prof Jihong Wang, Dr Jianlin Wei, Mr Paschalis Zachariades, Mr. Shen Guo
Differential equations
Hi 1 Ts
P
Q
wo Ho
Ki
wi
hi V
p v h τ
wi ho wo
q
Model based on mass balance and energy balance
Steam pressure model
KECO
CVArea
1 TECOs
Ho1
Hi1
AECO√
1 TWWs
Ho2
Hi2
AWW√
1 TSHs
Ho3
Hi3
ASH√
1 TCVs
Ho4
Hi4
X
KWW
KSH
1 1+TFFs
1 TRHs
Ho5
Hi5
X
IVArea
KRH
Feedwater flow
Fuel flow
WCV
WRH
Block diagram of boiler model. Where: Hi, Ho – input/output gain of steam flow entering/leaving associated nodes, P – node pressure, Q – heat transfer to node, Wi – flow rate of fluid entering node, Wo – flow rate of fluid leaving node
Differential equations
BECOECOoFWFiECOECO QKWHWHPT 11
BWWWWoECOiWWWW QKWHWHPT 22
BSHSHoWWiSHSH QKWHWHPT 33
CVoSHiCVCV WHWHPT 44
BRHRHoCViRHRH QKWHWHPT 55
BPFBFF QWQT
The steam patch is divided into following parts: • Economiser node • Waterwall node • Superheater node • Main steam line node • Reheater node
Power Plant Modelling SC Boiler Model Implementation in Matlab®/Simulink® software environment
Fuel Air Feedwater
HP IP+LP
to
con
den
ser
Water-Steam loop in basic once-through boiler design.
Econo- miser
Flue gases
F. de Mello: Dynamic Models for Fossil Fuelled Steam Units in Power System Studies. IEEE Transactions on Power Systems, Vol.6, No.2, 1991.
HP Steam Chest
Tandem-Compound Single-Reheat Steam Turbine
Generic Model of Steam Turbine/ Tandem-Compound Single-Reheat Steam Turbine
Pm1
1 1+sT4
1 1+sT5
1 1+sT6
1 1+sT7
K1
K2
K3
K4
K5
K6
K7
K8
GVArea
π PMS π
IVArea
Differential equations
SCAreaBSC WGVPWT )(4
RHSCRH PWPT 5
CRRHCR WWWT 6
Power Plant Modelling Steam Turbine and Governor Models Implementation in Matlab®/Simulink® software environment
DEH control system – block diagram, where: ∆f – frequency deviation; ∆n – speed deviation; K– speed drop ;
Pref – reference load signal; Pload – load signal; Pms – main steam pressure.
∆f
Pref
V D
1 1+sT1
PID K
- ∆n
Pload Pms
1
0 1
2
3
4
feed forward loop
DEH control system
feedback loop
Control mode Switch S1 S2 S3 S4
SC on off off off SCLF on off on off SCPF on off off on
SCLFF off on on off SCPFF off on off on
s
DPPT emm
)( ''''''''''0 dddqqqd XXIEEET
)( ''''''''''0 qqqdddq XXIEEET
)( ''''0 dddqfdqd XXIEEET
)(0 ''''0 qqqddq XXIEET
Differential equations
Power Plant Modelling
Synchronous generator connected to a large power system (Single-Machine Infinite-Bus):
a) diagram of connection, b) equivalent electrical circuit (π).
Synchronous Generator Model Implementation in Matlab®/Simulink® software environment
Electric Power System (EPS)
C
D
q
= t
A
B
d
Q
f (Xd – X’d) (X’d – X"d) X”d
Efd E’q E”q Uq
Id (Xq – X’q) (X’q – X”q) X”q
E’d E”d Ud
Iq
Rotor d-axis Rotor q- axis
Generator equivalent circuits
DRsT1
DRsK
s
IRK
EsT
1
fdEES
PRK
EK
AsT1
AK
FsT1
FsK
Efd
VPF VREF
─ ─ VC
─
DC4B excitation system
hIR uKx 1
2hDR
DR2DR xu
TK
xT
213 xxu
TKKVKxT h
DR
DRPRTAA
434 xEKVxTKxT fdEX
E
FF
fdEXfdE EKVxET 3
DC
4B
Differential equations
AC
8B
Differential equations
2hDR
DR2DR xu
TK
xT
hIR uKx 1
3213 xxxuTKKKxT h
DR
DRPRAA
FDDEXE IKxKVxxT 434
hIR uKx 1
ST4B
Differential equations
212 xxuKxT hPRA
323 11x
KKKKx
KKKKKKx
PMG
IMG
PMG
IMPMGIM
Power Plant Modelling
E
FDCN V
IKI
NEX IfF
EE VS
EK
DK
A
A
sTK1
DRsT
DRsK
1
s
IRK
PRK
Efd
─
IFD
VPF VREF
─ VC
uh
EsT1
AC8B excitation system
TVTI
E
FDCN V
IKI
TLPITPE IXKKjVKV )(
NEX IfF
GK
AsT11
sKK IR
PR sKK IM
PM Efd
VPF VREF
─ VC
IFD
─
ST4B excitation system
Evaluation of the exciter saturation curve SE(Efd).
E
FDC V
IK NEX IfF
Efd
IFD
VE
b.)
III
I
II
1
1 0
a.)
FEX
IN
Three-phase bridge rectifier: a.) voltage-current characteristic, b.) block diagram.
Excitation System Model Implementation in Matlab®/Simulink® software environment
IEEE Standard 421.5-2005: IEEE Recommended Practice for Excitation System Models for Power Stability Studies
Integrating the intelligent optimisation algorithms with the power plant model for parameters identification
Power Plant Modelling Model Parameters identification process in Matlab®/Simulink®
SIMULINK® Simulation
for new parameters
(+measurement
input DATA)
Parameters update
MATLAB® Genetic
Algorithm [Toolbox]
Plant measurement DATA from
SC Power Plant
Data input to model
Simulated and measured
outputs YES
NO
Model Parameters
Stopping criterion
met ?
Model = structure + parameters
Power Plant Modelling
Steady-State Data Start-Up Data
Parameters identification process based on measurement data from SCPP
GA Fitness Function: Based on measured data form SCPP 1. Mechanical Power output Pm 2. Main steam pressure MSP 3. Reheater pressure RHP
Error calculation based on Integral of Time Absolute Error (ITAE) criteria:
Input Data: Output Data: FWF – feedwater flow FF – fuel flow
Pm – mechanical power MSP – main steam pressure RHP – reheater pressure
MODEL
Power Plant Modelling Model Parameters Verification – Results for the best parameters set
data from industry Simulink model
000 Pm – mechanical power
2000 4000 6000 8000 10000 12000 14000 0.3 0.4 0.5 0.6 0.7 0.8 0.9
t [s]
PM
[p
u]
00 MSP – main steam pressure
2000 4000 6000 8000 10000 12000 14000 0.4
0.5
0.6
0.7
0.8
0.9
1
t [s]
MS
P [p
u]
2000 4000 6000 8000 10000 12000 14000 0.3
0.4
0.5
0.6
0.7
0.8
t [s]
RH
P [p
u]
00 RHP – reheater pressure
Outline
• Overview of the EPSRC Project (J Wang)
• Power Plant Modelling (J Wojcik)
• Power Plant Simulation (Y L Xue)
• Summary (J Wang)
THE UNIVERSITY of BIRMINGHAM
Content
Tsinghua University Development of 600MW supercritical pulverized coal
power plant simulation software Summary
Tsinghua University is ranked the top university in China Seasons in Tsinghua University, located in Beijing
Ther
mal
En
gin
eeri
ng
Dep
artm
ent
Institute of Thermal Engineering
Institute of Power Mechanics & Engineering
Institute of Fluid Mechanics & Engineering
Institute of Engineering Thermophysics
Institute of Simulation & Control of Power System
Division of Thermal Power System State Key Laboratory of Control & Simulation of Large Power System & Generation Equipment
Tsinghua University has 56 academic departments
Research and Teaching in Power Plant Modeling and Simulation at Tsinghua University
• The research in this area has over 30 years history
• Giving great contributions to China power industry development
• Playing a major role in training key skilled personnel required in China
• Leading in the research areas of power plant modeling and simulation, clean coal technology and CCS
• State-of-the-art research facilities
Operator skills contest 135MW CFB Power Plant Simulator State Key Task 10.5 National Development Plan in China
Energy and Power Engineering Simulation Practice Compulsory Subjects for 3rd year undergraduates
Teaching facilities
Development of large scale power plant simulation software
• Principle
• Theoretical basis – System Theory, Control Engineering, Computer Science
– Thermodynamics, Fluid dynamics, Combustion
– Mass/Energy/Momentum conservation equation, heat transfer equation, state equations
Example - comparison results of a CFB simulator - Bed temperature, coal and oil flow rate in startup process
Bed Temp.
Feed Coal
Feed Oil
Field data Simulation results
600MW SCPC Simulation Scope
Objective
– Understand the dynamic load response character of SCPC
– Improve its control quality
Simulation Scope The complete process of SCPC power plants from fuel preparation to electricity output
– Main devices • Boiler, Turbine, Generator,
• Auxiliary Power, and related auxiliary machine
– Control Systems • DAS/MCS/FSSS/BMS/SCS/ECS/DEH/ETS
– Malfunctions simulation
– Human Machine Interface
600MW SCPC Simulation – Hardware Configuration
仿真服务器
就地操作站1 就地操作站2 DCS操作站2
……
指导教师工作站
DCS操作站1
……
大屏幕投影
工程师工作站Instructor Station Simulator Server
Local Operator Station DCS Operator Station
Large Screen Display
Engineer Station
600MW SCPC Simulation - Software Structure
Simulation
Support
System
Process models
Control system models
Database managem
ent
Network communic
ation
Real-time running
Model develop support
600MW SCPC Simulation - Key Challenges
(1) Dynamic model of water fall
• Subcritical boiler riser tube – one-section lumped parameter model
• Supcritical boiler water fall – multi-section lumped parameter model
– At subcritical pressure, the water is heated gradually into steam-water mixture (two phase flow)
– At supercritical pressure, the water is heated and evaporated into steam directly (one phase flow)
– Near the critical point, the specific heat capacity shows dramatic change
1 2 N
Heat
OutletInlet
Heat
Inlet Outlet
600MW SCPC Simulation - Key Challenges
(2) Dynamic model of build-in startup separator
(3) Build the steam/water thermodynamic property calculation method
Subcritical Supcritical
Steam Water Separator Steam Chamber
Wet State Dry State
Boundary Node
600MW SCPC Simulation - Key Challenges
(4) Control system model
• Automatically stabilize the process to improve the operator training quality
• Basis for advanced study on control system strategy and controller parameter optimization
Keep a proper coal water ratio - to track the unit load command quickly while minimize the main steam temperature
Feed forward signal from unit load command - to coordinate boiler/turbine response
Control intermediate point temperature or enthalpy - to keep stable heat distribution in water wall
Multivariable nonlinear control
600MW SCPC Simulation - Feedwater Control
Feed water flow control in once-through supercritical coal-fired boiler is different with that in drum-type boiler
– The fluctuation of feed water flow or combustion ratio all have great impact on the dynamic of unit load and main steam temperature due to lack of drum
– To regulate unit load with minimum main steam temperature variation, the combustion ratio (fuel and air flow) and feed water flow should keep a proper ratio—coal/water ratio
Control scheme: • Outer loop: feed water flow
command, consists of two parts: a basic command comes from coal-water ratio calculation, then plus a calibration signal from middle point temperature control.
• Inner loop: feed water pump speed control
600MW SCPC Simulation – Human Machine Interface
600MW SCPC Simulation – progress summary
• Completed
– Main devices modeling
– Substance property calculation
– Main Control system modeling
– Main steam-water system modeling
• To be developed
– HMI (DCS, DEH, MEH, etc) to facilitate the research on dynamic response for grid code compliance
– Joint debugging and integration of the whole simulator
– Dynamic characteristic analysis and coordination control strategy optimization
– Research on CCS+PC
Outline
• Overview of the EPSRC Project (J Wang)
• Power Plant Simulation (Y L Xue)
• Power Plant Modelling (J Wojcik)
• Summary (J Wang)
THE UNIVERSITY of BIRMINGHAM
Summary
• The grid code study and comparison are carried out
• The first version of Mathematical modelling for the whole
plant process was derived
• Simulation programme at the industrial scale is to complete
soon.
• Post combustion CCS process dynamic simulation study
started a few months ago (Shen Guo)
• Computational intelligent algorithms are used for optimisation
THE UNIVERSITY of BIRMINGHAM
Summary
Next stage work:
dynamic responses analysis and Grid Code compliance
control strategy for improvement of dynamic responses
in parallel with:
Post combustion CCS dynamic modelling
and simulation is on going
new/additional intelligent algorithms for power plant
optimisation
THE UNIVERSITY of BIRMINGHAM
Summary
Collaboration:
We would like to work with other academic
institutes together in the research area of
mathematical modelling and simulation of
large scale power plant with CCS process.
THE UNIVERSITY of BIRMINGHAM
Thank You!
THE UNIVERSITY of BIRMINGHAM