1. THE UNIVERSITY
of BIRMINGHAM
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)
Mathematical Modelling and Simulation of Power Plants and CO2 Capture
WORKSHOP
University of Warwick, 20th-21st March 2012

2. 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

3. Outline
• Overview of the EPSRC Project
• Power Plant Simulator
• Power plant modelling
• Summary
THE UNIVERSITY
of BIRMINGHAM

4. Supercritical technology
Subcritical Supercritical Ultra supercritical
(conventional)
Temperature 500 – 550 500 – 600 550 – 600, (600 – 700)*
(°C)
Pressure (MPa) 16 – 17 24 – 26 27 – 32, (40 – 42)*
Features Drum: single Once through: Once through: double
reheat single reheat reheat
Efficiency 33 - 35 40-45 42 – 47, (50 –
cycle (%) 55)*
THE UNIVERSITY
of BIRMINGHAM

5. Future new power plants in the UK - SUPERCRITICAL
Power generation responses
to the demand changes
Fast enough to satisfy the grid
specification
THE UNIVERSITY
of BIRMINGHAM

6. Subcritical Supercritical
Subcritical water-steam cycle Supercritical water-steam cycle (no
Drum – energy storage phase change)
Once-through operation – no energy
Challenges: storage
Can supercritical power generation responses to the demand
changes fast enough to satisfy BG Grid Code requirement?
THE UNIVERSITY
of BIRMINGHAM

7. 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

8. List of UK power stations - All Subcritical (~33% efficiency)
Station Name Representation Company Address Capacity
in MW
Aberthaw B National Ash RWE Npower The Leys Aberthaw, Barry South CF62 42W 1,489
Glamorgan
Cockenzie ScotAsh Scottish Power Prestopans East Lothian 1,152
Cottam EDF Energy EDF Energy Cottam Power Nottinghamshire DN22 0ET 1,970
Company, PO Box 4, nr
Retford
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 DN14 0BS 1,960
Humberside
Ferrybridge C Keadby generation Scottish & Southern PO Box 39, Stranglands Knottingley West WF11 8SQ 1,955
Ltd Energy plc Lane Yorkshire
Fiddlers Ferry Keadby generation Scottish & Southern Widnes Road Cuerdley Warrington WA5 2UT 1,961
Ltd Energy plc
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- Fife FK10 4AA 2,304
on-Forth
Lynemouth Alcan Alcan Primary Metal - Ashington Northumberland NE63 9YH 420
Europe
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 Nottinghamshire DN22 9BL 1,932
Company, Retford
Wilton Hargreaves CCP ICI PO Box 1985, Wilton Middlesborough TS90 8WS 100
International
THE UNIVERSITY
of BIRMINGHAM

9. Collaboration
Mathematical
modelling, simulation Industrial scale power
study, dynamic
Consortium
interactions plant modelling and
response analysis,
optimal control, simulation,
Exchange of software
Grid Code studies materials, data,
information development,
Shared models,
verification
software and
simulations
Supercritical water, Integrated testing
test rig evelopment, programme Power plant control,
Experimental tudies, intelligent
data collection and algorithms,
analysis

10. 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

11. 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 Electricity
Generation Demand at all time.
PGen PDem
Electricity Electricity Power System
Generation Demand Instability
Frequency Total Outage
Deviations (Blackout)
THE UNIVERSITY
of BIRMINGHAM

12. UK Power System
Electricity Supplied by Fuel Type in 2010:
Transmission System Operators (TSOs):
• National Grid
• Scottish and Southern Energy
• Scottish Power
System Data:
Circuit Voltage Circuit Length
TSO
[kV] [km]
National Grid 400, 275 ~14,000
Scottish and
275, 132 ~5,000
Southern Energy
Scottish Power 400, 275, 132 ~4,000
THE UNIVERSITY
of BIRMINGHAM

13. UK Power System — The Grid Code —
Nominal Frequency: 50 Hz
Frequency Variation Interval [Hz]
Normal
Critical Situations
Operation
49.5 – 50.5 47.0 – 52.0
Frequency Control Strategies
Type of Frequency Control
Response Time
Strategy
active power increase within 10 s and
Primary Frequency Response
maintained for another 30 s
active power increase within 30 s and
Secondary Frequency Response
maintained for another 30 min
active power decrease within 10 s and
High Frequency Response
maintained thereafter
THE UNIVERSITY
of BIRMINGHAM

14. UK Power System — The Grid Code —
Frequency Response Capability of a Generating Unit
Test:
A frequency ramp decrease/
increase of 0.5 Hz over a period of 10 s.
THE UNIVERSITY
of BIRMINGHAM

15. 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

16. Power Plant Modelling
Mathematical modelling of Supercritical Power Plant in Matlab®/Simulink® software environment
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
Δω Single-Machine
Control
Governor Infinitive Bus
System
CVArea IVArea
WC PMSP Pe Pe
δ Pg
WFWF Electrical
ΔPpa COAL SC WSC Steam PM Synchronous Eqb Qg
Edb Power
Tin MILL WPF BOILER PRH Turbine Generator Ug
Ig System
B+jG
EFD Ig
Excitation
System Ug
Mathematical model of Supercritical Power Plant - Block diagram.

17. Power Plant Modelling
Coal Mill Model Implementation in Matlab®/Simulink® software environment
Two different types of pulverised coal mill in power plants
Vertical Spindle Tube-Ball mill ΔPout , ΔPin , Tin , Tout
mill Ap1 Ap2 Wc 
M
c 1 Mc P
Differential equations
Kf  s K1,K2,
K3,K4,
Mc_initial K15 K5,K6,
K7,K8,
ΔPout, Mpf_initial  K14,
W pf M pf Mpf T
1 K17
K16 
s
Block diagram of coal mill model.
‘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
Model based on mass balance and heat balance:
Graphical
Unit
Interface

18. Power Plant Modelling
SC Boiler Model Implementation in Matlab®/Simulink® software environment
The steam patch is divided into following parts:
• Economiser node
• Waterwall node
HP IP+LP • Superheater node
• Main steam line node
• Reheater node
condenser
to
Feedwater flow
Hi1
Fuel Fuel
flow 1
KECO
1 Differential equations
1+TFFs TECOs
Air Econo- Feedwater 

TECO  PECO  H i1  WFWF  H o1  WECO  K ECO  QB
Ho1
miser AECO√

Hi2

TWW  PWW  H i 2  WECO  H o2  WWW  KWW  QB
Flue gases KWW
1
TWWs

TSH  PSH  H i 3  WWW  H o3  WSH  K SH  QB


TCV  PCV  H i 4  WSH  H o4  WCV
Water-Steam loop in basic once-through boiler design. Ho2
AWW√


TRH  PRH  H i 5  WCV  H o5  WRH  K RH  QB
Hi3

TFF  QB  WPF  QB
Model based on mass balance and energy balance KSH

1
TSHs
Ho3
ASH√

V Q Ki
Hi4
1
hi wi ho wo 
TCVs X
WCV
p v wi Hi 1 P Ho4
CVArea

h τ Ts
Hi5
1
KRH
wo Ho  TRHs X
WRH
q Ho5
IVArea
Steam pressure 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
Block diagram of boiler model.

19. Power Plant Modelling
Steam Turbine and Governor Models Implementation in Matlab®/Simulink® software environment
Tandem-Compound Single-Reheat DEH control system
Steam Turbine Control mode
Switch
S1 S2 S3 S4
   feed forward loop
Pm1 SC on off off off
SCLF on off on off 1
K1 K3 K5 K7 0
SCPF on off off on
SCLFF off on on off 1 2
PMS π
1 1 1 1 SCPFF off on off on
π
1+sT4 1+sT5 1+sT6 1+sT7
GVArea IVArea K8 ∆n VD
K2 K4 K6 ∆f 1
1+sT1
K   PID 
  
-
Pref
Generic Model of Steam Turbine/ feedback loop Pload 3
Tandem-Compound Single-Reheat Steam Turbine Pms 4
Differential equations

T4WSC  ( PB  GVArea )  WSC DEH control system – block diagram,

where: ∆f – frequency deviation; ∆n – speed deviation; K– speed drop ;
T5 PRH  WSC  PRH
Pref – reference load signal; Pload – load signal;

T6WCR  WRH  WCR Pms – main steam pressure.
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

20. Power Plant Modelling
Synchronous Generator Model Implementation in Matlab®/Simulink® software environment
Differential equations
B
   s 
Generator equivalent circuits
d
Tm   Pm  Pe  D
 (Xq – X’q) (X’q – X”q) X”q Iq (Xd – X’d) (X’d – X"d) X”d Id
f
 = t   
Td''0 Eq''  Eq'  Eq''  I d ( X d'  X d'' )
Q
  E  E  I ( X  X ) E’d E”d Ud E’q E”q
A
Tq''0 Ed'' '
d
''
d q
'
q
''
q
Uq
Efd
D 
Td' 0 Eq'  E  E  I ( X  X )
fd
'
q d d
'
d
Rotor q- axis Rotor d-axis
q 
T E'
q0
'
d  0  E  I ( X  X )
'
d q q
'
q
C
Electric Power System (EPS)
Synchronous generator connected to a large power system
(Single-Machine Infinite-Bus):
a) diagram of connection, b) equivalent electrical circuit (π).

21. Power Plant Modelling
Excitation System Model Implementation in Matlab®/Simulink® software environment
VREF
Differential equations
VPF K PR
x1  K IR  uh

K IR KA Efd
VC─
   
1
K 
s 1  sTA
sTE TDR x 2   DR
 T u h  x 2

─  DR 
DC4B
─ sK DR
1  sTDR

KE
 K  
TA x3  K AVT  K PR  DR uh  x1  x2 
  
 
S E E fd
 TDR  
sK F
1  sTF
TF x 4 

KF
x3  V X  K E E fd   x4
TE
DC4B excitation system 
TE E fd  x3  V X  K E E fd
VREF
VPF
K PR
uh Efd Differential equations
VC ─

K IR

KA
1  sTA
 1  x1  K IR  uh

s
─
sTE
FEX  f I N 
K
TDR x 2   DR


u h  x 2 Evaluation of the exciter
sK DR T 
S E VE   DR  saturation curve SE(Efd).
AC8B
1  sTDR
I FD  K  
 KE I N  KC
VE TA x3  K A  K PR  DR
  u h  x1  x2   x3

 TDR   a.)
KD
b.)
IFD TE x4  x3  VX  K E x4  K D I FD
 FEX
AC8B excitation system 1 VE Efd

KG I
VREF I FD
VPF KC FEX  f I N 
─ Differential equations II IFD VE
K 1 K
 K PR  IR  IM Efd x1  K IR  uh
VC ─ s 1  sTA  K PM
s
  III
ST4B
TA x2  K PR  uh  x1  x2
 0
VT 1 IN
VE  K PVT  j ( K I  K P X L ) I T   K K K  K G K IM
IT x3   K IM  G PM IM
   x2 
 x3
 1  K G K PM  1  K G K PM Three-phase bridge rectifier: a.) voltage-
IFD IN
I
 K C FD
VE
FEX  f I N  current characteristic, b.) block diagram.
ST4B excitation system
IEEE Standard 421.5-2005: IEEE Recommended Practice for Excitation System Models for Power Stability Studies

22. Power Plant Modelling
Model Parameters identification process in Matlab®/Simulink®
Model = structure + parameters
Integrating the intelligent optimisation algorithms with the power plant
model for parameters identification
Plant measurement
DATA from
SC Power Plant
Data input to model
Simulated and
MATLAB® Parameters SIMULINK®
measured
Genetic update Simulation Stopping YES
outputs Model
Algorithm for new criterion
parameters
Parameters
[Toolbox] met
(+measurement ?
input DATA)
NO

23. Power Plant Modelling
Parameters identification process based on measurement data from SCPP
Steady-State Data Start-Up Data
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:
Pm – mechanical power
FWF – feedwater flow MODEL MSP – main steam pressure
FF – fuel flow
RHP – reheater pressure

24. Power Plant Modelling
Model Parameters Verification – Results for the best parameters set
0.9
0.8
PM [pu]
0.7 Pm – mechanical power
000
0.6
0.5
0.4
0.3
2000 4000 6000 8000 10000 12000 14000
t [s]
1
0.9
MSP [pu]
0.8
0.7 00
MSP – main steam pressure
0.6
0.5
0.4
2000 4000 6000 8000 10000 12000 14000
t [s]
0.8
RHP [pu]
0.7
00
RHP – reheater pressure
0.6
0.5
0.4
data from industry
0.3
2000 4000 6000 8000 10000 12000 14000 Simulink model
t [s]

25. 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

26. Content
 Tsinghua University
 Development of 600MW supercritical pulverized coal
power plant simulation software
 Summary

27. Tsinghua University is ranked the top university in China
Seasons in Tsinghua University, located in Beijing

28. Tsinghua University has 56 academic departments
Institute of Thermal
Engineering
Thermal Engineering
Institute of Power
Mechanics & Engineering
Department
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

29. 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

30. Teaching 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

31. 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

32. 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

33. 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

34. 600MW SCPC Simulation – Hardware Configuration
Large Screen
大屏幕投影
Display
指导教师工作站 仿真服务器 工程师工作站
Instructor Station Simulator Server Engineer Station
…… ……
就地操作站1 就地操作站2
Local Operator Station DCS操作站1
DCS Operator DCS操作站2
Station

35. 600MW SCPC Simulation - Software Structure
Process
models
Model Control
develop system
support models
Simulation
Support
System
Database
Real-time
managem
running
ent
Network
communic
ation

36. 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
Heat
Heat
Inlet Outlet
Inlet Outlet
1 2 N

37. 600MW SCPC Simulation - Key Challenges
(2) Dynamic model of build-in startup separator
Subcritical Supcritical
Steam Water Separator Steam Chamber
Wet State Dry State
Boundary Node
(3) Build the steam/water thermodynamic property calculation method

38. 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
Multivariable nonlinear control
 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

39. 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

40. 600MW SCPC Simulation – Human Machine Interface

41. 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

42. 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

43. 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

44. 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

45. 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

46. Thank You!
THE UNIVERSITY
of BIRMINGHAM