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- 1. THE UNIVERSITY of BIRMINGHAM Study of Supercritical Coal FiredPower 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 reheatEfficiency 33 - 35 40-45 42 – 47, (50 –cycle (%) 55)* THE UNIVERSITY of BIRMINGHAM
- 5. Future new power plants in the UK - SUPERCRITICALPower generation responses to the demand changesFast 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 energyChallenges: storageCan supercritical power generation responses to the demandchanges fast enough to satisfy BG Grid Code requirement? THE UNIVERSITY of BIRMINGHAM
- 7. Project ObjectivesThrough study supercritical coal fired power plantmathematical 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 MWAberthaw B National Ash RWE Npower The Leys Aberthaw, Barry South CF62 42W 1,489 GlamorganCockenzie ScotAsh Scottish Power Prestopans East Lothian 1,152Cottam EDF Energy EDF Energy Cottam Power Nottinghamshire DN22 0ET 1,970 Company, PO Box 4, nr RetfordDidcot A National Ash RWE Npower Didcot Nr Oxford OX11 7HA 2,020Drax Hargreaves CCP Drax Power Limited Drax Selby North Yorks YO8 8PQ 3,870Eggborough British Energy British Energy Eggborough Goole North DN14 0BS 1,960 HumbersideFerrybridge C Keadby generation Scottish & Southern PO Box 39, Stranglands Knottingley West WF11 8SQ 1,955 Ltd Energy plc Lane YorkshireFiddlers Ferry Keadby generation Scottish & Southern Widnes Road Cuerdley Warrington WA5 2UT 1,961 Ltd Energy plcIronbridge EON UK PowerGen Buildwas Road Telford Shropshire TF8 7BL 970Kingsnorth EON UK PowerGen Hoo Saint Werburgh Rochester Kent ME3 9NQ 1,974Longannet ScotAsh Scottish Power ScotAsh Ltd, Kincardine- Fife FK10 4AA 2,304 on-ForthLynemouth Alcan Alcan Primary Metal - Ashington Northumberland NE63 9YH 420 EuropeRatcliffe EON UK Powergen Ratcliife on Soar Nottingham NG11 0EE 2,000Rugeley International Power International Power Rugeley Power Station Armitage Road Rugeley WS15 1PR 976Tilbury B National Ash RWE Npower Fort Road Tilbury Essex RM18 8UJ 1,020West Burton EDF Energy EDF Energy West Burton Power Nottinghamshire DN22 9BL 1,932 Company, RetfordWilton Hargreaves CCP ICI PO Box 1985, Wilton Middlesborough TS90 8WS 100 International THE UNIVERSITY of BIRMINGHAM
- 9. Collaboration Mathematicalmodelling, 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 testingtest rig evelopment, programme Power plant control,Experimental tudies, intelligent data collection and algorithms, analysis
- 10. TeamUniversity of Warwick: Prof J Wang, Dr J Wojcik Mr M Draganescu, Mr S GuoUniversity of Birmingham: Dr B Al-Duri, Mr O MohamedTsinghua University Prof. J F Lv, Prof Q R Gao, Dr Y L XueNorth 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 PDemElectricity Electricity Power SystemGeneration Demand Instability Frequency Total Outage Deviations (Blackout) THE UNIVERSITY of BIRMINGHAM
- 12. UK Power SystemElectricity 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 UnitTest: 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 ModellingMathematical 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 plantsVertical 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 GuoModel 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 Hi1Fuel 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 QBModel 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 2PMS 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 Td0 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 Tq0 Ed d d q q q Uq EfdD 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 VREFVPF K PR uh Efd Differential equationsVC ─ 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 systemIEEE 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 + parametersIntegrating 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 SCPPSteady-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] 0.8RHP [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 SystemDivision of Thermal Power SystemState Key Laboratory of Control & Simulation of Large PowerSystem & Generation Equipment
- 29. Research and Teaching in Power Plant Modeling andSimulation 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 facilitiesOperator skills contest135MW CFB Power Plant SimulatorState Key Task 10.5 National Development Planin China Energy and Power Engineering Simulation Practice Compulsory Subjects for 3rd year undergraduates
- 31. Development of large scale power plant simulationsoftware• 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 ScopeObjective – Understand the dynamic load response character of SCPC – Improve its control qualitySimulation 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 ControlFeed water flow control in once-through supercritical coal-fired boiler isdifferent 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. SummaryNext 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. SummaryCollaboration: 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

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