Study of Supercritical Coal Fired Power Plant Dynamic Responses and Control for Grid Code Compliance

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, 20 th -21 st 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 Power generation responses to the demand changes Fast enough to satisfy the grid specification THE UNIVERSITY of BIRMINGHAM

Subcritical Supercritical Subcritical water-steam cycle Drum energy storage Challenges: Supercritical water-steam cycle (no phase change) Once-through operation no energy storage Can supercritical power generation responses to the demand changes fast enough to satisfy BG Grid Code requirement? 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) 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, Kincardineon-Forth Fife FK10 4AA 2,304 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 Company, Retford Nottinghamshire DN22 9BL 1,932 Wilton Hargreaves CCP ICI PO Box 1985, Wilton International Middlesborough TS90 8WS 100 THE UNIVERSITY of BIRMINGHAM

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

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

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 P Gen Electricity Demand P Dem at all time. Electricity Generation Electricity Demand Frequency Deviations Power System Instability Total Outage (Blackout) THE UNIVERSITY of BIRMINGHAM

UK Power System Electricity Supplied by Fuel Type in 2010: 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 THE UNIVERSITY of BIRMINGHAM

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

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

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

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 Control System Governor Δω Single-Machine Infinitive Bus CV Area IV Area W C ΔP pa T in COAL MILL W FWF W PF SC BOILER P MSP W SC P RH Steam Turbine P M P e Synchronous Generator δ E qb E db I g Electrical Power System P e P g Q g U g B+jG E FD Excitation System I g U g Mathematical model of Supercritical Power Plant - Block diagram.

Power Plant Modelling Coal Mill Model Implementation in Matlab /Simulink software environment Two different types of pulverised coal mill in power plants Vertical Spindle mill Tube-Ball mill A p1 A p2 ΔP out, M pf_initial K f K 16 W c W pf M c_initial K 15 ΔP out, ΔP in, T in, T out M c M pf 1 s 1 s M c M pf K 1,K 2, K 3,K 4, K 5,K 6, K 7,K 8, K 14, K 17 P T Differential equations 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

to condenser Power Plant Modelling SC Boiler Model Implementation in Matlab /Simulink software environment HP IP+LP The steam patch is divided into following parts: Economiser node Waterwall node Superheater node Main steam line node Reheater node Fuel Air Economiser Feedwater Flue gases Water-Steam loop in basic once-through boiler design. Model based on mass balance and energy balance Feedwater flow Fuel flow 1 1+T FF s K ECO K WW K SH H i1 H o1 H i2 H o2 H i3 1 T ECO s A ECO 1 T WW s A WW 1 T SH s T Differential equations ECO PECO Hi1 WFWF Ho 1 T WW PWW Hi2 WECO Ho2 T T SH PSH Hi3 WWW Ho3 CV PCV Hi4 WSH Ho4 T W RH PRH Hi5 WCV Ho5 T FF Q W B PF Q B W W W CV W RH ECO WW SH K K K K RH ECO WW SH Q B Q Q Q B B B H o3 h i w i V p v h τ h o w o w i Q K i H 1 i Ts P H i4 H o4 A SH 1 T CV s CV Area X W CV H i5 q Steam pressure model Where: H i, H o input/output gain of steam flow entering/leaving associated nodes, P node pressure, Q heat transfer to node, W i flow rate of fluid entering node, W o flow rate of fluid leaving node w o H o K RH H o5 1 T RH s X IV Area Block diagram of boiler model. W RH

Power Plant Modelling Steam Turbine and Governor Models Implementation in Matlab /Simulink software environment P MS π Tandem-Compound Single-Reheat Steam Turbine 1 1+sT 4 K 1 1 1+sT 5 π K 3 1 1+sT 6 K 5 1 1+sT 7 K 7 P m1 DEH control system 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 feed forward loop 0 1 1 2 GV Area K 2 IV Area K 4 K 6 K 8 f 1 1+sT 1 n K - PID V D Generic Model of Steam Turbine/ Tandem-Compound Single-Reheat Steam Turbine feedback loop P ref P load P ms 3 4 Differential equations T W ( P GV ) W 4 SC B Area P 5 RH WSC PRH T T W 6 CR W RH W CR SC 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. 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

Power Plant Modelling Synchronous Generator Model Implementation in Matlab /Simulink software environment D C Q B f d q = t A Differential equations s T P P D T T T m T m E E e ' E E I ( X X '' '' ' '' ' '' d 0 q q q d d d E E E I ( X X '' '' ' '' ' q0 d d d q q q E E E I ( X X ' ' ' d 0 q fd q d d d E 0 I ( X X ' ' ' q0 d d q q q ) '' ) ' ) ) Generator equivalent circuits (X q X q ) (X q X q ) X q I q E d E d U d Rotor q- axis E fd (X d X d ) (X d X" d ) X d I d E q E q U q Rotor d-axis 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).

ST4B AC8B DC4B Power Plant Modelling Excitation System Model Implementation in Matlab /Simulink software environment V REF Differential equations V PF V C K PR K IR s sk DR 1 st DR 1 sk F st F K A 1 st A DC4B excitation system S E 1 st E K E E fd E fd 1 x K IR u h T DR K x 2 T DR DR u h x K T x DR A 3 K AV T KPR uh x1 x2 TDR K F T x F 4 x3 VX K E E fd x4 T T E E E fd x 3 V X K E 2 E fd V PF V C V PF V T I T V REF V C I FD u h V REF E K 1 sk K IR s PR DR st DR K A 1 st A S E V E K E K D st E AC8B excitation system I N K P PR T K s V K V j( K K X ) I IR I FD KC V E I P F 1 1 st A L T f EX I N K PM K G 1 K s IM F f EX I N E fd I K I FD V N C I FD E fd E Differential equations 1 x K IR u h K TDR x 2 T T x DR DR u h K x DR A 3 K A K PR uh x1 x2 x3 T DR T 1 E x 4 x3 VX K E x4 x K IR u h 2 K D I FD Differential equations T x A 2 KPR uh x1 x2 x 3 K IM KGK PM K 1 K K G IM PM KGK IM x2 1 KGK ST4B excitation system IEEE Standard 421.5-2005: IEEE Recommended Practice for Excitation System Models for Power Stability Studies PM x 3 a.) F EX 1 0 I Evaluation of the exciter saturation curve S E (E fd ). II 1 III b.) V E I FD I N I K F f FD C VE EX I N Three-phase bridge rectifier: a.) voltagecurrent characteristic, b.) block diagram. E fd

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 MATLAB Genetic Algorithm [Toolbox] Parameters update SIMULINK Simulation for new parameters (+measurement input DATA) Simulated and measured outputs Stopping criterion met? NO YES Model Parameters

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: FWF feedwater flow FF fuel flow MODEL Output Data: Pm mechanical power MSP main steam pressure RHP reheater pressure

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

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

Thermal Engineering Department Tsinghua University has 56 academic departments 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

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

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

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 Large Screen 大 屏 幕 投 影 Display 指 导 教 师 工 作 站 Instructor Station 仿 真 服 务 器 Simulator Server 工 程 师 工 作 站 Engineer Station 就 地 Local 操 作 站 Operator 1 就 Station 地 操 作 站 2 DCS DCS 操 作 站 Operator 1 DCS Station 操 作 站 2

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

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 1 2 N Outlet

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

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

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