Real Time Simulation of Power Plants

Transcription

1 Real Time Simulation of Power Plants Torsten Dreher 1 System Simulation Group Friedrich-Alexander-University Erlangen-Nuremberg Siemens Simulation Center, Erlangen December 14, torsten.dreher@informatik.uni-erlangen.de

2 Outline Introduction to Thermal Power Plants Usage of Simulation in Thermal Power Plants Problems and Solutions (Examples) Conclusions

3 Outline Introduction to Thermal Power Plants Usage of Simulation in Thermal Power Plants Problems and Solutions (Examples) Conclusions

4 Introduction to Power Plants Usage of Simulation Problems and Solutions Thermal Power Plants Heat energy sources can be fossile, nuclear, biomass, waste, geothermal, solar etc. LEHRSTUHL FU R INFORMATIK 10 (SYSTEMSIMULATION) Conclusions

5 Scheme of Thermal Power Plant

6 Scheme of the Thermodynamic Cycle

7 The Thermodynamic Cycle

8 The Combined Cycle Power Plant (CCPP) ˆ efficiency up to 60% ˆ by gas or integrated gasification combined cycle (IGCC) ˆ also as singleshaft

9 Power Plant Automation

10 Outline Introduction to Thermal Power Plants Usage of Simulation in Thermal Power Plants Problems and Solutions (Examples) Conclusions

11 Usage of Simulation in Thermal Power Plants for ˆ the design of all major parts ˆ error detection ˆ error reconstruction ˆ design changes ˆ automation purposes

12 Introduction to Power Plants Usage of Simulation Problems and Solutions Conclusions Simulation for Finding the Design, e.g. Evaporator LEHRSTUHL FU R INFORMATIK 10 (SYSTEMSIMULATION)

13 One-dimensional mass-, momentum- and energybalance leads to system of partial differential equations (PDEs): ρ t = 1 A ṁ z ṁ t = 1 (ṁ2 A z ρ h t = ṁ A ρ Flow in the Evaporator ) ( p A ( 1 ρ p z h z + 1 ρ τ U A z + ρ g sin(ϕ) + τ U ) A ) + U q mf A ρ + 1 ρ p t

14 Spatial Discretization of the Evaporator Discretization leads to differential algebraic equations (DAEs): ρ 1 t = 1 A z (ṁ 1 ṁ in )

15 Solving the System Solve the DAE-system with an implicit method (Euler, Runge-Kutta etc.) Resulting matrix is sparse

16 More Simulation A story... ˆ a module detects an error ˆ an other one finds its reason ˆ the source of fault can be removed

17 Error Detection I Power output: Detected discrepancy between simulated value (blue) and metered value (red)

18 Error Detection II Correlates with loss of steam: Simulated (blue) and metered (red)

19 Finding the Physical Error

20 Reproducing the Source of Fault Analysis of instabilities in the evaporator Without deflector plates With deflector plates

21 Taking Measures in Designchanges

22 Simulation for the Automation System Simulation replaces physical process and several levels of automation; same automation used as in plant

23 For the Entire Lifecycle

24 Requirements Simulate ˆ the whole plant ˆ dynamically ˆ in real-time ˆ on a single PC ˆ with given accuracy ˆ in an easy to parameterize environment

25 Outline Introduction to Thermal Power Plants Usage of Simulation in Thermal Power Plants Problems and Solutions (Examples) Conclusions

26 Water-Steam-Properties IAPWS-IF97 implementation ˆ by International Association for the Properties of Water and Steam ˆ standard for industrial use ˆ too slow ˆ no derivatives ˆ numerical problems

27 Water-Steam-Properties: (Bi-)Cubic Spline Interpolation Problems with spline interpolation (1-D example) S 0 (x), x [x 0, x 1 ] S S(x) = 1 (x), x [x 1, x 2 ] S n 1 (x), x [x n 1, x n ] S j (x) =a j + b j (x x j )+ c j (x x j ) 2 + d j (x x j ) 3 j =0,..., n 1 match nodes, be twice continuous differentiable in nodes

28 Water-Steam-Properties: Numeric Problems Choice of the appropriate equation system A PDE-System with density, enthalphy and massflow as states requires the property p = p(ρ, h) A PDE-System with pressure, enthalphy and massflow as states requires the property ρ = ρ(p, h)

29 Water-Steam-Properties: Density-Enthalpy Pane p = p(ρ, h)

30 Water-Steam-Properties: Pressure-Enthalpy Pane ρ = ρ(p, h)

31 Increasing Performance Parallelization on Many-Core-Processors ˆ as known: not the clockspeed but number of cores will increase ˆ reason: power consumption increases exponentially with the clock frequency ˆ more low-frequency cores on a single chip ˆ Moore s Law still valid ˆ from quad-core to many-core processors ˆ gap between the slow memory access and fast CPU execution ˆ data locality and data communication ˆ parallel solver

32 Parameterizing the Simulation for the Automation System Problems ˆ not all physical parameters are known ˆ simple model due to real time requirement ˆ sensitivities of parameters are unknown Leads to an optimization problem

33 Dynamic Behaviour Transient operating conditions ˆ starting process lasts approximately 8 hours ˆ at several points the manual intervention of the operator ˆ big number of analogue values

34 Optimization Problem Minimize the difference between metered and simulated curves ˆ for any transition ˆ do not exceed 10% difference at any point ˆ meet static behaviour even more accurate

35 Approaches Problem: Time consuming evaluation of objective function ˆ parameterize single components (e.g. evaporator) first ˆ parametreize static behaviour first Problem: Huge number of parameters ˆ look only at sensitive parameters ˆ use heuristic optimization to get close to global minimum, then proceed with a local method Problem: Unlimited number of transient operating conditions ˆ use a model that is based on physics ˆ limit the parameters to physically reasonable values

36 Outline Introduction to Thermal Power Plants Usage of Simulation in Thermal Power Plants Problems and Solutions (Examples) Conclusions

37 Conclusions We have learned about simulation applications in power plants ˆ the design of all major parts ˆ error detection ˆ error reconstruction ˆ design changes ˆ automation purposes

38 Conclusions Take home message ˆ simulation is a wide field in industry ˆ not only for design ˆ several approaches only possible with latest or future computer technology ˆ there are several research fields with still unsolved ploblems Go, solve them!

39 Conclusions Take home message ˆ simulation is a wide field in industry ˆ not only for design ˆ several approaches only possible with latest or future computer technology ˆ there are several research fields with still unsolved ploblems Go, solve them!

40 Acknowledgements ˆ Prof. U. Ruede ˆ Siemens Simulation Center ˆ Siemens E F ES EN 72 ˆ Siemens E F ES EN 11 ˆ Siemens CT PP 2 Thank you for your attention!