Discover optimization potentials. maximizing efficiency of power plant operation. with state of the art energy management systems

Transcription

1 Discover optimization potentials maximizing efficiency of power plant operation with state of the art energy management systems Dr. Christian Herr STEAG KETEK IT GmbH STEAG KETEK IT, November 2006, Page 1

2 STEAG Power Plant Sites (8.800 MW in total) Based on decades of experience Walsum Lünen Herne Voerde Weiher Bergkamen Bexbach Fenne 600 MW 500 MW 950 MW 2,222 MW 707 MW 747 MW 773 MW 502 MW treading new paths in the energy business. Leuna Termopaipa Iskenderun Köln-Godorf Mindanao Walsum Herne 158 MW 165 MW 1,320 MW 211 MW 232 MW. 750 MW 750 MW COD 2006 COD 2010 COD 2011 COD = commercial operation date STEAG KETEK IT, November 2006, Page 2

3 STEAG KETEK IT products monitor, control and optimize the essential, techno-commercial relationships in the power generation process Quality management of the operational measuring equipment Evaluation of the process component performance Optimization of the plant operation mode Information extraction for state-related maintenance SR2 SR1 SR::EPOS EbsilonProfessional STEAG KETEK IT, November 2006, Page 3

4 SR::x - central data management and state-of-the-art visualization Long-term storage of measured and computed values in time-oriented archives Versatile trend displays featuring drag and drop from values in the process screens provide easy data analysis and evaluation Excel-Add-In and HTML-List generator allow the generation of extensive reporting systems STEAG KETEK IT, November 2006, Page 4

5 EBSILONProfessional - A powerful tool for power plant cycle calculation Software tool for modeling, simulating and validating power plants Continuous development since 1990 Over hundred companies worldwide are using EBSILONProfessional for offline planning tasks and for online performance monitoring and optimization Offline and online what-if calculations can be easily performed with this tool Extensive configuration options at the user s disposal in a graphical user interface (GUI) ( build your own power plant model component by component ) Comprehensive component library for modeling all kinds of power plant processes STEAG KETEK IT, November 2006, Page 5

6 EBSILONProfessional - Entire plant modeling - Mapping of 85 units in India based on GTZ project STEAG KETEK IT, November 2006, Page 6

7 EBSILONProfessional - Multi unit optimization Neural networks can quickly solve complex optimization tasks based on non-linear modeling Training of neural networks Calculation of global optimum The combination of thermodynamic simulation tools, neural networks and heuristic optimization algorithms represent a reliable concept for complex optimization tasks for power plant operation STEAG KETEK IT, November 2006, Page 7

8 SR::EPOS - the SR product for performance monitoring and unit optimization Monitors periodically (every 5 mins) the power plant process technically and economically Evaluates plant components online and provides planning data for state-related maintenance Suggests optimum modes of operation from economic and ecological aspects Evaluates the impact of different, changing environmental conditions Optimizes the units operation and enhances its efficiency typically by %, up to 1% Based on EBSILONProfessional STEAG KETEK IT, November 2006, Page 8

9 SR::EPOS - SIMHADRI Power Plant + 14 additional UNITS (NTPC) - BHEL Cooperation on PADO/BPOS Boiler mapping, optimization of boiler operation including soot blowing, air supply, burner tilt, coal mills, etc. Optimization of turbine cycle, condenser, cold end and preheating Optimization of cleaning of air heaters and ESPs, evaluation of fan operation What-if calculation, metal temperature calculation, set point optimization STEAG KETEK IT, November 2006, Page 9

10 SR::EPOS - Cost of condenser fouling 162T total losses due to condenser fouling STEAG KETEK IT, November 2006, Page 10

11 SR::EPOS::BCM - the SR product for optimizing the soot blowing Controlled by costs or other criteria the optimum points in time for activating the individual blower levels are calculated Closed-Loop application possible if desired Application of fuzzy technology Actual heater efficiencies Allowed minimal efficiency values Soot blowing recommendations Color changes indicate heater efficiency STEAG KETEK IT, November 2006, Page 11

12 SR::EPOS - Online coal analysis (in cooperation with RWE) Steam generator Calculation of heat surface fouling Intelligent soot blowing sequence Coal Online analysis of coal Detection and forecast of fouling behavior; self learning knowledge base Heating value Ash S, Fe K, Ca Al, Si Na STEAG KETEK IT, November 2006, Page 12

13 SR::EPOS - Data preparation / data reconciliation Input data set 1 Input data set 2 Neural Network Output data set Model output Forecast Raw measurement value 5min. average value from main system Range check Substitute value from range check Neural plausibility check Substitute values from neural networks Check by data reconciliation Substitute value from data reconciliation Σ ( x - u) ² /σ² = Min x = Measured value u = validated value σ = standard deviation SR-Value Input value for SR::EPOS-calculation STEAG KETEK IT, November 2006, Page 13

14 SR::EPOS - Online realization of statistical process control based on data reconciliation reduces the noise in key figures derived from site measurements Pre-heaters with small TTD are especially sensitive to measurement errors The online application of data reconciliation points out the continuous degradation of the components STEAG KETEK IT, November 2006, Page 14

15 Objectives of CO 2 monitoring and benchmarking at STEAG based on data reconciliation Fleet wide real time tracking of CO 2 emissions Data reconciliation of coal consumption data Consistent determination of characteristic plant data for the STEAG fleet SR::EMUB - Emission Monitoring & Unit Benchmarking SR::x as central platform for optimization results Provision of current, validated figures of all STEAG power plant units: - Operation mode/ load case - Characteristics - Efficiencies - Coal consumption - CO 2 -emissions STEAG KETEK IT, November 2006, Page 15