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Integrated Optimization for GHG Reduction

Integrated Optimization for GHG Reduction. RGGI Stakeholder Meeting NYC, New York February 16 th , 2005. NeuCo, Inc. Boston-Based Software Company Founded in 1997 Market Leader in Combustion Optimization Providing AI Solutions to Electric Power Data to Knowledge to Action

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Integrated Optimization for GHG Reduction

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  1. Integrated Optimization for GHG Reduction RGGI Stakeholder Meeting NYC, New YorkFebruary 16th, 2005

  2. NeuCo, Inc. • Boston-Based Software Company • Founded in 1997 • Market Leader in Combustion Optimization • Providing AI Solutions to Electric Power • Data to Knowledge to Action • Process Control to Enterprise Optimization • Running on >80 Large Fossil Generating Units • Fuel Efficiency Improvements from 0.5 – 2.5% • NOx Reductions from 5 - 25% • Developing/Demonstrating Integrated Optimization Suite

  3. Customers El Paso Electric

  4. NeuCo Growth

  5. NEUCo Solutions

  6. ProcessLink Solutions for GHG Reduction • CombustionOpt • NOx and Heat Rate Reduction though Fuel & Air Management • PerformanceOpt • Heat Rate Reduction through Reduced Controllable Losses • MaintenanceOpt • Heat Rate Reduction through Reducing Maintenance-Related Inefficiencies • SootOpt • NOx and Heat Rate Reduction through Optimal Boiler Cleaning • DispatchOpt • Meeting electricity demand with the most efficient units

  7. NEUCo Solutions & GHG Benefits • CombustionOpt provides reductions of 0.25-2.0% with simultaneous NOx reductions. • PerformanceOpt can add another 1.0-1.5% reduction. • SootOpt can provide an additional 0.5-1% improvement. • MaintenanceOpt, by identifying equipment degradation and efficiency losses, may dwarf other gains but won’t be demonstrated until late 2005. • DispatchOpt ensures most efficient mix of units for any overall electricity demand. • These products will also help users respond to existing and emerging competitive pressures in the face of fewer skilled operators and engineers.

  8. Summary of Potential Impacts on GHG

  9. Technical Appendices

  10. Combustion Optimization • Lower NOx (5-25%) • Improved heat rate (0.5-2.5%) • Better control of CO, LOI, and opacity • Enhanced operator performance • More consistent operations • Advanced diagnostics • Lower post-combustion costs • Capital preservation

  11. +0.5% -15% Boiler EfficiencyNOx Impact of Combustion Optimization on Operations & Performance Before

  12. CombustionOpt biases are engaged here Boiler Efficiency Increases Same 600 MW Unit Five Years Later (1/1/05)

  13. Boiler Efficiency Trend at CSU Drake 7

  14. 25% NOx, Drake Unit 7, Dec 28 - Feb 1st NOx Trend at CSU Drake 7

  15. LOI Indicators Over One Year with CombustionOpt

  16. NOx Over One Year with CombustionOpt

  17. CombustionOpt biases are engaged here. Average Heat Rate goes down Heat Rate Improvement at a T-Fired Unit

  18. Heat Rate Improvement at a 600 MW B&W Opposed Unit

  19. Substantial NOx Reductions are also Achieved

  20. SootOpt • Two modules • Multiple zones, real time CFs • Flexible integration approach • Utilizes existing technology • Addresses global objectives

  21. PerformanceOpt • Serves as bridge between physical and business optimizers. • Builds upon investment and field experience through EPRI and Black & Veatch. • Combines thermodynamics with inductive and expert systems technologies. • Collects, calculates and validates plant performance metrics. • Coordinates equipment level actions toward the unit goals of heat rate, emissions and capacity. • Advanced analysis and diagnostics tools support improved maintenance and remote diagnostics.

  22. Ash Plugging in AH

  23. MaintenanceOpt • Exploits same underlying thermodynamic model and rule sets as PerformanceOpt. • Focuses on inefficiencies requiring maintenance actions to rectify. • Maintenance actions tend to have greater impact on efficiency than DCS-actuated controllable losses. • Integrates with all major CMMS platforms. • Advanced analysis and diagnostics tools support improved maintenance and remote diagnostics.

  24. DispatchOpt • Use real-time cost modeling to optimize unit-specific production levels for aggregate MW commitments at any node. • Explicitly addresses start-up and shut-down costs. • Simulates optimal dispatch under any set of unit costs, operating parameters and market conditions. • Inductively forecasts demand and LMP on Day-Ahead and Hourly basis. • Ensures most efficient mix of units meeting any overall level of electricity demand.

  25. NeuCo’s Optimization Framework

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