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RGGI Stakeholder Meeting NYC, New York February 16 th, 2005 Integrated Optimization for GHG Reduction.

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Presentation on theme: "RGGI Stakeholder Meeting NYC, New York February 16 th, 2005 Integrated Optimization for GHG Reduction."— Presentation transcript:

1 RGGI Stakeholder Meeting NYC, New York February 16 th, 2005 Integrated Optimization for GHG Reduction

2 1 NeuCo, Inc. Boston-Based Software Company l Founded in 1997 l Market Leader in Combustion Optimization Providing AI Solutions to Electric Power l Data to Knowledge to Action l Process Control to Enterprise Optimization Running on >80 Large Fossil Generating Units l Fuel Efficiency Improvements from 0.5 – 2.5% l NOx Reductions from 5 - 25% Developing/Demonstrating Integrated Optimization Suite

3 2 Customers El Paso Electric

4 3 NeuCo Growth

5 4 NEUCo Solutions

6 5

7 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

8 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.

9 8 Summary of Potential Impacts on GHG

10 9 Technical Appendices

11 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

12 11 Before Impact of Combustion Optimization on Operations & Performance Boiler Efficiency NOx -15% +0.5%

13 12 Same 600 MW Unit Five Years Later (1/1/05) CombustionOpt biases are engaged here Boiler Efficiency Increases

14 13 Boiler Efficiency Trend at CSU Drake 7

15 14 25% NOx, Drake Unit 7, Dec 28 - Feb 1st NOx Trend at CSU Drake 7

16 15 LOI Indicators Over One Year with CombustionOpt

17 16 NOx Over One Year with CombustionOpt

18 17 Heat Rate Improvement at a T-Fired Unit CombustionOpt biases are engaged here. Average Heat Rate goes down

19 18 Heat Rate Improvement at a 600 MW B&W Opposed Unit

20 19 Substantial NOx Reductions are also Achieved

21 20

22 21 SootOpt  Two modules  Multiple zones, real time CFs  Flexible integration approach  Utilizes existing technology  Addresses global objectives

23 22 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.

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25 24 Ash Plugging in AH

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27 26 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.

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30 29 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.

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32 31 ProcessLink Platform Key Attributes Object-oriented optimization and model building platform First hybrid modeling and optimization environment Based on Windows.NET standards Scaleable, modular, extensible design Open architecture Proprietary IP

33 32 NeuCo’s Optimization Framework


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