Real-Time Energy Optimization Best Practices

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Real-time energy optimization is applying process models on chillers, boilers, turbines, compressors and other equipment to meet processing requirements at minimum energy costs. Key technology and functionality requirements enable the delivery of sustainable solutions that are useful to operations and save real money. Hear about examples of successful projects and how past lessons have identified, what the requirements of future solutions are. A demonstration of this uniquely capable industry answer will be provided.

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Real-Time Energy Optimization Best Practices

  1. 1. Copyright © 2014 Rockwell Automation, Inc. All Rights Reserved. PUBLIC INFORMATION Real-Time Energy Optimization Best Practices Michael Tay: Pavilion Product Manager
  2. 2. Copyright © 2014 Rockwell Automation, Inc. All Rights Reserved. • What is Real-Time Optimization? • What are known ways RTO can go awry? • How a Rockwell Automation prototype has been designed to solve these issues 2 Real-Time Optimization Best Practices
  3. 3. Copyright © 2014 Rockwell Automation, Inc. All Rights Reserved. What is Real-Time Optimization 3 • RTO sits above the control packages • Performs steady state optimization at the plant level • Uses plant models, business requirements, plant-wide operating conditions, forecast and scheduling information to: • Predict optimal products to make in a plant • When to make them and • What are the best operating conditions to maximize profitability. Where control maintains operations at targets: Optimization determines the best targets. Where advanced control provides best operator performance: Optimization calculates global best performance and only optimization can identify new ways to operate. Optimization requires sufficient complexity and changing conditions (pricing, demand, environmentally driven constraints) otherwise the answer is obvious or is gained through gradual learning without math.
  4. 4. Copyright © 2014 Rockwell Automation, Inc. All Rights Reserved. What is Real-Time Optimization Necessary components: 1. A Steady-state process model 2. Economic information (e.g., prices, costs) 3. A performance Index to be maximized (e.g., profit) or minimized (e.g., cost). Common Types of Optimization Problems 1. Operating Conditions – equipment load, resource storage usage and recycle 2. Allocation – equipment dispatch 3. Scheduling – equipment start-up and shut-down 4 Plant Economic Model Constraint Optimum
  5. 5. Copyright © 2014 Rockwell Automation, Inc. All Rights Reserved. Optimization of Plant Energy Systems  Consider System Holistically  Electricity, steam, chilled water, refrigeration, fuel  Target Economic Optimization of the overall energy system  Optimize against actual operating condition of the plant 5 Save 10-20% of current energy bill!
  6. 6. Copyright © 2014 Rockwell Automation, Inc. All Rights Reserved. Optimization of Plant Energy Systems  Consider System Holistically  Electricity, steam, chilled water, refrigeration, fuel  Target Economic Optimization of the overall energy system  Optimize against actual operating condition of the plant 6 Save 10-20% of current energy bill!
  7. 7. Copyright © 2014 Rockwell Automation, Inc. All Rights Reserved. 7 GT HRSG External Utility Electricity Grid B5 T1 T2 LD1 LD2 Atmosphere LP External Demand HP Header IP Header LP Header IP External Demand HP External Demand B6 B4 Models 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 24 3 8 12 16 20 24 Hour Cost$/KWH Elect Price Objective: Economically optimal supply of steam to process Reliable Steam Supply in a Dairy Plant Real-time pricing Overall Utility Demand Holistic System Models
  8. 8. Copyright © 2014 Rockwell Automation, Inc. All Rights Reserved. DCS Interface: Economic Optimization Optimizer Results Turbine 3 Steam (kg/hr) Turbine 1 Steam (kg/hr) Train A Letdown (T/hr) Train B Letdown (T/hr) LP Makeup (kg/hr) LP Dump Steam (kg/hr) Boiler 5 Production (T/hr) Boiler 6 Production (T/hr) Turbine 3 (kW) Turbine 1 (kW) Boiler 4 Production (T/hr) 28.80 8.00 0.00 20,178 0 11.68 0.00 0 0 2,727 0 Cost Information HP Steam Cost ($/T) IP Steam Cost ($/T) LP Steam Cost ($/T) Coal Cost ($/T) Electricity Cost ($/kWh) Water Cost ($/T) Boiler Efficiencies Boiler 5 Boiler 6 Boiler 4 10.89 9.98 8.83 0.062 0.80 0.74 0.60 5.15 1.54 0.00 Boiler 5 Coal (T/hr) Boiler 6 Coal (T/hr) Boiler 4 Coal (T/hr)Delta Obj Function Opt Current 18.25 18.13 15.32 0.00 0 14,456 15.62 6,257 35 0 758 Opt Current Opt Current Control TagWrite1 Control TagWrite2 60.00 0.50 11.15 10.17 9.33 0.77 0.77 0.60 126.87 3.39 3.37 0.00 $126.87 / hr Driver for action
  9. 9. Copyright © 2014 Rockwell Automation, Inc. All Rights Reserved. What are Known Ways RTO Can Go Awry?  Models don’t cover the entire system necessary to define correct decisions (“the answer is right, except when it isn’t”)  Constraints don’t completely represent operator restrictions on decisions  Models are not kept representative of current plant situation (out-of-date)  Results do not have the frequency required for useful decisions (“I need to babysit the system to trim right after implementing an answer”)  Results are open loop and become operator nuisance instead of performance enabler (closed-loop optimization is recommended).  Report of value is unclear or suspect (measurement of optimization is a challenge under the changing situations that justify optimization) 9
  10. 10. Copyright © 2014 Rockwell Automation, Inc. All Rights Reserved. Initial Application: Plant-wide Optimization
  11. 11. Copyright © 2014 Rockwell Automation, Inc. All Rights Reserved. Predictive Demand Models Copyright © 2011 Rockwell Automation, Inc. All rights reserved. 11 Chilled Water kW Steam Natural Gas Predicted vs. Measured Energy for Campus Buildings
  12. 12. Copyright © 2014 Rockwell Automation, Inc. All Rights Reserved. Easy to Manage and Maintain 12 Steam Electricity Chilled Water MODEL Graphical Representation of the optimization network Pre-defined models for Unit Operation
  13. 13. Copyright © 2014 Rockwell Automation, Inc. All Rights Reserved. Easy to Tune/Calibrate Models 13 Access operation data in a database Monitor model prediction (green) against current or past operation data (black), Initiate refit if needed.
  14. 14. Copyright © 2014 Rockwell Automation, Inc. All Rights Reserved. Easy for Equipment Operators Scheduled capacity vs. anticipated demand Unit operation Schedule The operator can modify the schedule manually The cost & consequence of operator decision will be reported immediately.
  15. 15. Copyright © 2014 Rockwell Automation, Inc. All Rights Reserved. Economics of Optimization Savings proportional to decision complexity!
  16. 16. Copyright © 2014 Rockwell Automation, Inc. All Rights Reserved. Keys to Optimization  Model Accuracy & Calculation Speed: Offering optimal balance between “accuracy” and “computational efficiency”  Sustainability of Optimization Models: Opening optimization problem definition and maintenance to plant or a variety of support personnel  Operator Acceptance: Customized MINL solver with a plant dynamic horizon. Starting and Stopping equipment, managing equipment over time and observing all plant constraints is critical to operator acceptance.  Right Scope of Optimized Equipment: a local optimization is generally an incorrect optimization. Rapid solution allows for closed-loop, real-time optimization and the closer the solution is to the equipment the better.  Accept or Reject Solution: optimization observes all plant constraints relevant to its decisions. Partial acceptance is as wrong as local optimization. Operators can reject and the solution can recalculate with new formulation. (Confidential – For Internal Use Only) Copyright © 2010 Rockwell Automation, Inc. All rights reserved.d
  17. 17. Copyright © 2014 Rockwell Automation, Inc. All Rights Reserved. Current Operation Opt not changing equipment Opt changing equipment Saving not changing equipment Saving changing equipment Optimization Results
  18. 18. Copyright © 2014 Rockwell Automation, Inc. All Rights Reserved. Open-Loop Optimization Results Pre-Optimization Post-Optimization Reduction in average KWhr/TR 6.7% Reduction in standard deviation KWhr/TR ~50% > $150,000/year KW savings: AMPS savings * 480V * 1.732 (3-phase power)/1000 Electrical energy $0.07/KW
  19. 19. Copyright © 2014 Rockwell Automation, Inc. All Rights Reserved. Questions / Next Steps ?  Proteus Model-Based Optimization for Utility Centers: • Easy-to-use (graphical configuration, model library) • Functional (dynamic, discrete/continuous, fast optimization) • Runs equipment optimally to accurate demand forecasts • New product with a legacy of successful sustainable results
  20. 20. Copyright © 2014 Rockwell Automation, Inc. All Rights Reserved. www.rsteched.com Follow RSTechED on Facebook & Twitter. Connect with us on LinkedIn. PUBLIC INFORMATION Mike Tay (metay@ra.rockwell.com) Thank You

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