real-time energy optimization best practices

Copyright © 2014 Rockwell Automation, Inc. All Rights Reserved.

PUBLIC INFORMATION

Real-Time Energy Optimization Best Practices Michael Tay: Pavilion Product Manager


• 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


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.


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


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!


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!

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

00.010.020.030.040.050.060.070.080.09

24 3 8 12 16 20 24Hour

Cost

$/KW

H

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


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)

28.808.00

0.00

20,178

0

11.68

0.00

0

0

2,727

0

Cost InformationHP Steam Cost ($/T)

IP Steam Cost ($/T)

LP Steam Cost ($/T)

Coal Cost ($/T)

Electricity Cost ($/kWh)

Water Cost ($/T)

Boiler EfficienciesBoiler 5Boiler 6Boiler 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

ControlTagWrite1

ControlTagWrite2

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


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


Initial Application: Plant-wide Optimization


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


Easy to Manage and Maintain

12

Steam

Electricity

Chilled Water MODEL

Graphical Representation of the optimization network

Pre-defined models for Unit Operation


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.


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.


Economics of Optimization

Savings proportional to decision complexity!


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


Current Operation

Opt not changing equipment

Opt changing equipment

Saving not changing equipment

Saving changing equipment

Optimization Results


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


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


www.rsteched.com

Follow RSTechED on Facebook & Twitter. Connect with us on LinkedIn.

PUBLIC INFORMATION

Mike Tay ([email protected]) Thank You

real-time energy optimization best practices

Technology

plantwide optimization

loop optimization

rockwell automation

plant models

steady state optimization

t lp steam cost

cost information hp

t ip steam cost