©2012, Cognizant | All rights reserved. The information contained herein is subject to change without notice.

Dynamic Utility Optimization of Gas supply in Pipeline Network using ROMeo

| ©2012, Cognizant

Industrial Problem

5

Transportation of Industrial Gases in large quantities using industrial gas networks

Networks consisting of multiple/complex supply/demand nodes and subject to various time dependent constraints

This dynamic behavior impacts operation, pricing and inventory Electricity costs represent a significant portion of operating Electricity Price Varies for Large Industrial Consumers

Time of Day; Off Peak; Day Ahead; Real-time

| ©2012, Cognizant

Goals & Strategy

6

Goals Minimize Operating Energy Cost over a given time horizon Understand the impact of

Variation in supply and demand Scheduled equipment outages

Strategy Use Pipeline as inventory Build inventory when the electricity is cheap Opportunity for Dynamic Optimization!

| ©2012, Cognizant

Background

7

Cognizant is a member of CAPD at CMU Collaboration with Prof. Biegler’s Group at CMU

Cognizant has global R&D partnership and joint Go To Market agreement with Invensys

Cognizant-EMS practice: Experience in Process Simulation and Optimization Optimization problem of Industrial Relevance

Large Scale Optimization New Problem Industrial Gas Transport Optimization

Explore applicability of ROMeo to solve Dynamic Optimization problems

| ©2012, Cognizant

Problem Formulation

8

Objective Minimize Operating Energy Cost over a given time horizon

Constraints Dynamic Model Equations

Physical equations describing the system Pressure @ Demand Points Operating Constraints Price Schedule (Tariff)

Flat Rate Time of Day Day ahead Real time

| ©2012, Cognizant

Model Equations

9

Material Balance (assume ideal gas & isothermal conditions)

Multi-Period Form of Material Balance:

i – pipe segment i

0

z

q

t

P

RT

AM w

| ©2012, Cognizant

Model Equations

10

Momentum Balance

Pressure Drop

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Model Equations

11

Compressor Power

Objective

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Constraints

12

Cyclic Operation: Inventory Constraint

Max Load Limit for Compressor: 350 kWMinimum Demand Pressure: 1000 kPaDemand Flow Rate: 180 kg/hrParameters:

• Network length: 122 km• Pipe Dia: 0.1 m

| ©2012, Cognizant

Sample Network used in the study

13

2.1 km < Pipe Segment < 9.6 kmTotal Length = 122 kmPipe Dia = 0.1 m Demand Pressure >= 1000 kPa

Demand Flow = 180 kg/hr

| ©2012, Cognizant

Solution Overview

14

Initial Solution Approach (Lumped Flowsheet Model) Unified network model solved using ROMeo Not easy to extend and difficult to troubleshoot Partly overcome by introducing configurations ROMeo Solver Tuning

Improved Solution: Componentized Model Arcs Nodes Compressor Properties of Components from Thermo (MW, Cp, Cv) Ability to build configurable networks Flexible and Extensible – can build any network

| ©2012, Cognizant

Flow sheet representation in ROMeo

15

Individual Multi-period models for Source, Sink, Pipe,

Compressor & Junction

©2012, Cognizant | All rights reserved. The information contained herein is subject to change without notice.

Input Specifications across a time

horizon

©2012, Cognizant | All rights reserved. The information contained herein is subject to change without notice.

Change of electricity pricing

©2012, Cognizant | All rights reserved. The information contained herein is subject to change without notice.

Problem was solved

successfully

| ©2012, Cognizant

Validation Steps

20

Confirmed the results of Componentized Model with “Lumped” model Steady-State for 24 Time Periods (1 hour each) Validated Pressure Drop with Pipe Phase

0 5 10 15 20 25 30 35490

505

520

535

550

Node #1

Time

Pres

sure

Compressor Drop Out

© 2010, Cognizant Technology Solutions. | Confidential

Compressor Drop-out problem Small Pipeline model – Two sources and three demands Compressor outage at a scheduled time (T=6)

Compressor Drop-Out Results

22© 2010, Cognizant Technology Solutions. | Confidential

0 1 2 3 4 5 6 7 8 9479.94

479.96

479.98

480

480.02

480.04

480.06

480.08

480.1

480.12

Node 6Node 7Node 8

Time PeriodsP

ressure

0 1 2 3 4 5 6 7 8 90

500

1000

1500

2000

2500

3000

3500

Compressor 1Compressor 2

Time Periods

Pow

er

| ©2012, Cognizant

Optimization

23

Objective – Minimize Electricity Cost Found Optimal Solutions for 24 Time Periods

Solver Tuning was needed Electricity Pricing Models

Flat Rate – Optimized – Used for comparison Time of Day (6 hour periods)

| ©2012, Cognizant

Electricity Pricing Schemes

24

Pricing SchemeElectricity Cost (¢/kWh)

0 – 6 hr 7 – 12 hr 13 – 18 hr 19 – 24 hr

A1 (Flat Rate) 6.50 6.50 6.50 6.50

B 5.00 8.00 5.00 8.00

A2 (Flat Rate) 7.50 7.50 7.50 7.50

C 5.00 10.00 5.00 10.00

| ©2012, Cognizant

Results – Compressor Profiles

25

Cost per kWhA: Off Peak ¢ 5B: On Peak: ¢ 8 C: On Peak: ¢ 10

Fixed Variable

Customer Flowrate

Free Variable

Supplier Pressure

Length of Time Interval = 1 hr

- ON Peak hours

Savings = 16.7% for C

Savings = 4.6% for B

| ©2012, Cognizant

Results – Inventory

26

Cost per kWh

On Peak - ¢ 8 & 10Off Peak - ¢ 5

Fixed Variable

Customer Flowrate

Free Variable

Supplier Pressure

Length of Time Interval = 1 hr

- ON Peak hours

| ©2012, Cognizant

Results – Sample Pressure Profile

27

Cost per kWhA: Off Peak ¢ 5B: On Peak: ¢ 8 C: On Peak: ¢ 10

Fixed Variable

Customer Flowrate

Free Variable

Supplier Pressure

Length of Time Interval = 1 hr

- ON Peak hours

| ©2012, Cognizant

Conclusions

28

For the pipeline network studied, we observed cost savings in the range of 6% - 16%

Models for network components – flexibility and easy to configure gas pipeline networks

Multi-period formulation applied to solve a dynamic optimization problem in ROMeo

This opens up interesting opportunities to optimize inherently dynamic operations

| ©2012, Cognizant

Other possible applications

29

Water Transport with Intermediate Reservoirs Water Cost can Depend on Source

Other Cyclical/Dynamic Processes Grade Changes Batch Optimization

We will be glad to discuss any of your specific problems where this approach could be applied

©2012, Cognizant | All rights reserved. The information contained herein is subject to change without notice.

Thank you

©2012, Cognizant | All rights reserved. The information contained herein is subject to change without notice.

Appendix

| ©2012, Cognizant

Pipeline Optimization – One slider

32

Industrial Scenario: Gas companies operate Compressors to

transport gas from their storage/gas well to various consumers through pipeline networks

In deregulated electricity markets, electricity price fluctuates during the entire day

And, electricity cost form the significant component of the overall operations

This drives companies to optimize electricity consumption at the Compressor systems level

Project Background: Cognizant leveraged its academic1

relationship to develop dynamic optimization scenarios

And, EMS expertise in developing optimization solutions using ROMeo (Invensys) platform

Solution: Combines the insight on

electricity prices and pipeline network scenarios to optimize compressor operations

Reduces the overall electricity cost by operating the Compressor during Off-peak hours

Respond to network disruptions promptly with inventory in the pipeline

Key Benefits include: Achieve saving up to 4.6% when

electricity the price varies between ¢5 and ¢8

And, up to 16.6% savings when electricity price varies between ¢5 and ¢10

Optimize pipeline network operations in consideration to real-time scenarios like Compressor drop-out

Cost Profile

Other applications: Water Pipeline Optimization Pressure Swing Adsorption

Note:1)A member of Center for Advance process Decision

Making at Carnegie Mellon University

| ©2012, Cognizant

Results – Size of the Problem

33

Mode Time Periods Total # of Variables

Total # of Equations

Total # of Fixed Variables

Degrees of Freedom

Simulation

0 451 431 20 0

5 2697 2582 115 0

10 4942 4732 210 0

20 9432 9032 400 0

Optimization

0 456 436 17 3

5 2702 2587 97 15

10 4947 4737 177 30

20 9437 9037 337 60

| ©2012, Cognizant

Results – Supply Rate

34

Cost per kWh

On Peak - ¢ 8 & 10Off Peak - ¢ 5

Fixed Variable

Customer Flowrate

Free Variable

Supplier Pressure

Length of Time Interval = 1 hr

- ON Peak hours

| ©2012, Cognizant

Results - Summary

35

Pricing Scheme

Flat rate electricity

price

Weekly Operating

Cost ($)

% Saving with respect to flat

rate price

Compressor Peak Load (%) Peak Inventory

(kg)Node 10 Node 24 Node 29

A1 6.50 375.82 0 44.62 11.19 12.95 12553.93

B 358.52 4.60 100 63.74 79.97 19608.27

A2 7.50 433.31 0 44.62 11.19 12.95 12553.93

C 361.15 16.65 100 100 100 28786.12

| ©2012, Cognizant

Inventory Profile

36