1. Integration of Combined Cycle Units into Economic Dispatch Computation
Client’s Name: Faculty Advisor Name: Group Members’ Name:
MidAmerican Energy Dr Gerald B. Sheblé Brent Miller
Company Mun-Hong “Marvin” Chong
Jason Mardorf
Zobair Molla
May04-11

2. Presentation Outline II. Project Activity Description
Introductory Materials
A. Problem statement
B. Operating environment
C. Intended use(s) and user(s)
D. Assumptions and limitation
E. End product and other deliverables
II. Project Activity Description
A. Previous accomplishments
B. Present accomplishments
C. Approaches considered and used
D. Project definition activities
E. Research activities

3. Presentation Outline F. Design activities G. Implementation activities
H. Testing, results and modification
I. Other important activities
Resources and Schedules
A. Resources and schedules
1. Personal effort requirements
2. Other resource requirements
3. Financial requirements
B. Schedules

4. Presentation Outline Closing Materials A. Project evaluation
B. Commercialization
C. Recommendation for additional work
D. Lessons learned
E. Risk and risk management
F. Closing summary

5. Problem Statement Combined-cycle plant
-Three gas-fired combustion turbines
-One heat recovery unit
-Together comprise a combined cycle plant
-Heat rate curve is not a typical
-Acts as turbocharger

6. Problem Statement Problem statement
Combined cycle units have non-monotonically increasing curves
Economic dispatch: meet demand at lowest cost
Can’t use standard optimization techniques

7. Problem Statement Solution approach statement
Separate the linear units and the combined cycle units
Combine these techniques to yield the lowest cost

8. Operating Environment
Windows based PC
Normal computer operating environment
MATLAB

9. Intended User(s) Introductory knowledge of economic dispatch
Understanding of power system analysis
Understanding of elementary differential calculus

10. Intended Use(s) Be able to input generation parameters
For a given demand produce lowest cost solution
Provide proof of concept for client

11. Assumptions Assumptions Justification
Given real world representative data
Needed to perform reasonable test
Given genetic algorithm code to study
Needed to fully understand the algorithm
Given data in MW range
Need to know proper range of values
Client will receive program code
Part of original agreement
End product shall be on a windows PC
Team members have the most experience on PC
To be used in USA
Countries have different power systems
Input data from client
The output is only as good as the input

12. Limitations Limitations Justification Max and min number of units
Design requirements
Max solution time
Client requirement
Required accuracy

13. End Product and Other Deliverables
Program code
Do file I/O
Determine lowest cost solution to meet electric demand
Output each unit’s power output
Output each unit’s cost for a specified power output
Test results
Give client results of test data
Give optimal parameters of code

14. Project Activity Description

15. Previous Accomplishments
Learning genetic algorithm (GA) concepts
Did a conventional dispatch of generators with segmented operating areas
Project Plan
Poster
Design Plan

16. Present Accomplishments
Finalized the end product design
Developed a flow chart of this design
Wrote a large part of the code

17. Approaches Considered
Standard LaGrangian techniques
Convex optimization techniques
Genetic algorithms techniques

18. Advantage/Disadvantage
Classical techniques
Advantage
Easy
Standard
No issues
Disadvantage
Not accurate
phony data

19. Advantage/Disadvantage
Convex optimization techniques
Advantage
Mathematically grounded
Apparently easier
Implement equations
Disadvantage
Too mathematical based
Didn’t feel comfortable with it

20. Advantage/Disadvantage
Genetic Algorithms techniques
Advantage
No solution space problem
Will work on any weird function
Disadvantage
A high learning curve
Takes computing power

21. Advantage/Disadvantage
Matlab
Advantage
Members’ familiarity level
Ease of testing
Natural use of matrices
Disadvantage
Programs don’t run as fast
Global variables can cause problems

22. Selected Approach and Why
Genetic algorithm/Classical approach
Faculty advisor has extensive knowledge of genetic algorithms
Made best use of each technique

23. GA/LaGrangian Main Idea: Use each technique at its strong point
LaGrangian techniques excel at optimizing monotonically increasing functions
Genetic algorithms excel at optimizing any type of function
Result: Split the problem into two parts
Linear units
Combined cycle units

24. LaGrangian Key advantage Incremental costs of all units are equal
A linear equation-(Incremental cost curves)
Can develop a system chart to treat the system as one unit. (Graphical method)

26. GA Part of Solution Genetic algorithms are for optimization
No proof as to how they work…they just do
Model nature…survival of the fittest
1. Represent solution as a binary chromosome
2. Determine the “fitness” of the encoded solution
3. Crossover: The fittest solutions exchange their “DNA”
4. The results of this crossover form a new generation
5. Mutation: Random bit flipping to avoid local minima
6. Stop after X number of generations.

27. GA - Chromosome Encode a solution in binary chromosome
Make a population of these chromosomes

28. GA - Fitness
Evaluate the fitness of each chromosome, for each member of the population
Fitness Function unit i
Fitness Function unit i

29. GA - Fitness
Determine each chromosome’s relative fitness to the whole population

30. GA - Crossover Crossover (DNA swapping)
-Randomly select site to do crossover (swap)
7. This process completes one generation
Crossover sites
Parents - Generation “n”
Children - Generation “n+1”

31. Project Design Input the demand to be dispatched
GA selects operating point for CC units
Do table lookup of linear units to dispatch the demand minus Power (MW) from CC units
Evaluate the total cost of using a particular chromosome as a solution
Use this cost as the fitness function to determine chromosomes for next generation

32. Implementation Activities
Code that:
Reads in the units’ IHR data and system data
Generates the first generation of the GA
Decodes the chromosome
Determines the amount for the linear units to dispatch
Does table lookups determining the operating point for each unit
Does cost calculation for operating each generator at a particular point
Assigns a fitness value to a chromosome
Randomly selects chromosomes for mating based on normalized fitness value

33. Resources and Schedules

34. Resources and Schedules
Resource Requirements
- Personal effort requirements
Other resources requirements
Financial requirements
Schedules
Tasks and Subtasks vs. Calendar

35. Personal Effort Requirements

36. Other Resources Requirements
Reference book:
Genetic Algorithms in Search, Optimization and Machine learning

37. Financial Requirements
Item
W/0 Labor
With Labor
Parts and materials:
Poster
$50
Printing and Binding
$10
Book
$31.50
Subtotal
$91.50
Labor at $10.00 per hour
a. Molla, Zobair
$1,920
b. Miller, Brent
$2,000
c. Chong, Mun Hong
$1,880
d. Mardof, Jason
$1,930
$7,730
Total
 $ 91.50
$7,821.50

38. Tasks and Subtasks

39. Closure Materials

40. Project Evaluation Understanding problem 100% Developing linear model
95%
Learning SGA theory
Develop Code
50%
Testing
15%

41. Commercialization
No commercialization planned

42. Recommendations Unit commitment Ramp rates Conversion to C code
Commercialization package

43. Lessons Learned Things that went well Team/FA meetings Design
Linear Dispatch
Things that did not go well
Understanding previous code
Determining which design to use
Understanding all of client’s data

44. Lessons Learned Technical knowledge gained
LaGrangian optimization techniques
Simple genetic algorithms
Matlab programming
Non-technical knowledge gained
Communication
Documentation

45. Lessons Learned Things to be done differently if done again
Develop linear system data earlier
Understand the workings of a GA sooner
Come to decision on approach earlier

46. Risk and Risk Management
Risk 1: Loss of team member
Management: Make sure of working knowledge of the design
Risk 2: Future users not able to understand our code
Management: Provide ample comments and documentation of the theory

47. Closing Summary
Problem: Normal optimization techniques don’t work on non-monotonically increasing curves
Our solution minimizes computation by splitting up the linear and non-linear units
This project is important because it involves saving money. This is nearly always a motivating factor.

48. Questions ?