1. Integration of Combined-Cycle Units into Economic Dispatch Computation
Client: Faculty Advisor: Group Members:
MidAmerican Energy Dr. Gerald B. Sheblé Brent Miller
Company Mun-Hong “Marvin” Chong
Jason Mardorf
Zobair Molla
May04-11
April 27, 2004

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

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

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

5. Definitions
Combined-Cycle Plant: A generation facility which recovers waste heat to produce electricity
Economic Dispatch: Technique which decides the point at which to operate all units most cheaply
GA: Genetic Algorithm. An optimization technique which models natural evolution
LaGrangian Optimization: Classical optimization technique used in economical dispatch.
System Lambda: The LaGrangian multiplier represents the incremental cost of operating the system at one more MW

6. Acknowledgements Dr. Sheblé- Faculty advisor
MidAmerican Energy Company - Client

7. 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

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

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

10. Operating Environment
Windows based PC
Normal computer operating environment
MATLAB software

11. Intended User(s) Introductory knowledge of economic dispatch
Understanding of power system analysis
Understanding of elementary differential calculus
Workers in a utility’s energy control center

12. Intended Use(s)
Determine dispatches for all units to meet demand at the lowest cost
Be able to input generator unit parameters
Provide proof of concept for client
End product can be used as a benchmark for future projects

13. Assumptions Project does not include unit commitment
All unit in test set must remain on always
The heat rate curves will not change with time
Straight line interpolation between breakpoints is enough accuracy
Prohibited zones are ignored for simplicity
Combined cycle unit will only operate with recovery unit engaged to
reduce complexity

14. Limitations A finite number of units (12 units) Solution time
Static data: nothing is being updated
Required accuracy: within a few MW

15. 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
Documentation and Test results
Provide user documentation to client
Give optimal parameters of code

16. Project Activity Description

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

18. Present Accomplishments
Finalized the end product design
Developed a flow chart of this design
Wrote all of the code to implement the design
Testing and user documentation

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

20. Advantage/Disadvantage
Classical techniques
Advantage
Easy
Standard
No issues
Disadvantage
Not accurate
Phony data

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

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

23. 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

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

25. 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

26. Research Activities: 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)

28. Research Activities: G A
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. Repeat the fitness evaluation
6. Mutation: Random bit flipping to avoid local minima
7. Stop after X number of generations.

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

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

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

32. 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”

33. Design Activities 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

34. 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
Performs the chromosome “Swap” to form a new generation
Repeats this process for X generation

35. Testing Activities Ran multiple test in MATLAB
Attempt to get consistent results
Minimize total cost

36. Other Activities
User Documentation
Client Meeting

37. Resources and Schedules

38. Resources and Schedules
Resource Requirements
Personal effort requirements
Other resources requirements
Financial requirements
Schedules
Tasks vs. Calendar

39. Personal Effort Requirements

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

41. 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

42. Tasks Vs Calendar

43. Closure Materials

44. Project Evaluation Understanding problem 100% Developing linear model
Learning SGA theory
Develop Code
User Documentation
95%

45. Commercialization
No commercialization planned

46. Recommendations Consider more constraints Make the code more dynamic
Conversion to C code
Write code to calculate system incremental cost data for all linear units

47. Lessons Learned Things that went well Things that did not go 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

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

49. 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
Communicate more consistently with the client

50. 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

51. 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.

52. Questions ?