Integration of Combined-Cycle Units into Economic Dispatch Computation

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 • 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 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 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 1 1 0 1 1 0 1 0 0 1 0 0 1 0 1 1 1 1 0 1 1 0 1 0 1 1 0 1 1 0 1 0 0 1 0 0 1 1 1 0 0 1 0 0 0 0 1

30. GA - Fitness • Evaluate the fitness of each chromosome, for each member of the population Fitness Function unit i 1 1 0 1 1 0 1 0 0 1 0 0 1 0 1 1 Fitness Function unit i 1 1 0 1 1 0 1 0 0 1 0 0 1 0 1 1

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 1 1 0 1 0 0 1 1 1 1 0 1 1 0 1 0 1 1 0 1 1 0 1 0 1 1 0 0 0 0 1 0 0 0 0 1 1 0 1 0 1 1 0 0 0 0 1 0 0 0 0 1 0 0 1 1 1 1 0 1 1 0 1 0 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

42. Tasks VsCalendar

43. Closure Materials

44. Project Evaluation

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 • 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