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

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

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

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

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

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

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

Operating Environment Windows based PC Normal computer operating environment MATLAB

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

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

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

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

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

Project Activity Description

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

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

Approaches Considered Standard LaGrangian techniques Convex optimization techniques Genetic algorithms techniques

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

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

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

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

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

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

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)

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.

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

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

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

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”

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

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

Resources and Schedules

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

Personal Effort Requirements

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

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

Tasks and Subtasks

Closure Materials

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

Commercialization No commercialization planned

Recommendations Unit commitment Ramp rates Conversion to C code Commercialization package

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

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

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

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

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.

Questions ?