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DYNAMIC PROGRAMMING IN BALANCING RENEWABLE ENERGY INVESTMENT

DYNAMIC PROGRAMMING IN BALANCING RENEWABLE ENERGY INVESTMENT. Cem Keskin * Ass. Prof. Dr. Gülgün Kayakutlu ** *ITU Energy Institute **ITU Industrial Engineering Department. Content. Objective of the Study Literature Survey Methodology Application Results and Discussions

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DYNAMIC PROGRAMMING IN BALANCING RENEWABLE ENERGY INVESTMENT

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  1. DYNAMIC PROGRAMMING IN BALANCING RENEWABLE ENERGY INVESTMENT CemKeskin* Ass. Prof. Dr. GülgünKayakutlu** *ITU Energy Institute **ITU Industrial Engineering Department

  2. Content • Objective of the Study • Literature Survey • Methodology • Application • Results and Discussions • Conclusion and Suggestions YAEM2010 Cem Keskin Gülgün Kayakutlu

  3. Objective of the Study Helping decision makers for small renewable energy investments in terms of: • Considering time dependent factors • Data of renewable resources • Consumption • Investment costs • Power conversion efficiency • Evaluate the alternatives YAEM2010 Cem Keskin Gülgün Kayakutlu

  4. Literature Review Dynamic programming in energy fields is used for: • Both renewable and conventional energy resources (Liu et al., 1999) • Energy resources management (O’keefe & Markel, 2006) • Optimum utilization of multireservoir resources (Ferrero et al.,1998) • Energy systems performance prediction (Chaabene & Annabi, 1996) • Sizing optimization and systems expansion planning (Park et al., 1998) YAEM2010 Cem Keskin Gülgün Kayakutlu

  5. Contribution of the Study • Used for a small scale investment • Used for a time dependent investment planning • States are determined in terms of technology combinations • Fewer parameters than widely accepted methods YAEM2010 Cem Keskin Gülgün Kayakutlu

  6. Methodology: Dynamic Programming • Determination of stages • Determination of states for each stage • Definition of decision function • Definition of recursion function YAEM2010 Cem Keskin Gülgün Kayakutlu

  7. Model: Determination of Stages Stage1 Stage1 Stagen YAEM2010 Cem Keskin Gülgün Kayakutlu

  8. Model: Determination of States Stage1 Stage1 Stagen State1 State1 State1 Staten Staten Staten YAEM2010 Cem Keskin Gülgün Kayakutlu

  9. Model: Decisions Stage1 Stage1 Stagen State1 State1 State1 Staten Staten Staten YAEM2010 Cem Keskin Gülgün Kayakutlu

  10. Model: RecursionFonction Stage1 Stage1 Stagen State1 State1 State1 Staten Staten Staten YAEM2010 Cem Keskin Gülgün Kayakutlu

  11. Problem Definiton A site in Muğla, Bodrum is wanted to be launched grid integrated renewable energy system while net electrical energy (production minus consumption) should be bigger than zero for every month. It has a similar electricity consumption profile to Turkey’s 2008 profile and increase in demand is known. A long term plan (20 year) is need to meet the demand with renewable resources where the potential data are availablein related maps of General Directorate of Electrical Power Resources Survey and Development Administration (EİE). Variations in efficiency and cost of related technologies should be considered. YAEM2010 Cem Keskin Gülgün Kayakutlu

  12. Application Data 1 YAEM2010 Cem Keskin Gülgün Kayakutlu

  13. Application Data 2 YAEM2010 Cem Keskin Gülgün Kayakutlu

  14. Application Data 3 Solar EnergypotentialMap of General Directorate of Electrical PowerResourcesSurveyand DevelopmentAdministration (EİE)

  15. Application Data 4 WindEnergyPotential Data of General Directorate of ElectricalPower ResourcesSurveyandDevelopmentAdministration (EİE) YAEM2010 Cem Keskin Gülgün Kayakutlu

  16. Energy Technologies 1 POWER CHARACTERISTICS Power: 5kw Diameter: 5,5m Height: 12m Cost: 13500$ YAEM2010 Cem Keskin Gülgün Kayakutlu

  17. Energy Technologies 2 Model: STP280 Type: Polycrystalline MaxPower: 280W Efficiency: %14,5 EfficiencyLoss : %1/year YAEM2010 Cem Keskin Gülgün Kayakutlu

  18. Energy Technologies 3 YAEM2010 Cem Keskin Gülgün Kayakutlu

  19. Mathematical Model • Tx= number of technology module x • Mx= Investment cost of technology module x • Pxi = potential of technology x in month i(per module) • = (monthly potential of resource)x(conversion efficiency)x(efficiency loss) • Di = electric power demand in month i • S= Electricity selling price Min Z = (T1 x M1)+……+(Tx x Mx) – S x {∑i=1[(T1 x P1i)+…..+(Tx x Pxi) – Di ]} • ST • ∑X=1 (TxxPxi) ≥ Di for vi TX, PXi, Di , S, Mi≥ 0 YAEM2010 Cem Keskin Gülgün Kayakutlu

  20. Determination of States: Stage4 ( T1x P1i ) + ( T2x P2i ) ≥ Diforvi (x=1 forwindturbine & x=2 for solar panel) YAEM2010 Cem Keskin Gülgün Kayakutlu

  21. DecisionforStates: Stage4 Min Z = (T1 x M1)+(T2 x M2) – S x {∑i=1[(T1 x P1i) +(T2 x P2i) – Di]} YAEM2010 Cem Keskin Gülgün Kayakutlu

  22. RecursionFunction fi = Ci + fi+5 • fi : Total cost at year i • Ci : Cost of investment in year i • fi+5: Total cost of nextstage (fiveyearlater) YAEM2010 Cem Keskin Gülgün Kayakutlu

  23. Solution YAEM2010 Cem Keskin Gülgün Kayakutlu

  24. Solution YAEM2010 Cem Keskin Gülgün Kayakutlu

  25. Results & Discussions YAEM2010 Cem Keskin Gülgün Kayakutlu

  26. Conclusion & Suggestions • Dynamic programming is easy to use for small investments planning • Time dependent investment is observed in different stages • Optimum solution is obtained with fewer parameters • Updated resourse potential data is needed for more precise results • Computerized version will allow a larger set of alternatives • A more detailed cost function is planned YAEM2010 Cem Keskin Gülgün Kayakutlu

  27. QUESTIONS… ? YAEM2010

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