Download
portfolio selection for energy projects under the clean development mechanism cdm n.
Skip this Video
Loading SlideShow in 5 Seconds..
Portfolio selection for energy projects under the Clean Development Mechanism (CDM) PowerPoint Presentation
Download Presentation
Portfolio selection for energy projects under the Clean Development Mechanism (CDM)

Portfolio selection for energy projects under the Clean Development Mechanism (CDM)

107 Vues Download Presentation
Télécharger la présentation

Portfolio selection for energy projects under the Clean Development Mechanism (CDM)

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Portfolio selection for energy projects under the Clean Development Mechanism (CDM) Olena Pechak, PhD candidate George Mavrotas, Asst. Professor School of Chemical Engineering National Technical University of Athens, Greece 1ο ΠΑΝΕΛΛΗΝΙΟ ΦΟΙΤΗΤΙΚΟ ΣΥΝΕΔΡΙΟ ΕΛΛΗΝΙΚΗΣ ΕΤΑΙΡΕΙΑΣ ΕΠΙΧΕΙΡΗΣΙΑΚΩΝ ΕΡΕΥΝΩΝ (Ε.Ε.Ε.Ε.)

  2. Structure of the presentation Introduction CDM projects Project selection problem Description of the method Two step method Incorporate uncertainty Monte Carlo simulation in GAMS Results and conclusions E.E.E.E., 25 – 27 Νοεμβρίου 2010 2

  3. Introduction Climate change is a complicated problem created by humanity. In order to face it, UNFCCC was adopted. It coordinates activities for mitigation and adaptation to climate change. Kyoto Protocol to the UNFCCC provides three types of flexible mechanisms to reduce GHG emissions: joint implementation (JI), clean development mechanism (CDM) and international emission trading (IET). RES activities within CDM: 65% of all projects in CDM pipeline 46% of emission cuts 858 wind energy projects (17.4% of total in Jan. 2010) 284 of which (with installed capacity - 12.55 GW) are already registered. E.E.E.E., 25 – 27 Νοεμβρίου 2010 3

  4. Introduction: Main features of CDM • Cooperation between developing and developed countries • The projects activity provides GHG emission reductions compared with BAU scenario, • During the operation of the project achieved reductions are translated into Certified Emission Reductions (CERs), backed by the 1 t CO2-eq • CERs may be sold in carbon market. The price for them is variable • Project duration is variable and is chosen before the registration of the project E.E.E.E., 25 – 27 Νοεμβρίου 2010

  5. Introduction: Wind energy Influencing factors on popularity of wind energy: • growing energy demand, • increased concerns for environmental and climate issues, • improvements of technology itself. 3 key regions: Europe, North America, and Asia(with China and India as main players) E.E.E.E., 25 – 27 Νοεμβρίου 2010

  6. Project selection problem Selection of project portfolio with constraints like: • Policy • Budget • Geographical distribution • Technical constraints and MW of installed capacity • Logical constraints In addition we have: • Set of hypothetic projects • Variable CER prices Solving software: GAMS CPLEX 12.2 E.E.E.E., 25 – 27 Νοεμβρίου 2010

  7. Project selection problem Input • 100 CDM projects across 4 regions (China 50, India 30, Latin America 10, Mediterranean 10) • Budget constraint is 2.8 billion $, the sum of candidates - 4.2 billion $ • regional constraints (at least 3 projects from Latin America and Mediterranean, India must have no less than half of China) • MW constraints - not more than 75% of MW in China • logical constraints (mutually exclusive projects) • technology constraints (sum of off shore MW across all countries less than 2 GW) E.E.E.E., 25 – 27 Νοεμβρίου 2010

  8. Project selection problem In calculations we: • maximize total NPV (with discount rate =8%) • consider only the CER price as uncertainty factor • perform Monte Carlo simulation and optimization • for normal distribution around 20$ (μ=20, σ=3.3) • for uniform distribution in [10, 30] The process of solving the problem is divided into 2 big steps. E.E.E.E., 25 – 27 Νοεμβρίου 2010

  9. Description of the method Step I • An Integer Programming (IP) model is developed with binary decision variables that express the existence of each project in the selected portfolio. • objective function - cumulative NPV • constraints: policy, logical and budget • Due to the uncertainty related to the future price of Certified Emission Reductions (CERs) a Monte Carlo simulation-optimization process is designed. Projects from resulting portfolios form subsets: • Green set – projects are present in all portfolios • Red set – none of projects is selected in any portfolio • Grey set – projects, present in some portfolios E.E.E.E., 25 – 27 Νοεμβρίου 2010

  10. Description of the method Step II • Only projects from “grey” set are under consideration • A new IP is formulated only with the “grey” projects and uses the frequency obtained from the Monte Carlo simulation as their objective function coefficients. • The constraints are the same as before appropriately modified to take into account the sure adaptation of the “green” projects. The final output is the set of projects (portfolio) with the best performance under the given uncertainty conditions. E.E.E.E., 25 – 27 Νοεμβρίου 2010

  11. μ Graphical representation of Monte Carlo simulation and optimization CER price(i) Obj function (z) uniform i =1…1000 Solution of IP CER price(i) normal E.E.E.E., 25 – 27 Νοεμβρίου 2010

  12. Results The normal and uniform distributions of projects’ NPV during the Step I E.E.E.E., 25 – 27 Νοεμβρίου 2010 12

  13. Results Results of the Step I in both cases are similar. The differences are observed only in frequencies of the projects from the “Grey” set. Totals: Green set – 58 Red set – 23 Grey set – 19 E.E.E.E., 25 – 27 Νοεμβρίου 2010 13

  14. Results Let’ s see the Grey sets more carefully. E.E.E.E., 25 – 27 Νοεμβρίου 2010 14

  15. Results: final selection Results of the Step II. Case of Normal distribution of CER prices: Case of Uniform distribution of CER prices: E.E.E.E., 25 – 27 Νοεμβρίου 2010 15

  16. Conclusions about the method • Integer Programming can be effectively used for a portfolio selection problem • Theobjectivefunctioncanbeselectedinordertoreflectthedecisionmakingpreferences • ThecombinationofMonteCarloandoptimizationisusedsuccessfullyfordealingwithuncertainty • Thetwostepapproachis a usefuldecisionaidtoolthat (a) classifiestheprojectsand (b) usestheinformationfromthefirststeptodrivethesecondoptimizationandresultinthefinalchoice E.E.E.E., 25 – 27 Νοεμβρίου 2010 16

  17. Conclusions about the problem The resulting sets of projects from the Monte Carlo simulation are the same for normal and uniform distribution. The difference between uniform and normal distribution is observed in frequencies of “grey” projects. It also influences the ranking of the grey projects between each other. 19 ambiguous projects (grey set) from all kinds Normal and uniform distribution give almost the same final choice In final selection there are: China – 33 projects (#39 and #43 are very similar), Latin America – 8 projects, Mediterranean – 8 projects, India – 24 projects. E.E.E.E., 25 – 27 Νοεμβρίου 2010 17

  18. Future research Incorporate multiple criteria MCDA and then IP Multiobjective IP Group decision making IP for optimization Multiple portfolios, one for each DM Same approach (green, red, grey set) Step1: Green set  the principle of unanimity Step2: Grey set  the principle of majority E.E.E.E., 25 – 27 Νοεμβρίου 2010 18

  19. Thank you! E.E.E.E., 25 – 27 Νοεμβρίου 2010 19