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.)

E.E.E.E., 25 – 27 Νοεμβρίου 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 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 GW) are already registered.

E.E.E.E., 25 – 27 Νοεμβρίου 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 CO 2 -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 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 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 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 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 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 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 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.

Graphical representation of Monte Carlo simulation and optimization μ CER price(i) Solution of IP i =1…1000 Obj function (z) uniform 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 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 GREEN set 1,3,4,6,8,9,10,11,13,14,15,16,17,18,21,23,24,25,26,2 8,30,37,40,42,44,46,47,49,50,52,53,55,56,57,58,61,6 3,65,66,71,72,74,75,77,78,79,81,82,83,86,87,88,90,9 1,92,93,94,97 RED set 2,5,12,19,20,22,27,31,33,35,36,41,45,48,54,59,68,73, 89,95,96,99,100 GREY set 7,29,32,34,38,39,43,51,60,62,64,67,69,70,76,80,84, 85,98

E.E.E.E., 25 – 27 Νοεμβρίου Results Let’ s see the Grey sets more carefully. # Normal Uniform # N U

E.E.E.E., 25 – 27 Νοεμβρίου Results: final selection Results of the Step II. Case of Normal distribution of CER prices: Case of Uniform distribution of CER prices: ProjectsTotalMWBudget 1,3,4,6,8,9,10,11,13,14,15,16,17,18,21,23, 24,25,26,28,29,30,32,34,37,39,40,42,44, 46, 47,49,50,51,52,53,55,56,57,58,60,61,63,64, 65,66,67,69,70,71,72,74,75,76,77,78,79,80, 81,82,83,84,85,86,87,88,90,91,92,93,94,97, Billion $ ProjectsTotalMWBudget 1,3,4,6,8,9,10,11,13,14,15,16,17,18,21,23, 24,25,26,28,29,30,32,34,37,40,42,43,44, 46, 47,49,50,51,52,53,55,56,57,58,60,61,63,64, 65,66,67,69,70,71,72,74,75,76,77,78,79,80, 81,82,83,84,85,86,87,88,90,91,92,93,94,97, Billion $

E.E.E.E., 25 – 27 Νοεμβρίου Conclusions about the method  Integer Programming can be effectively used for a portfolio selection problem  The objective function can be selected in order to reflect the decision making preferences  The combination of Monte Carlo and optimization is used successfully for dealing with uncertainty  The two step approach is a useful decision aid tool that (a) classifies the projects and (b) uses the information from the first step to drive the second optimization and result in the final choice

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