J Kittel

J W Martin

HYDROGEN INDUCED CRACKING (HIC) -LABORATORY TESTING ASSESSMENT OF LOW ALLOY STEEL LINEPIPE

Keywords: HIC, sour (H2S containing) environment, linepipe steel

 testing were assessed. The literature sources suggested a variety of methodologies that can be used for FFP HIC testing. However, a universally applicable FFP test method was not identified. Therefore, an experimental study was launched to establish a suitable methodology and better define the key parameters involved in HIC phenomena. The use of "sweet service These results suggest that a "regions of environmental severity" diagram, similar to that developed for sulphide stress cracking in the 1990s and later incorporated into NACE MR0175/ISO 15156, can be developed for the HIC resistance of suitable sweet service grade steels. This relates the HIC resistance to the two-dimensional environmental severity defined by the partial pressure of H 2 S and the pH. This diagram will enable the mapping of HIC according to cracking susceptibility and help in defining the 'HIC sour service' limits for linepipe steels.

grade" C-Mn linepipe in mild sour service could provide economic benefits, but would require an assessment of the HIC performance to be undertaken for the intended service conditions. Therefore, five different sweet service line-pipe steels were used for these studies. Experiments consisted of HIC exposure tests based on the NACE TM0284-2003 Standard Test Method. Parameters studied were pH (3.5 to 5.5), H 2 S partial pressure (3 to 100mbar), and time of exposure (96 hours to 3 months). Characterisation included hydrogen content measurement for diffusible hydrogen (glycerol method). HIC cracking was evaluated by ultrasonic testing and by metallographic examination in accordance with the NACE TM0284-2003 Standard Test method.

From the HIC exposure tests, a good correlation was found between diffusible hydrogen and HIC. Above 1ppm diffusible hydrogen, all specimens exhibited severe HIC.

For specimens tested at a level of 10mbar H 2 S no HIC was observed after 96 hours exposure. But tests with longer exposure (4 weeks) did result in HIC. This suggests that the low severity conditions require longer exposures to reach equilibrium levels of internal hydrogen. This must be taken into account for FFP HIC testing in mildly sour environment. Considering the impact of the environment, H 2 S partial pressure seemed to have a much greater impact than pH on the extent of HIC.

INTRODUCTION / BACKGROUND

In the presence of H 2 S, hydrogen atoms originating from the cathodic reduction reaction (driving the anodic dissolution reaction) can diffuse into the steel, resulting in cracking mechanisms such as hydrogen-induced cracking (HIC), sulphide stress cracking (SSC) and stress-oriented hydrogen-induced cracking (SOHIC). The presence of liquid water necessary for the corrosion reactions can occur even if the gas temperature in the pipeline is above the water dew point, e.g. due to the lower temperature of the pipe wall compared to the gas temperature or where the reduction of gas temperature along the pipeline results in the water vapour in the gas condensing out. Due to the fact that such cracks can be very difficult to detect during routine inspection, this type of damage is often regarded as more dangerous than weight-loss corrosion.

The classification of carbon/low alloy steel materials within regions of environmental severity, as already defined for resistance to cracking in NACE MR-0175/ISO 15156 Part 2 [START_REF]Petroleum and natural gas industries -Materials for use in H 2 S containing environments in oil and gas production -Part 2: Cracking-resistant carbon and low alloy steel, and the use of cast iron[END_REF] (Figure 1), could provide a tool for the accurate assessment of HIC susceptibility of sweet service linepipe which may have adequate resistance to HIC in mild sour conditions thus providing significant economic benefits without impacting upon safety. However, it needs to be recognised that the pH -pH 2 S severity regions for SSC and HIC will not necessarily match. Thus, Figure 1 is suitable for SSC resistance only and a similar plot is required for HIC and related mechanisms. In addition, NACE MR0175/ISO 15156 Part 2 does not place any lower H 2 S limit below which an assessment of HIC resistance is unnecessary. The definition of 'sour service' within NACE MR0175/ISO 15156 is not restricted to conditions above 3.5 mbar H 2 S, as was the case in the previous editions of NACE MR0175 which considered only SSC resistance. Therefore, for the definition of the different region boundaries for HIC, environmental conditions with H 2 S partial pressures at and below 3.5 mbar (i.e. region 0) have also to be taken into account. As the standard HIC test is performed under severe conditions, it is an overly conservative method to qualify materials for mildly sour conditions. In addition, it is not sufficient for establishing the region boundaries on a domain diagram of the type in Figure 1. Therefore, there is a strong need for new or modified test conditions/methods with respect to the different operating requirements for fit-for-purpose testing.

As the first step of this study by the European Pipeline Research Group (EPRG) Corrosion Committee, a literature survey was undertaken on HIC phenomena [START_REF] Hermann | HIC Assessment of Low Alloy Steel Line Pipe for Sour Service Application -Literature Survey[END_REF] . Material and metallurgical parameters were investigated; in addition the various testing environments and criteria used for fit-for-purpose testing (FFP) were assessed. The literature sources suggested a variety of methodologies that could be used for FFP HIC testing. It was stated that hydrogen permeation measurements in general can provide a quantitative measure for differentiating HIC performance and that field measurements of the maximum concentration of dissolved hydrogen in steel could also be helpful for FFP classification. However, a universally applicable FFP test method was not identified.

From a consideration of the results from the literature survey a number of key conclusions were drawn, as follows:

 The clarification of the environmental boundary conditions was considered to be the key to FFP testing.  To perform FFP laboratory testing, the test environment must represent the specific operating environment. This can be obtained by the variation of the test severity using modified test set-ups (e.g. the modification of number of exposed specimen sides, H 2 S content, pH, specimen type).  A HIC domain diagram similar to the ISO 15156-2 definition of SSC regions dependant on H 2 S partial pressure and pH, might be obtained, in part, by hydrogen permeation measurements, if these could be correlated to HIC performance.  For acceptable HIC testing, the transferability of the results to the actual field conditions must be achieved. It has been demonstrated that the surface concentration of diffusing hydrogen atoms on operating pipelines can reach a peak value within a short period (even a few days for sour service conditions 2 ), such that in order to match the operating environment the test environment should reach the same peak value.  It was finally stated that full-scale test results do not necessarily correlate with laboratory test results. In particular, tests involving six-sided hydrogen charging might not be correlated with the performance of the pipes under service conditions. During the laboratory test, the hydrogen activity remains constant over the specimen thickness, whereas under service conditions, there is a linear decrease from the inside to the outside of the pipe wall.

Starting from these insights, an experimental study was launched with the objective of establishing a suitable methodology for FFP HIC testing and to better define the key parameters involved in HIC phenomena. A 'reference steel' (type API 5L X65 non sour service steel) was first studied. Experiments consisted of HIC exposure tests based on the NACE TM0284-2003 Standard Test Method 3 . Parameters studied were pH (3.5 to 5.5), H 2 S partial pressure (10 to 100mbar), and time of exposure (96 hours to 1 month). Comparisons between one-sided (more typical of field operating conditions) and six-sided exposure of HIC specimens were also undertaken. Characterisation of the material after test included measurements of the diffusible hydrogen content by the glycerol method. HIC cracking was evaluated both by ultrasonic testing and by metallographic examination in accordance with NACE TM0284-2003.

The results of this preliminary study were reported in a previous Paper [START_REF] Kittel | Hydrogen induced cracking assessment of low alloy steel linepipe for sour service application -Laboratory testing[END_REF] . For the reference steel, cracking occurred mostly in the half thickness region. A good correlation was found between the crack length ratio (CLR) estimated from metallographic examinations, and the crack area ratio (CAR) from ultrasonic testing. A threshold diffusible hydrogen content of 1ppm was determined, above which 100% of the specimens exhibited HIC. It was also found that the low severity environments (down to 3 mbar H 2 S) still could result in HIC, if the time of exposure was sufficiently long (up to 3 months).

The FFP test methodology established as a result of this preliminary study was generally in accordance with NACE TM0284-2003, but with the following deviations/additional features:

 Testing period of two weeks, although longer term tests are required to initiate cracking for lower severity conditions, e.g. 4 weeks at 10mbar H 2 S and pH 3.5.  Six-sided exposure, to ensure attainment of equilibrium levels of hydrogen within the testing period (with one-sided testing, equilibrium could not be ensured).  Test solution parameters varied are pH (buffered values of 3.5, 4.5 and 5.5) and H 2 S partial pressure (10mbar, 35mbar and 100mbar). It was found that the H 2 S partial pressure had a far greater influence on the extent of HIC (and indeed the levels of diffusible hydrogen) than did the pH. Increasing the partial pressure of H 2 S and, to a lesser extent, decreasing the pH resulted in an increased risk/extent of HIC.  Measurements of the diffusible hydrogen made on samples at the end of the HIC test. A good correlation was found between diffusible hydrogen and the extent of HIC  HIC cracking evaluated using both ultrasonic testing and metallographic examination in accordance with NACE TM0284-2003.

Following on from completion of the preliminary study, the present study to assess a total of five different sweet service steels was launched to evaluate the effects over a range of different steels.

MATERIAL TESTED DURING THE PRESENT STUDY

Five sweet service steel types were tested during the present study (Table 1). No welds were included in the tested specimens, only the base metal was tested.

The chemical compositions of all steels were analysed by optical emission spectrometry (OES), except for carbon and sulphur which were analysed by a chemical method (Table 2).

For all materials, the microstructure was observed using an optical microscope, after polishing with a 0.05µm finishing suspension and etching with a 2% Nital (nitric acid -ethanol) solution.

The microstructures at the mid wall location are presented in Figure 2:  Steel "P" presents a typical ferritepearlite microstructure. The grain size is between 10 and 30µm and the microstructure is highly orientated in the rolling direction.  The microstructure of steel "R" is mainly composed of ferrite and pearlite, with traces of bainite. The pearlite content seems lower than in steel "P". The grain size is between 5 and 15µm. The microstructure is oriented in the rolling direction, with a segregation area in the mid-thickness.  The phases observed in steel "S" are ferrite and pearlite. The grain size is between 5 and 20µm. The microstructure is highly orientated in the rolling direction, with a segregation zone in the mid-thickness.  The major phases observed in steel "T" are ferrite and pearlite. The grain size is between 5 and 15µm. The microstructure is orientated in the rolling direction.  The major phases observed in steel "U" are ferrite and a small proportion pearlite. The grain size is between 5 and 20µm. The microstructure is highly orientated in the rolling direction, with a segregation area in the mid-thickness.

Table 3 compares the tensile properties of all steels in the direction longitudinal to rolling, measured according to NF EN10002-1 [START_REF]1 -Matériaux métalliques[END_REF] . The inclusion content was analysed according to ASTM E45 method A [START_REF]Standard Test Methods for Determining the Inclusion Content of Steel[END_REF] . The results are presented in Table 4.

EXPERIMENTAL METHODS

Test solution and exposure conditions

The test solutions were prepared in accordance with EFC16 [START_REF]EFC publication n°16 -Materials Requirements for Carbon and Low Alloy Steels for H 2 S-Containing Environments in Oil and Gas Production 8[END_REF] , using sodium acetate buffer, and the pH was monitored and adjusted, as required, during the experiments. The one exception to this was for tests where the standard NACE TM0284-2003 solution A was used (starting pH of 2.7). All specimens were of 6-sided exposure type, 100mm long and 20mm wide and standard glass vessels were used. All HIC tests were performed following the general NACE TM0284-2003 standard, including three replicates for each condition and a volume / surface ratio greater than 3mL/cm². The experimental parameters studied were pH (3.5, 4.5 and 5.5) and H 2 S partial pressure (3, 10, 35 and 100mbar), with a CO 2 balance up to the 1bar total test pressure. The impact of the exposure time was also evaluated: longer exposures were performed for the "low severity" condition (i.e. 4 weeks at 10mbar H 2 S and 3 months at 3mbar H 2 S), while the higher pH 2 S were tested for 96 hours and 2 weeks. The corresponding pH-pH 2 S regions of investigation are mapped onto the SSC regions map in Figure 3.

HIC specimen preparation

HIC specimens for 6-sided exposure were sampled following the NACE TM0284-2003 standard test method. The sample dimensions were 100mm in the longitudinal direction, 20mm

wide and the same thickness as the base material (0.5 to 1mm were removed from the inner and outer surfaces). All faces were machined to obtain a finish equivalent to that achieved using 320 grit SiC paper.

HIC evaluation by metallography and ultrasonic testing

Two methods were used for the determination of the extent of HIC. The first consisted of the standard NACE TM0284-2003 practice, with three equidistant cuts along the length and metallographic examination of the cut faces.

For the other method, ultrasonic inspection was applied using a 15MHz transducer (1/4" diameter). The system was calibrated according to an in-house technique. For each specimen, the total area of the defects was calculated from the UT scans, and the crack area ratio (CAR) was determined as the ratio between the area of the defects and the total area of the specimen in the short transverse plan.

Hydrogen concentration

The determination of diffusible hydrogen concentration in the steel specimens was performed using an in-house procedure based on a Japanese standard 8 : Immediately after removal of the specimen from the test solution, it was rinsed with tap water and wiped with paper to remove the non-adhering sulfur deposit, then immediately immersed in 45°C glycerol under a collector tube. The time gap between specimen sampling and immersion in glycerol was always less than 2 minutes. The diffusible hydrogen was then measured by the displacement of glycerol after one week immersion at 45°C. The experimental error associated with free hydrogen measurement is 0.1mL. The repeatability was evaluated by comparing three identical specimens. The standard deviation was always less than 20%. For the sake of comparison with literature data, the diffusible hydrogen content was expressed in ppm. The volume of hydrogen that was measured at 45°C was first corrected to standard temperature and pressure using the perfect gas law. The corresponding mass of hydrogen was then divided by the mass of the corresponding steel specimen to obtain the hydrogen content in mass ppm.

RESULTS AND DISCUSSION

Table 5 summarizes the diffusible hydrogen content measurements. CAR values from US tests and CLR values from metallographic cuts are reported in Table 6 and Table 7 respectively.

Comparisons between US method and metallographic examination

In order to further investigate the comparative results between both US examination and metallographic analysis, a correlation graph between CAR and CLR was generated covering all the experiments (Figure 4). Each point in the graph represents the CAR and CLR (average of three cross-sections) measured for one individual specimen.

It is clear from this graph that the correlation between CAR and CLR was very good. For the specific range of conditions/materials as well as US equipment/calibration used in this study, the following equation can be proposed:

CAR CLR   64 . 0
This good correlation is explained by the location of cracks in a narrow zone at, and close to, the center line. This is often the case for sweet service steels, where the segregation of inclusions is often limited to the center line area. However, in the case of steels with a distribution of cracks throughout the thickness of the plate, the top view projection of the cracks, as used for the CAR determination from UT scans, might not be sufficient .

Using this ratio, the upper CLR limit value of 15% proposed in ISO 15156-2 after 96 hours exposure in the NACE TM0284 solution A would correspond to a CAR upper limit of 23%. The comparison between CAR and CLR as selection criteria is further illustrated in Figure 5. The CLR and CAR values of all specimens are reported in bar graphs, from the highest to the lowest CLR. Using 15% CLR or 23% CAR as pass or fail criteria gives the same ranking, i.e. specimen n°1 to specimen n° 15 failed.

It can thus be considered that, for the purposes of this study, there were no major differences between the two techniques. Therefore, the remaining discussion in the present report is based only on CAR results.

Correlations between diffusible hydrogen content and HIC sensitivity

In order to check if there is a correlation between HIC extent and the amount of diffusible hydrogen in the material, a correlation graph between CAR and diffusible hydrogen was generated for all the experiments (Figure 6). The CAR value used for this graph was determined on the same specimen which was subjected to diffusible hydrogen content determination.

Though there is no clear and direct correlation between the CAR value and diffusible hydrogen content, it is still possible to distinguish three distinct regions:  Below 0.5ppm of diffusible hydrogen, where only one of the specimens suffered (slight) HIC,  Between 0.5 and 1ppm of diffusible hydrogen, where most of the specimens of steels R, S and T and U had CAR values below 10%. Only 1 specimen of the other steel (steel P) suffered a higher CAR value (CAR 29%) and, even then, only after a very long duration test (i.e. 3 months under 3mbar H 2 S),  If the CAR upper allowable limit value of 23% indicated earlier was used, all specimens of steels R, S, T and U with less than 1ppm hydrogen would pass. For steel P, only one specimen had a high HIC ratio (CAR 29%) together with a moderate hydrogen concentration (0.9ppm) and, even then, only after a long duration test with low H 2 S (3mbar / 3months),  Above 1ppm diffusible hydrogen, most of the specimens exhibited a high level of HIC, with CAR values above 20%. However, 3 specimens exhibited lower CAR, respectively 11, 3 and 0%, the last two corresponding to 1.1ppm diffusible hydrogen (i.e. borderline).

These results are in good agreement with previous tests using the same diffusible hydrogen content methodology. Revie et al. [START_REF] Revie | Hydrogen-Induced Cracking of Linepipe Steels .1. Threshold Hydrogen Concentration and pH[END_REF] studied the HIC resistance of eight steel grades exposed to buffered solutions from pH 1.1 to 5.9 saturated with H 2 S. They determined a threshold hydrogen concentration above which HIC occurred. For 4 steel grades out of 5, this threshold hydrogen concentration varied from 0.2 to 0.9mL/100g (standard pressure and temperature), which corresponds to 0.2 to 0.8ppm, in excellent agreement with the 1ppm limit proposed in this work.

Another study recently reported by Hara et Al. [START_REF] Hara | The condition of HIC occurrence of X65 linepipe in wet H 2 S environments[END_REF] on HIC evaluation of X65 linepipe steels gave similar CAR / hydrogen concentration trends. For most of the studied steels (8 types of X65 linepipe) the critical hydrogen concentration was between 0.6 and 2ppm.

Therefore it is possible to consider that the diffusible hydrogen content could be a good indicator of the risks of HIC. For conventional ferrite pearlite steels, the critical concentration below which HIC risks are moderate could be 0.8 to 1ppm of diffusible hydrogen. Above 1ppm diffusible hydrogen, the situation requires a more detailed investigation, since some cases of low HIC were found for hydrogen content greater than 1ppm (albeit for two cases this was only marginally above the 1ppm limit). As suggested by the results from the literature, it may be necessary to determine a hydrogen limit on a case by case basis for different steels to provide more accuracy.

Impact of pH 2 S and pH

The variation of H 2 S partial pressure was found to significantly affect the severity of HIC for all the materials tested (Figure 7). Indeed, decreasing the partial pressure of H 2 S from 100mbar to 10mbar drastically decreased the risks of HIC, even though the exposure time was increased from 2 weeks to 4 weeks at the same time. At 100mbar, all materials exhibited CAR values from 13% to 65% after 2 weeks exposure. At 10mbar H 2 S and after 4 weeks exposure, two materials did not suffer HIC, and the highest CAR was only 14%. Intermediate values were found for the tests under 35mbar H 2 S.

However, the exposure time also played a major role in the severity of HIC: for materials P and S, higher CAR values were found after 3 months at 3mbar H 2 S than after 1 month at 10mbar. This result is potentially important since 3 mbar H 2 S is an established limit below which the risk of SSC is often considered insignificant for all practical purposes (Region 0 in NACE MR0175/ISO 15156 part 2). For HIC, it seems that the situation is not straight-forward and this raises the question of what the optimum test duration is under 'mild' conditions for HIC laboratory tests, and their correlation to field exposure. The conclusion from the preliminary stages of the project that the hydrogen equilibrium concentration would be reached after 2 weeks exposure for all conditions 4 might be misleading in that sense. Alternatively, diffusible hydrogen concentration alone might not be a good indicator, or the analysis method might not be appropriate. Full scale tests with representative 1-sided exposure are needed to further investigate this point. HIC laboratory tests aimed at studying specifically the impact of exposure time for different pH -pH 2 S would also be helpful. At least two questions require evaluation:  i/ is there a correlation between pH -pH 2 S conditions and the incubation time for HIC start?  ii/ Once HIC has started, how long does it take to progress and reach an "equilibrium level", and is this equilibrium level linked with pH, pH 2 S, or both ?

The impact of test solution pH was also examined. Figure 8 presents the CAR value versus pH graph for all the experiments undertaken at 100mbar H 2 S. With the exception of steel P, which was the least resistant of the HIC tested materials and had high CAR values over the whole pH range, the steels had a tendency towards a decrease in CAR when the pH increased. However, contrary to the case for H 2 S partial pressure, there did not seem to be a clear pH threshold above which no HIC is expected in the pH range 2.7 to 5.5. Even though no tests were performed at pH levels above 5.5 during the present study, similar experiments were described in the literature [START_REF] Bosch | Fit-for-purpose HIC assessment of large diameter pipes for sour service application[END_REF] , and CAR values above 5% were still observed after 4 days testing at pH 6.2 under 1bar H 2 S, on a steel grade close to those used in the present work.

For the present tests the results for the NACE TM0284-2003 Solution A were always less severe than comparable results for EFC 16 solution at a constant pH of 3.5. This might be explained by the rapid pH increase when using the NACE TM0284-2003 solution A. As illustrated in Figure 9 for the tests under 100mbar, it took only 2 days to reach pH 3.5. Therefore, 2 weeks exposure in EFC 16 solution at a constant pH value of 3.5 was more severe than the corresponding 2 weeks test in NACE solution without pH adjustment.

pH -pH 2 S severity diagram

From these results HIC severity diagrams were created for each steel grade, in a similar manner to the SSC region plot in NACE MR0175/ISO 15156 Part 2 (Figure 10). The following convention was adopted in order to classify CAR values into different categories:  CAR < 5%: no or little HIC  5% ≤ CAR < 23% light HIC (corresponding to < 15% CLR)  23% ≤ CAR < 50% strong HIC  CAR ≥ 50% very strong HIC These values were chosen essentially to provide an average distribution of the results of the present study in the different categories. It is worth noting that with respect to the correlation between CAR and CLR that was indicated earlier, the 15% CLR acceptance criteria of ISO 15156 for the NACE TM0284-2003 Standard Test Method with Solution A would correspond to a 23% CAR. The SSC regions of ISO 15156-2 are also indicated on the graphs for comparison.

A preliminary attempt at defining HIC severity domains has been made using the results from this study. It is emphasised that the impact of exposure time is not well understood at this stage of the investigation such the severity domains should be treated as preliminary. The HIC severity domains that have been proposed are therefore based mainly on the results with comparable exposure times, i.e. 2 weeks and up to 1 month. The results obtained under 3 mbar and after 3 months immersion have not been included for the purpose of this discussion.

One of the more important conclusions concerns the impact of pH 2 S, which always had a greater influence on HIC than pH. This confirms previous findings [START_REF] Bosch | Fit-for-purpose HIC assessment of large diameter pipes for sour service application[END_REF] , but is also in contradiction with the well established theory that HIC is only linked with the hydrogen activity in the system, and therefore more closely linked with the test solution pH. Nevertheless, it seems reasonable to propose to incorporate a lower threshold for H 2 S partial pressure in a HIC severity diagram.

From the results of this work, a lower H 2 S partial pressure threshold could be chosen between 1mbar and 10mbar, but further work is required to establish whether such a threshold exists.

The proposal to incorporate a diagonal threshold line in a HIC diagram is also open to question. The SSC diagonal limit with a slope of 1 between (1mbar H 2 S; pH 3.5) and (1bar H 2 S; pH 6.5) does not seem to be well adapted to HIC. Indeed, for 3 out of 5 materials tested CAR values were greater than 20% when tested under 100mbar H 2 S and pH 5.5. However, if no diagonal threshold was used, the impact of pH would be completely disregarded. This option is considered excessive; though limited, it was observed that pH did have an impact on the severity of HIC. It is proposed that the diagonal threshold for HIC could link (3mbar H 2 S; pH 3.5) and (100mbar H 2 S; pH 6.5). However, during the present work no experiments above this diagonal line were performed such that more testing will be required, including an investigation of the impact of test duration, to better define this diagonal limit. Concerning the test duration, it should be remembered that steel "P" did not suffer HIC after 4 days exposure at pH 3.5 and 10mbar H 2 S whereas 4 weeks exposure in the same conditions resulted in 14% CAR. HIC in low severity conditions is therefore not "instantaneous", and several days or weeks might be necessary to initiate cracking. This could be related to the low hydrogen permeation rates observed at low pH 2 S [START_REF] Duval | Hydrogen permeation through ARMCO iron membranes in sour media[END_REF][START_REF] Kittel | New insights into hydrogen permeation in steels: measurements through thick membranes[END_REF] , such it takes more time to reach the critical hydrogen content for HIC.

As has already been indicated, further investigations are required to establish the principle and limits of the domains. For example:  Within this work, only few experiments were performed in region 1, as defined below (Figure 10).  A still wider range of steels will also have to be tested.  Additionally, full scale experiments will be necessary for a definition of acceptance criteria, especially in the case of US testing, and also to re-define the necessary exposure times of HIC tests. This is particularly true for the low H 2 S conditions.

Notwithstanding the need for further work, at this stage a preliminary HIC severity diagram could be proposed, with four distinct regions (Figure 11):  Region 0: non sour. No specific steels are required. The existence and value of the threshold H 2 S level still needs to be established but it is proposed that this could be somewhere between 1 and 10 mbar.  Region 1: mildly sour region. At the present stage of investigations, it might become necessary that longer term (e.g. 4 weeks) tests are required to demonstrate suitability of specific 'sweet service steels' in terms of HIC resistance in this region.  Region 2: transition region. It is recommended to perform HIC exposure tests for the qualification of 'sweet service' steels for use in these conditions.  Region 3: sour region. It is mandatory to use sour service materials.

CONCLUSIONS

Use of sweet service grade linepipe in mild sour service could provide potential economic benefits, but would require an assessment of the HIC performance under the intended service conditions. For this purpose, a test methodology developed for the assessment of the HIC resistance of linepipe steels for 'mild sour environments' 4 has been applied to five commercially available sweet service linepipe steels. Experiments consisted of exposure tests based on the NACE TM0284-2003 standard test method, but with different pH and H 2 S partial pressure levels. The impact of the 'time of exposure' was also examined. Characterisation methods comprised diffusible hydrogen content measurements and cracking evaluations, by both metallographic examination and ultrasonic testing.

These tests showed that:  A good correlation was found between diffusible hydrogen and the extent of HIC:

o For a diffusible hydrogen level of below 0.5 ppm no HIC was observed. o For a diffusible hydrogen level above 1ppm all samples exhibited high extents of HIC, with CAR values of between 21% and 75%. These values are in good agreement with previously published values and indicate that diffusible hydrogen may be a good indicator of HIC resistance. However, the threshold hydrogen content might be specific to each material.

 For the five steels used in this study, a good correlation was found between the CLR determined by metallographic examination and the CAR calculated from the ultrasonic scans. This good correlation is probably a consequence of the localisation of the cracks in the half thickness area for all tested steels. CSR and CTR correlations might be required for other types of steels.

 All steels exhibited moderate to high HIC after 2 weeks exposure tests to 35mbar or 100mbar partial pressure of H 2 S. For the tests at pH 3.5 and low H 2 S partial pressure, HIC was found for three steel grades after 1 month exposure under 10mbar H 2 S, and for three steel grades after 3 months exposure under 3mbar H 2 S.

 When comparing the results of two week exposure tests, H 2 S partial pressure had a far greater influence on the extent of HIC (and indeed the levels of diffusible hydrogen) than did the pH. Increasing the partial pressure of H 2 S and, to a lesser extent, decreasing the pH resulted in an increased risk/extent of HIC.

From these results a preliminary HIC severity diagram, synonymous to the SSC severity diagram in ISO 15156-2, has been proposed (Figure 11) with the following Regions:

 Region 0, non sour, where no special precautions are required.  Region 1, mildly sour, where longer term (e.g. 4 weeks) tests might be necessary to demonstrate suitability of 'sweet service' linepipe materials in terms of HIC resistance.  Region 2, transition, where HIC testing with a test period of two weeks might be sufficient to qualify 'sweet service' linepipe materials.  Region 3, sour, where sour service materials must be used.

It should be recognised that this HIC severity diagram has been generated by testing a limited number of steels (five to date) such that further testing is required on a range of 'sweet' materials to confirm the limits of the regions, and especially the boundary between regions 0/1 and 2/3.

A problem that has been identified during this study is that the requirement in NACE MR0175/ISO 15156 Part 2 for acceptance criteria of 'no cracking' for HIC testing under application specific environmental conditions would be extremely difficult to conform to, even under the most mild sour conditions, and hence requires further study. One possibility is to investigate this in a further Phase of the EPRG programme, with a view to generating acceptance criteria that could be applied to milder sour conditions frequently associated with 'application specific' test conditions. This would also enable further refinement of the HIC severity diagram.

The second problem that requires additional investigation is the impact of exposure time, which is not well understood at this stage. Steel P -CAR (%) < 5% 5% < < 23% 23% < < 50% > 50% pH pH 2 S (mbar)

Figure 1 :Figure 2 :Figure 4 :FailFigure 5 :Figure 6 :

 12456 Figure 1: SSC regions of environmental severity according to ISO 15156-2.

Figure 7 :

 7 Figure 7: Impact of H 2 S partial pressure on CAR for the experiments at pH 3.5.

Figure 8 :

 8 Figure 8: Impact of pH on CAR values for the 2 week exposure under 100mbar H 2 S (points at pH 2.7 corresponds to test in NACE solution, without pH adjustment).

Figure 9 :

 9 Figure 9: pH evolution during 2 weeks test in NACE TM0284-2003 Solution A under 100mbar H 2 S.

Figure 10 :

 10 Figure 10: Representation of CAR results in the pH-pH 2 S diagram. Exposure time was 2 weeks for all experiments, except at pH 3.5 -10mbar (4 weeks) and at pH 3.5 -3mbar (3 months). EFC 16 solution A was used for all tests, except for tests at pH 2.7, where NACE TM0284-2003 solution A was used.

Figure 11 :

 11 Figure 11: Proposal of HIC severity regions.

Table 1 :

 1 Origins of steels used for the study.

	Steel

designation Strength level / Process Coming from Thickness Made for type of pipe

	P	X65 TMCP*	LSAW pipe	25	LSAW
	R	X70 HSM**	Sheet (coil)	14.2	HSAW
	S	X60 HSM	HSAW pipe	14.2	HSAW
	T	X60 HSM	Sheet (coil)	8.7	HFI
	U	X70 HSM	Sheet (coil)	14.2	HFI
	*TMCP: thermo mechanical controlled heavy plate
	** HSM: thermo mechanical rolled hot strip

Table 2 :

 2 Comparison of chemical composition (wt %) for tested materials.

	Steel	C	Mn	Si	P	S	Cr	Ni	Mo	Cu	Nb	V
	P	0.09	1.56	0.28	0.014	0.001	0.05	0.03	0.01	0.02 0,040 0,050
	R	0.093	1.8	0.393 0.014	0.005	0.038 0.035 0.007 0.026 0.036 0.083
	S	0.098	1.4	0.238 0.017	0.006	0.046 0.046 0.009 0.077 0.025 0.055
	T	0.12	1.26	0.274	0.01	0.003	0.042 0.044 0.01 0.055 0.035 <0.005
	U	0.06	1.25	0.227 0.007	0.003	0.092 0.194 0.017 0.028 0.038 0.074

Table 3 :

 3 Comparison of mechanical and microstructural properties of tested materials.

	Steel	Rp 0.2 (MPa)	Rm (MPa)	A (%)	grain size (µm)
	P	523	649	24.1	10-30
	R	590	671	25.5	5-15
	S	480	597	31	5-20
	T	510	625	25	5-15
	U	548	616	28	5-20

Table 4 :

 4 Comparison of inclusion content of tested steels, according to ASTM E45 method A.

Table 5 :

 5 Diffusible hydrogen content measured by the glycerol method after each experiment and for each steel grade (mass ppm).

	pH 2 S	3mbar	10mbar	35mbar		100mbar
	pH	3.5**	3.5*	2.7	4.5	3.5	5.5	4.5	3.5	2.7
	P	0.9	1.0	0.9	1.1	1.5	2.1	1.2	1.7	1.3
	R	0.5	0.4	0.4	0.6	1.5	0.4	2.8	1.1	1.1
	S	1.3	0.9	0.8	1.1	1.3	1.9	4.1	3.4	1.7
	T	0.2	0.6	0.2	0.2	0.3	0.5	0.5	1.5	0.6
	U	0.4	0.6	0.5	0.9	0.9	0.8	0.7	0.8	2.2
	* 1 month test; ** 3 months test

Table 6 :

 6 CAR values determined from the US scans on all specimens after each experiment.

	pH 2 S	3mbar		10mbar		35mbar			100mbar
	pH	3.5	4.5		3.5	2.7 4.5 3.5		5.5	4.5	3.5	2.7
	duration	3m	2w 4d 1m 2w 2w	2w 4d 2w 4d 2w 4d 2w 2w
	P	25	0	0	14	0	32	75	21 66 49 56 52 63 48
	R	0			5	0	6	39		23	38	65 35
	S	18			1	0	3	23		38	43	48 39
	T	0			0	0	0	4		0	3	13	2
	U	1			0	0	10	7		3	21	15 15
	Duration: d=days; w=weeks; m=months
	Table 7: CLR values determined from three metallographic cuts (25-50-75mm) on all
				specimens after each experiment
	pH 2 S	3mbar		10mbar		35mbar			100mbar
	pH	3.5	4.5		3.5	2.7 4.5 3.5		5.5	4.5	3.5	2.7
	duration	3m	2w 4d 1m 2w 2w	2w 4d 2w 4d 2w 4d 2w 2w
	P	12	0	0	11	0	21	46	6	38 13 33 44 38 21
	R	0			1	0	2	33		11	22	35 26
	S	7			0	0	3	12		23	33	44 24
	T	0			0	0	0	0		0	0	9	1
	U	0			0	0	6	1		1	13	7	7
	Duration: d=days; w=weeks; m=months

The FFP terminology is used in accordance with the NACE MR0175/ISO 15156, where the following definition is given: "suitability for use under the expected service conditions".


Résumé

La planification énergétique peut être caractérisée comme un problème de décision d'investissement. Les investisseurs utilisent de nombreuses méthodes différentes pour traiter ces problèmes. L'une des méthodes la plus courante est basée sur la théorie du portefeuille de Markowitz par laquelle les investisseurs tentent de maîtriser les risques et de maximiser la performance du portefeuille en vertu de divers résultats économiques volatils. Ce travail explique les idées de la théorie du portefeuille et analyse leurs principales applications dans un pays producteur de pétrole et de gaz naturel. Nous allons illustrer comment le parc de production d'électricité en Iran peut être influencé par une addition supplémentaire de ressources nucléaires et renouvelables. En comparaison avec les mix électriques dominés par les ressources fossiles, des portefeuilles efficaces de production d'électricité peuvent réduire considérablement les coûts de production tout en incorporant une plus grande part d'énergies décarbonées dans le mix. Les résultats optimaux pour le mix électrique Iranien montrent que par rapport aux mix basés sur les ressources fossiles, il existe de nombreuses structures de production avec de plus grandes parts de non-fossiles (à la fois nucléaires et renouvelables) à des coûts et des risques égaux ou même inférieurs. D'ailleurs, si nous prenons également en considération les recettes d'exportation de combustibles fossiles libérés (coût d'opportunité des combustibles), cette conclusion devient encore plus évidente.

En outre, notre modèle d'analyse du portefeuille reflète l'interrelation des coûts (covariances) parmi les alternatives de production d'électricité et leur impact sur les coûts et les risques du portefeuille final. Les résultats montrent que le portefeuille typique de la génération d'électricité en Iran, basé sur des ressources fossiles, offre peu de diversification. Bien que cela puisse isoler le risque aléatoire, comme les enjeux entourant le développement de la filière nucléaire Iranienne, il fournit peu de couverture contre le risque systématique des mouvements du prix du pétrole et du gaz, qui historiquement ont été fortement corrélés.

Mots-clés: Portefeuille de la Production d'Electricité; Iran; Diversification Abstract Energy planning can be characterized as an investment-decision problem. Investors use many different methods for treating such problems. One of the most common methods is based on the Markowitz's portfolio theory by which investors try to manage risk and maximize their portfolio performance under variety of volatile economic outcomes. This work explains essential portfolio theory insights and analysis their application in an oil and gas producing country. We will illustrate how different electricity generation mixes can be influenced by additional share of nuclear and renewable sources. In comparison to the fossil dominated mixes, efficient power generation portfolios can dramatically reduce the generation costs while containing larger shares of decarbonized power units in the mix. The optimal results for the Iranian generation mix demonstrate that compared to the fossil-based mixes, there exist many generating mix structures with larger non-fossil shares (both nuclear and renewable) at equal or even lower expected costs and risks. Moreover, if we also take into consideration the export revenues of released fossil fuels (opportunity cost of fuels) this conclusion becomes even more affirmative.

Moreover, our portfolio model analysis reflects the cost inter-relationship (co-variances) among generating alternatives and their impact on the final portfolio costs and risks. The results illustrate that the typical Iranian gas and fuel generating portfolio offers little diversification. While it may insulate from random risk, such as Iranian nuclear issues, it provides little insulation from the systematic risk of oil and gas price movements, which have historically been highly correlated.

Keywords: Power Generation Portfolio; Iran; Diversification

Iran's Economic Outlook and Recent Political Development

Currently Iran's economy is going through an extremely difficult period. GDP contracted in 2012 for the first time since the early 1990s, under the weight of US sanctions, which are supported by many other major economies. While sanctions have long been in place, they have become more severe since 2010 in response to Iran's alleged nuclear program. The sanctions are targeting Iran's energy sector in particular, as hydrocarbon products accounted for almost 80% of Iranian exports and government revenues in 2010. Furthermore, sanctions have become more effective because they are not only targeting Iran directly but also countries or companies that trade with Iran. For example, the EU placed a ban in 2012 on insurance for tankers carrying Iranian crude. The result has been that oil exports have more than halved in the past few years, a blow to Iran's fiscal position.

The imposition of sanctions and the collapse in exports are causing knock-on effects that will have serious repercussions in both short and long terms. The value of Iran's currency, the Rial, has depreciated by over 80% since late 2011, causing a sharp increase in the price that Iran must pay for imported goods. This has added to inflation that was already high following the progressive removal of energy subsidies that began in December 2010. As a result, inflation hit 37.5% in July 2013. Aside from stoking social unrest, high inflation levels reduced consumer purchasing power, thereby reducing domestic consumption and contributing to the short-term economic slowdown.

The surprise election of Dr. Hassan Rouhani as Iran's next president signals a possible change in the country's external relations and economic policies from recent years. Rouhani's moderate tone resonated with the majority of voters who turned out in record numbers (72.2% of an estimated 50 million voters) to reject the election of more conservative candidates.

The new political leadership will focus on tackling the country's economic crisis after Iran has suffered under crippling sanctions over the past two years, which pushed the inflation rate to 36%, increased youth unemployment to 28% and more than halved the value of the Iranian Rial against the dollar since July 2010. In order to revive the economy, Rohani will have to restart bilateral talks with the US on nuclear issues to get international sanctions removed and to mitigate their permanent damage to the country's industrial growth.

Iran Oil and Gas Sector

Iran is the second largest oil producer in the Middle East and plans to significantly increase output through developing a number of oil and gas fields in the Persian Gulf and through enhancing the recovery rate with gas injection technology. This is a very ambitious plan given that external investment is constrained under US and EU sanctions, unattractive buy-back contract conditions, ageing assets and chronic under-investment in petroleum infrastructure.

The largest oil fields are Ahwaz, Gachsaran and Marun, which are located onshore and account for about one third of Iran's oil current production. Oil exploration, production, transportation and exports are managed and operated by various units of the National Iranian Oil Company (NIOC). The NIOC, through its affiliates, has a high degree of control over oil development projects. Buy-back contracts with Iranian oil companies must be signed by international oil companies to develop gas fields in Iran. This has tended to result in lengthy delays as Iranian companies struggle to find capital. To date, there have been twelve buy-back deals with foreign companies but only Asian NOCs remain in the existing field developments, given the exodus of IOCs from Iran. Oil and gas infrastructure of Iran is illustrated in figure 3-1.

Although Iran has the second largest gas reserves behind Russia, the country is expected to be challenged in exploiting these gas resources. Similar to the oil sector, external investment constraints will be the major impediment to the development of the gas sector. Iran's remaining commercial gas reserves are estimated to be 170 trillion cubic feet and are mainly found offshore in the giant South Pars gas field which is part of the same structure that Qatar has developed successfully (the Qatari side is called the North Field). Foreign companies must enter into buy-back contracts with NIOC (similar to the oil sector) to develop gas fields in Iran. A number of foreign companies had agreements for the development of South Pars including Phases 2&3 (Total, Petronas and Gazprom), Phases 4&5 (Agip and Petro Pars) and Phases 6-8 (Statoil and Petro Pars). Although South Pars phases 11, 13 and 14 were due to be developed, the IOC participants have since been removed from gas production projects.

Current natural gas production is dominated by four onshore non-associated gas fields (Khangiran, Kangan, Nar and the Parsian group) and the first five phases of South Pars. Unlike Qatar's success in developing its gas reserves, Iran has been unable to support its plans to expand supply capacity and to become a major gas exporter. While sales gas production has increased from 56 bcm in 2000 to 138 bcm in 2010, Iran now faces some choices how to invest in the gas sector to maintain or increase production capacity.

Although Iran is a large gas producer, the country will continue to be a net importer over the next years. Iran imports gas via pipelines from Turkmenistan to supply Iran's northeast, which has no direct pipeline connection with Iran's producing fields in the South. While some imported gas is also going to Tehran, the densely populated area around Teheran in the country's centre is mainly supplied via pipelines from the South. Iran currently exports gas only to Turkey via long-term pipeline contracts, which will expire in 2025.

Iran's Power Sector Overview

Gas is the dominant fuel for Iran's electricity generation as it provided 76% of total power generation in 2010, while oil and hydro supplied 20% and 4%, respectively (figure 3-2). Gasfired generation grew at 7% per annum over 2000-10, spurred by low gas prices (figure 3-3).

A severe drought caused hydro power generation to plummet in 2008-09 and electricity from hydro power to drop from 18 TWh in 2007 to 7 TWh in 2009. The shortage of hydro generation was offset by an increase in gas-fired power generation. Frequent power outages resulted from insufficient hydro power supply during the drought, which could not meet quickly rising power demand. Since then, a significant program of state investment has been implemented to boost installed power capacity in order to avoid power shortage in the future.

Hydro power generation started to rise again in 2011 as water levels returned to normal.

Iran's electricity generation capacity reached 65 GW in 2011 and the country's generation plants are owned and operated by the company TAVANIR. Hydro generation capacity is owned and operated by then Iran Water and Power Resources Development Company or by Independent Power Producers (IPPs). Electricity production from TAVANIR supplies around 90% of Iran's total electricity generation. Any power produced by IPPs must be sold to TAVANIR under long-term contracts of around 25 years. The government does intend to establish a competitive wholesale market and allow direct sales to large end-users, but we believe it is highly unlikely that this will happen soon given the country's poor track record for privatization efforts over a number of years. Iran exports electricity to Armenia, Pakistan, Turkey, Iraq, and Afghanistan and the country's net electricity exports have increased noticeably over the past years. Although Iran's exports could be negatively impacted by rising domestic power prices when subsidies will be further removed, we expects the trend of rising electricity exports to continue over the forecast period as long as the domestic electricity generation and transmission infrastructure in neighbouring countries (Iraq, Pakistan, Afghanistan) remains underdeveloped.

The History of the Electric Power Industry in Iran

The history of electricity dates back to 1885 when the first dynamo came into service in Iran.

This machine with the capacity of 3 KW was used to light the royal court of Tehran, the capital of the country. This occurred 3 years after the inauguration of the first commercial electric lighting entity by Edison in the New York City. 8 years later, a 12hp generator was installed in the Mashhad city (north east of Iran) by the private sector. Also at that time, the first license for establishing commercial electric lighting was granted (for lighting only during evening hours) and the first power plant inaugurated in 1906. Since that time, during around 40 years, electric power was considered as a luxury product used only for lighting with small number of consumers all around the country. The private sector became active in this business and supply facilities were installed by private institutions. After World War II, the

Wind Energy Situation in Iran

Persians were the first people to construct the first wind mills around 200BC. Some of those historic mills are still on operation in rural areas of Khorasan province in North East of Iran.

Iran is blessed with diversified and four season climate and besides having deserts; it is also a mountainous land with Caspian Sea on the North and Persian Golf & Oman Sea in the South.

Due to this geographical position, the country benefits from various tropical wind flows coming from Central Asia during winters and Indian Ocean during summer seasons.

Iran's first experience in installing and using modern wind turbines backs to 1994. Two sets of 500KW NORD-TANK turbines were installed in Manjil and Roodbar sites (Alamdari et al. Looking at its potential, it is essential that Iran should absolutely not fall behind in the development of this technology. Nevertheless, it is a far journey so as to fill up the technology gaps and to utilize the large wind power potentials.

Hydropower Development in Iran

Iran is classified as an arid and semi-arid country because of its long-term average precipitation of around 250mm, which is nearly one-quarter of the world's average rate.

Moreover, the precipitation is not evenly distributed all over the country. The total surface water is around 92bcm of which 27bcm flow into three major basins: Dez, Karkheh and

Karoon rivers basins all located in the South-West of Iran over the Zagros mountain chains where the major hydroelectric projects are located. Northern and Northwestern regions have relevant precipitation and topography for developing small medium-sized hydro plants.

Currently, there are 42 hydroelectric plants on operation in Iran, with total installed capacity of around 8GW and many others with total capacity of almost 7GW are also under construction. Large hydro plants with capacity of more than 100MW cover more than 90% of the installed capacity.

Volume of hydropower is highly variable in Iran and depends on yearly water falls. For example, in 2007 more than 18TWh of power was fed to national grids while this amount was decreased by 72% in 2008 due to unexpected droughts (Ministry of Power Annual report 2008).

There are many water streams in Iran which either go waste or finish at rivers and finally into the sea. Therefore, many small and mini hydro systems can easily be installed to provide locally needed power or to be injected to grids. Unfortunately, these huge potentials of hydro power are not effectively utilized and are even deprived of any further extensive planning.

Solar Energy Status in Iran

Iran enjoys approximately 2800 sunny hours per year as it is located on the world's Sun Belt.

Iran's average solar insulation rate is estimated to be around 2000 kwh/m². Figure 3-7 shows the average annual sum of this rate for different regions.

The first in Iran and the largest in Middle East, Shiraz solar power plant will come to full operation by the end of 2015 according to Iran's Renewable Energy Organization.

Methodology

Hereby, we describe essential ideas related to portfolio theory and discuss their application in the analysis of the Iranian electricity generation mix. We adopted this methodology for the Iranian case as the electricity sector in Iran is a qausi-competitive system and more privatized than other oil and gas producing countries such as Saudi Arabia. Besides, energy diversity is the main focus of late between Iranian energy authorities and portfolio-based models are very much adapted to treat energy diversity issues. Therefore stand-alone least cost approach does not necessarily provide the most optimal solution for the Iranian generation mix and we should adopt an optimization model based on both cost and risk minimization process.

By applying this approach we will illustrate how electric power generation mixes can benefit from additional shares of non-fossil generating units. In comparison to fossil dominated mixes, efficient portfolios could decrease the total generating cost while including greater non-fossil (nuclear and renewables) shares in the mix. This improves also energy security.

Though counter-intuitive, this conclusion is completely consistent with fundamental finance theory. As a matter of fact, under dynamic and uncertain environments, the relative value of producing technologies should be determined not by evaluating alternative resources but alternative resource-portfolios.

Mean Variance Portfolio Approach

Markowitz' mean variance portfolio theory is a probabilistic approach which could be used to value and optimize fuel mix diversity. This theory defines portfolio risk as total risk (including both random and systematic fluctuations) measured by the standard deviation of periodic historic returns. An efficient portfolio includes the smallest risk for a given level of expected return or vice-versa, the biggest expected return for a given level of risk. The process contains making an optimal portfolio generally by using historical measures of risk, returns (costs) and of course the correlation coefficients between various assets to be considered in the portfolio.

By numerical (computer aided) processing the risk (standard deviation), return or cost and correlation coefficients data, it would be possible to produce a number of portfolios for varying amounts of return having the least risk level from asset classes consisted. They are called efficient/optimal portfolios, which situate on the so called efficient frontier. Efficient frontier of two risky assets and the set of optimal portfolios are shown in figure 345678.

Then according to this efficient frontier, the investor simply needs to choose his desired level of risk. Actually, mean-variance portfolio theory suggests not a single efficient portfolio, but a range of optimal choices. Based on their risk aversion and preferences, investors will choose a risk-return combination.

Initially, mean-variance portfolio theory developed only for financial applications. But it can also be used for power generation assets to determine the efficient portfolio for a country or generation company, discussed in detail in [START_REF] Awerbuch | Energy Security and Diversity in the EU: A Mean-Variance Portfolio Approach[END_REF] and [START_REF] Roques | Fuel mix diversification incentives in liberalized electricity markets: A mean-variance portfolio theory approach[END_REF]. [START_REF] Awerbuch | Energy Security and Diversity in the EU: A Mean-Variance Portfolio Approach[END_REF] suggest that the relative value of producing assets should be determined not by evaluating alternative assets, but by evaluating alternative asset portfolios. Hence, energy planning entities need to focus less on stand-alone least cost alternative and more on building optimal power generating portfolios.

In 1976, Bar-Lev and Katz applied mean variance portfolio theory to fossil fuel supply for US electric utilities. By focusing on a regional approach, they constructed the theoretical efficient frontier of fossil fuel mix for various regulated utilities and compare it with the real experience of the power utilities. They found out that most of the utilities portfolios were situated on the efficient frontier but with very high level of risk and rate of return. They They note that American electric utilities have approached more efficient points of generation since the 1980s, and that the switch toward natural gas took place in the 1990s were driven by strong wish for higher returns to investment. Awerbuch (2000) analysis the US gas-coal generation mix and demonstrating that more wind, solar and other renewables with zero variable costs in the portfolio will lead to overall risk and cost reductions, even if their stand-alone costs might be higher. [START_REF] Awerbuch | Energy Security and Diversity in the EU: A Mean-Variance Portfolio Approach[END_REF] attempt to determine the optimal European technology mix, taking into account not only fuel price risk but also construction period risks and operation and maintenance risks. They found that EU-2010 mix is coupled with higher rate of risk and return compare to EU-2000 generation mix. More recent studies like [START_REF] Roques | Fuel mix diversification incentives in liberalized electricity markets: A mean-variance portfolio theory approach[END_REF] focus more on a private investor prospective.

They conclude that in the absence of long-term power purchase contracts in the UK efficient portfolios differ greatly from socially optimal ones. They found that there is a little motivation of diversification for private investors as there is a high correlation between electricity, gas and carbon prices. This kind of conclusion raises questions about how policy makers and regulators should adapt the market frame work to assure system diversity and security of supply.

Portfolio Theory Application in Power Generation Investment

Traditional power generation investment valuations approaches such as the famous levelized cost method, are mainly based on stand-alone analysis. But generation technologies have various risks and return patterns, as such that there are many valuable potential advantages in constructing a diversified portfolio of power plants. Mean variance portfolio theory applications provide more information for a country policy maker and utilities regarding many critical risks in liberalized and quasi-liberalized power systems.

Portfolio theory application is highly used by financial investors to construct high return and low-risk asset portfolios under different economic contexts. In one word, investors have learned that an optimal portfolio contains no unnecessary risk to its expected return-oninvestment. Portfolio theory could be very suitable for planning and evaluating electric power portfolios and strategies as the process is too similar to one used by financial investors seeking to maximize their profit under minimization of the variety of unpredictable risks. In a similar way, it is essential to conceive of power generation not in terms of the levelized cost of a specific technology today, but in terms of its portfolio cost. In other words, when we apply portfolio theory to power generation planning and strategy, fossil and non-fossil alternatives are evaluated not on the bases of their stand-alone costs but on the basis of portfolio cost which is their contribution to total portfolio producing cost relative to their contribution to total portfolio risk.

If we look at the example of two assets from social planner view point (Iranian government in our case), the generating cost would be the relevant measure. As a matter of fact generating cost ($/KWh) is the inverse of a return (KWh/$); that is, a return in terms of physical output per unit of monetary input.

In this case, expected portfolio cost is the weighted average of the individual expected generating costs for the two technologies:

E(Cp) = X 1 × E(C 1 ) + X 2 × E(C 2 )
Where X 1 and X 2 are fractional shares of the two technologies in the generating mix and E(C 1 ) and E(C 2 ) are respectively their expected levelized costs per KWh.

20

Expected portfolio risk, E(бp) is the expected year-to-year variation in generation cost. It is also a weighted average of the individual technology cost variances, as tempered by their covariance:

E(бp) = (X 1 2 б 1 2 + X 2 2 б 2 2 + 2X 1 X 2 ρ 12 б 1 б 2 ) 0.5
Where X 1 and X 2 are the fractional shares of the two technologies in the mix, б 1 and б 2 are the standard deviations of the holding period returns (HPRs) of the annual costs of technologies and ρ 12 is their correlation coefficient.

Portfolio risk is estimated as the standard deviation of HPRs of the future cost of generation defined as:

HPR = (V 2 -V 1 ) / V 1
In which V 2 is the ending value and V 1 is the starting value of the costs. In case of fuel cost for example, V 2 can be considered as the cost of fuel in year (t + 1) and V 1 as the cost in year (t).

In other words, HPR, measures the rate of change in the cost stream from one year to the next.

A detailed discussion is given in [START_REF] Awerbuch | Energy Security and Diversity in the EU: A Mean-Variance Portfolio Approach[END_REF].

The correlation ρ is an indicator of diversity in a sense that smaller ρ among portfolio components generates greater diversity, as measured by an absence of correlation between portfolio constituents. Adding a zero fuel cost technology to a risky generating mix, lowers expected portfolio cost at any level of risk, even if this technology costs more [START_REF] Awerbuch | Portfolio-based electricity generation planning: Policy implications for renewables and energy security. Mitigation and adaptation strategies for global change[END_REF].

A fixed cost technology (with zero fuel cost) has бi=0, or very near to zero. This will decrease considering бp since two of three items in the E(бp) equation decrease to zero. And it is clear that бp reduces as ρ ij falls below one. For example again in case of pure fuel-less, fixed-cost renewable technologies, fuel risk is zero and its correlation with fossil fuel costs is also zero.

Modelling Tool and Process

For the modelling purpose we have used OptQuest and Crystal Ball tools developed by Oracle Enterprise Performance Management System. Firstly we go through the Crystal Ball simulator structure which we use for our cost estimation and modelling purpose and then

OptQuest modelling tool will be developed in details so as to reveal the in-depth structure of our portfolio optimization model and of course the results based on already modelled costs structures.

Crystal Ball is a forecasting and risk analysis tool for decision making under uncertainty.

Through Monte Carlo simulation technique, Crystal Ball forecasts the entire range of results for a given situation. It also shows us confidence levels, so we can know the likelihood of any specific event taking place. For each uncertain variable in a simulation, we can define the possible values with a probability distribution. A simulation calculates numerous scenarios of a model by repeatedly picking values from the probability distribution for the uncertain variables and using those values for the cell. Distributions and associated scenario input values are called assumptions. After hundreds or thousands of trials, we can view sets of values, the statistics of the results (such as the mean forecast value), and the certainty of any particular value. Crystal Ball actually is a simulation model that prepares the ground for our optimization model defined in OptQuest.

Traditional search methods work well when finding local solutions around a given starting point with model data that are precisely known. These methods fail, however, when searching for global solutions to real world problems that contain significant amounts of uncertainty.

Recent developments in optimization have produced efficient search methods capable of finding optimal solutions to complex problems involving elements of uncertainty. OptQuest incorporates meta-heuristics to guide its search algorithm toward better solutions. This approach uses a form of adaptive memory to remember which solutions worked well before and recombines them into new, better solutions. objective, but over time this process provides a highly efficient trajectory to the best solutions.

As shown in the following flow chart, the search process continues until it reaches some termination criteria, either a limit on the amount of time devoted to the search or a maximum number of simulations.

control variables

Monte-carlo

Iranian Power Generation Mix Model

As already mentioned at the beginning of this chapter both renewable and fossil fuel power plants are to be considered for the national electricity generation of Iran. Hence, coal, fuel-oil and natural gas power plants introduced to our model as fossil-based power units. In addition, nuclear power units and renewables (hydro, wind and solar) were also added to the generation mix. Geothermal units were not considered in our modelling because of the non-existence of any influential policy in the ministry agenda.

Therefore the total expected portfolio cost of the Iranian mix is given by:

E(C IranP ) = X oil E(C oil ) + X gas E(C gas ) + X coal E(C coal ) + X nuc E(C nuc ) + X hydro E(C hydro ) + X solar E(C solar ) + X wind E(C wind )
And the total expected standard deviation (risk) of the portfolio is: In which X i and C i are respectively the shares and costs of Iranian power generation technologies. The standard deviation associated with each technology is denoted by б i and ρ i illustrates the correlation coefficients between various fuels used in related power units. For instance the correlations between fossil fuel prices, calculated based on the last decade monthly-averaged price of fossil commodities, are shown in the below figure.

E(б IranP ) = [ X oil 2 б oil 2 + X gas 2 б gas 2 + X coal 2 б coal 2 + X nuc 2 б nuc 2 + X hydro 2 б hydro 2 +
In case of nuclear fuel, we considered the annual average price of natural uranium over the last fifteen years and its correlation with other fossil resources prices. This resulted in a rather high correlation coefficient between coal and nuclear at around 0.4 while natural gas and oil have respectively 0.2 and 0.1 correlation coefficients with nuclear fuels (table 3-1B).

Consequently, the total generating portfolio cost has been constructed based on the weighted average cost distribution of each technology. Costs were defined with normal distributions and their associated means and estimated standard deviations. Details of standard deviation (risk) for each technology for its construction period, fuel cost and O&M costs and correlation coefficients between various technologies are summarized in tables 3-1A and 3-1B. As you can see in the table the standard deviation for fuel costs are all equal to zero for renewable technologies. As there is no requirement for any sort of fossil fuel. Construction period risks vary by unit type and are mainly related to complexity and length of construction period 1 . Fixed cost implies an annual obligation that will be undertaken by an investor as long as sufficient income exists, which make this risk somehow similar to the risk of payments on the company's debt.

As explained previously, the correlation coefficient ρ is an indicator of diversity. Lower correlation among portfolio components creates greater diversity, which serves to reduce 1 Nuclear construction period and its related standard deviation is based on the normal situation and contractual relations, even if it was not really the case of the first nuclear power plant (Booshehr) constructed in Iran. The construction period of Booshehr plant took almost 30 years due to political reasons. [START_REF] Awerbuch | Portfolio-Based Electricity Generation Planning: Policy Implications for Renewables and Energy Security[END_REF]). In the case of fuel-less renewables, fuel risk is equal to zero and its correlation with fossil fuel costs is also taken as zero.

For each power unit, risk is equal to the year-to-year standard deviation of the holding period returns for main generating cost: capital or construction period risk, fixed and fuel. Fossil fuel standard deviations are estimated from historical data already explained in previous chapters.

The portfolio analysis focuses on the risk of generating costs only. We ignore year-to-year fluctuations in electricity output from wind (or solar) plants, taking the approach that a properly managed wind resource can produce constant annual output.

Future fossil fuel costs and other generating outlays are random statistical variables. While their historic averages and standard deviations are known, they move unpredictably over time.

No one knows for sure what the price of gas will be next month, just like nobody knows what the stock markets will do in finance theory. Estimating the generating cost of a particular portfolio presents the same problems as estimating the expected return to a financial portfolio.

It involves estimating cost from the perspective of its market risk.

Current approaches for evaluating and planning national energy mixes consistently bias in favour of risky fossil alternatives. Whereas by understating the true value of wind, solar, and similar fixed-cost, low-risk, passive, capital-intensive technologies. The evidence indicates that such technologies offer a unique cost-risk menu along with other valuable attributes that traditional valuation models cannot [START_REF] Awerbuch | Market-Based IRP: It's Easy![END_REF]. The evidence further suggests that fixed-cost renewables cost-effectively hedge the fossil price risk as compared to standard financial hedging mechanisms (Bolinger et al. 2004).

The total cost of the portfolio is the sum of all the levelized costs distribution (specific for each technology). Crystal Ball simulation tool, generates the total cost of the portfolio as showed in the below figures.

As a matter of fact, the cost of each power generation unit is given to the model under the normal distribution assumption of the cost distribution with associated mean and variance.

Afterwards, we generate the total cost of the whole portfolio under Monte-Carlo process based on the percentage share of each unit in the portfolio. Details of this total cost modelling process are illustrated in figure 3-12.

29

Figure 3-12

Cost distribution of each power generation unit

Total generation cost of the portfolio 30 Feasible intervals for technologies' share are also introduced in the model. Intervals are defined according to the techno-economic feasibilities for each power unit. For example, the upper bound of hydroelectricity cannot go beyond 20% due to its saturation level in Iran.

Technologies' share-bounds of all power units used in the model are given in table 3-2.

Model's Result and Optimal Portfolios

After running the model for around 10,000 trials (iterations), we obtain the following efficient frontier (figures 3-13 and 3-14) for the various generation mix portfolios for the Iranian mix.

31

Each point on the efficient frontier represents an optimal generation mix scenario based on its related generation total cost and risks. Results show us that the least risky portfolio of power generation has the total cost of electricity generation equals to 26.35 USD/MWh. Least risky portfolio has the largest possible share of hydroelectricity and solar power units while fossil fuel technologies have the least possible shares. In this case, nuclear power share stays at 11%. On the contrary, in the least cost portfolio, at around 13.8 USD/MWh with two times riskier portfolio, nuclear and gas power plants shares are respectively equal to 35 and 36 per cents. Renewable resources, both wind and solar, are at their minimum levels. A comparative analysis of these two max and min costs portfolios, illustrates the impact of the non-fossil power units integration into the national generation mix. The more we decarbonize the mix (via renewables and of course up to the upper bound limit), the less risky portfolio we have.

However, strong penetration of non-fossil power units in the system can increase significantly the total cost of the power portfolio. Central planner can choose among all the possible portfolios on the efficient frontier according to its risk aversion.

In the second step we place the current portfolio of the Iranian power generation mix (data available in figures 3-2 and 3-3) on the cost-risk graph (figure 3-13). This action implies an increase of non-fossil power units share up to 30% in the generation mix (with at least 11% of electricity coming from nuclear). Fuel oil power plants share must be reduced up to 40%, half of their current share. And in case of coal power units, the situation is less dramatic as the model suggests even a small increase of its share up to 1% of the total mix which can be explained by the tendency of the model to raise the diversity of the portfolio and consequently reducing the total risk. We also performed a sensitivity analysis over the fossil fuel price used in the energy system model. We did it for 30% of oil price variation in the international markets compare to our initial input. This will obviously impact the variable costs of power generation in our fossil based power units. As already analysed in the earlier sections of this paper, oil price variation will also influence the price of other fossil commodities. This in not only the case of natural gas price, which is mainly indexed on oil price, but also that of coal and natural uranium whose prices are in correlation with the oil price.

Fuel price sensitivity analysis was done for the whole portfolio and for every new price assumption. We run the simulation and optimization model so as to generate the new efficient frontiers of our power generation portfolio. For the same reasons explained above, we are mainly interested in the median case. Structures of each optimal electricity generation portfolio are given in table 3-4 under various oil price variation assumptions. The results show that the increase of oil price can highly promote the non-fossilisation of our power generation portfolio. For instance, in case of 30% increase in the oil price the shares of natural gas and fuel power units in the system can decrease respectively by 16 and 10 per cents. Nuclear and solar power units would be the most optimal and cost-risk efficient production means. Nevertheless, the model still recommends 2% of fuel power units as these units can provide energy diversity and reliable back-up power for intermittent renewables in the system. On the contrary in case of oil price decrease, the model suggest significant amount of gas and fuel power stations. But still the share of the natural gas in the system is less than that of the current portfolio (63% of gas) which is situated very far from the efficient frontier. This also the case for the power units run by fuel. Finally in this case, we can observe a dramatically decrease of the nuclear and renewable shares. Still their shares are not equal to zero as the model has always the tendency to bring energy diversity and security in the system. To conclude, we can say that under the oil price decrease assumption, the optimal mix would be pushed toward more fossil-based structures consisting highly inter-connected fossil fuels (oil, natural gas and even to some extent coal).

Moreover, higher oil prices in the international markets (and consequently higher natural gas prices) brings more export opportunity for Iran and vice versa. This can also accelerate the impact of oil price variations on the electricity mix structure of Iran. However, we must add that the natural gas export has recently become a very strategic matter due to its geopolitical and technological perspectives. And Iranian energy authorities have always announced keeping this issue as a priority whatever the opportunity cost of natural gas monetization would be elsewhere in other domestic usages, except as feed for petrochemical units providing products also ready for export.

Lastly, we also integrated the CO 2 costs of 10, 15 and 20 €/MWH in the model. The results for each CO 2 cost integration compare to the median optimal scenario without carbon price are given in table 3-5. The results show that CO 2 cost reacts and influences the power system in the opposite direction of oil price variation. Except for the case of coal which is not recommended neither in case of oil price increase nor CO 2 high costs. We can conclude that a proper CO 2 -cost integration in the system cannot only provide environmental benefits but also dampen the vulnerability of the electricity mix against oil price fluctuations. Yet, there is not still any solid CO 2 reduction policy concerning the power generation sector and for the time being the main focus of Iran in terms of environmental issues is rather on the transport sector than others.

Conclusion

Today's dynamic and uncertain energy environment of Iran needs planning procedures that accommodate risks and de-emphasize stand-alone electricity generating costs. Procedures that can reflect the cost inter-relationship among various generating alternatives. In this work we attempted to construct the efficient portfolio of national power generation for the Iranian electricity sector. This was done under Mean Variance Portfolio (MVP) approach of Markowitz theory, fully explained throughout the text.

Mean-variance portfolio theory that we applied in our analysis is well tested and ideally suited to evaluating national electricity strategies. The MVP framework offers solutions that enhance energy diversity and security and are therefore considerably more robust than arbitrarily mixing technology alternatives. MVP illustrates that the typical Iran gas and fuel generating portfolio offers little diversification. While it may insulate from random risk, such as Iranian nuclear issues, it provides little insulation from the systematic risk of oil and gas price movements, which have historically been highly correlated2 and can dramatically impact the export revenue of the country and opportunity cost of electricity generation.

Given the high degree of uncertainty, the relative value of generating technologies must be determined not by evaluating alternative resources, but by evaluating alternative resource portfolios. Energy analysts and policy makers in oil producing countries face a future that is technologically, institutionally and politically complex and uncertain. In this environment, MVP techniques help establish renewables targets and portfolio standards that make economic and policy sense (Jansen, 2004). They also provide the analytic basis policy-makers need to devise efficient generating mixes that maximize not only the national revenue but also the system security and sustainability. MVP analysis shows that contrary to widespread belief, attaining these objectives need not increase cost. In the case of the Iranian national power generation mix, increasing the non-fossil share, even if it is believed to cost more on a standalone basis, reduces portfolio cost-risk and enhances very high energy security. The results revealed that the current Iranian generation mix is far from the optimality in terms of cost and diversity. In fact, according to our model's outputs, there is a huge potential of improvement in costs and risks reductions (respectively 15 and 10 per cent) by going toward more nonfossil fuel based portfolios of power generation.

However, any sort of aggressive strategy concerning both cost and risk reduction process, is not recommended as they are negatively correlated to each other. Massive investment in nuclear and other non-fossil resources would highly increase the portfolio's costs and can make the Iranian power sector very vulnerable against technological risks even if the impact on the energy security risk reduction could be very significant. Besides, relying on the current investment trend in the fossil power units can harm the Iranian power sector seriously by increasing the total risk of electricity generation portfolio. Moreover, this will also lead to substantial reduction of hydrocarbon export, as the domestic demand of oil and gas for power generation will continue to rise.

A compromise between fossil and non-fossil sources of power generation would be the most efficient solution for Iran. In the short and medium term Iran should continue to invest in both types of power units while gradually decrease the share of fossil units in the generation mix until reaching the optimal values. Both nuclear and renewable (wind and solar) power plants should gradually become more and more present in the national electricity portfolio of the country. However, this should happen under the condition that the nuclear power units' costs per MWh become at the normal and internationally acceptable rates. If the nuclear costs continue to stay at the same levels as of the first Iranian nuclear power plant in Booshehr (30 years of construction time and tripled investment costs), this conclusion would be totally irrelevant and inconsiderable from economic point of view. But under any circumstances,

Figure

  Figure 3-1 (Source: Fanack 2012)

  Figure 3-2: Electricity production composition in Iran in 2010 (Source: IEA & BP Statistics)

Figure 3

 3 Figure 3-5: Electricity demand evolution from 2000 to 2010 (Source: IEA & BP Statistics)

  are major wind sites of Iran with installed capacity of 94MW (Mousavi et al. 2011). Almost all of the wind plants in the country are sate owned and private investors have not yet been involved in this technology. High investment costs, financing problems, lack of long-term governmental support and of course the low prices of electricity, due to heavily subsidized natural gas, are the most important barriers in front of private interventions. Evolution of wind capacity in Iran since 1997 is shown in figure 3-6.

Figure 3 - 6 :

 36 Figure 3-6: Iran wind installed power plants 1997-2010 (Source: Iran Renewable Energy Organization SUNA)

Figure 3

 3 Figure 3-7: Iran solar energy map (Source: Solar GIS)

Figure

  Figure 3-8

  Jansen et al. (2006) use portfolio approach for analysing the electricity generation mix of Netherlands. Their study concentrates on fuel price uncertainty and is based on generation costs. They conclude that more diverse production portfolios are generally associated with lower risks for the same amount of returns. Especially those which contain more fixed-cost renewables and nuclear which have a low covariance with the fossil-fuel technologies' costs.

Figure

  Figure 3-9

Figure

  Figure 3-13

Figure

  Figure 3-14

Trajectory 1 ,

 1 is the most risk-averse way of optimizing the Iranian portfolio while there are plenty other existing trade-offs among various risk and cost values. Trajectories 2 and 3 are other examples (figure3-16). If the planner follows path 3, it will lead to the least cost scenario in which the share of nuclear energy should reach the maximum upper limit and the natural gas units take over just after. What we recommend as the most economic rational

  2X oil X coal ρ oil,coal б oil б oal + 2X oil X gas ρ oil,gas б oil б gas + 2X oil X nuc ρ oil,nuc б oil б nuc + 2X oil X hydro ρ oil,hydro б oil б hydro + 2X oil X solar ρ oil,solar б oil б solar + 2X oil X wind ρ oil,wind б oil б wind + 2X gas X coal ρ coal,gas б gas б coal + 2X gas X nuc ρ nuc,gas б nuc б gas + 2X gas X hydro ρ hydro,gas б gas б hydro + 2X gas X solar ρ solar,gas б gas б solar + 2X gas X wind ρ wind,gas б gas б wind + 2X nuc X coal ρ coal,nuc б nuc б coal + 2X hydro X coal ρ coal,hydro б coal б hydro + 2X solar X coal ρ coal,solar б coal б solar + 2X wind X coal ρ wind,coal б wind б coal + 2X nuc X hydro ρ nuc,hydro б hydro б nuc + 2X nuc X solar ρ nuc,solar б nuc б solar + 2X nuc X wind ρ nuc,wind б nuc б wind 2X hydro X solar ρ hydro,solar б hydro б solar + 2X hydro X wind ρ hydro,wind б hydro б wind + 2X soalr X wind ρ solar,wind б solar б wind ] 0.5

	X solar 2 б solar 2 +
	X wind 2 б wind 2 +

Correlation analysis of fossil commodities used in the model

				Brent, Dubai and WTI Average, $/toe Technology Risk Estimates / Standard Deviation Natural gas, Europe, $/toe, current
	Nuclear 1200 Coal	Natural gas, US, $/toe, current Construction Period Fuel a Variable O&M Fixed O&M Coal, Australia, $/toe, current 0,2 0,15 0,2 0,08 0,18 0,05 0,2 0,08
	1000 Oil			0,1		0,3	0,2				0,08
	800 Gas			0,15		0,3	0,2				0,08
	Wind 600 Solar			0,05 0,09		0 0	0,2 0,2				0,08 0,08
	400 Hydro			0,2		0	0,2				0,08
	200	a. Estimation based on empirical data 2005-2012
	0
		1970M01	1971M05 Correlations Coefficients between Technologies 1972M09 1974M01 1975M05 1976M09 1978M01 1979M05 1980M09 1982M01 1983M05 1984M09 1986M01 1987M05 1988M09 1990M01 1991M05 1992M09 1994M01 1995M05	1996M09	1998M01	1999M05	2000M09	2002M01	2003M05	2004M09	2006M01	2007M05	2008M09	2010M01	2011M05	2012M09
				Gas Coal	Nuclear	Oil	Renewable
		Gas		1	0,5	0,2	0,8		0
	Brent, Dubai and WTI Average, $/toe			1
	Natural gas, Europe, $/toe, current Natural gas, US, $/toe, current Coal	0,5	1	0,942016019 0,740124098 0,4	0,5		1 0,675343067 0					1
	Coal, Australia, $/toe, current Nuclear	0,2	0,4	0,572281856 1	0,1		0,591320862 0					0,462094974	1
		Oil		0,8	0,5	0,2	1		0
	Renewable	0	0	0	Figure 3-11 0	1

Brent, Dubai and WTI Average, $/toe Natural gas, Europe, $/toe, current Natural gas, US, $/toe, current Coal, Australia, $/toe, current

Table 3 -

 3 1A

Table 3 -

 3 1B Source: TAVANIR, Awerbuch et al. (2010) & Author's estimations portfolio risk. In general, portfolio risk falls with increasing diversity, as measured by an absence of correlation between portfolio elements. Adding a fixed-cost technology to a risky generating mix serves to lower expected portfolio cost at any level of risk, even if the fixedcost technology costs more

Table 3 - 2

 32

	Objective function
	Constraints & requirements
	Decision variables

Table 3

 3

	-4

Power Generation Units No CO 2 CO 2 at 10 €/MWh CO 2 at 15 €/MWh CO 2 at 20 €/MWh Coal

		2%	1%	0.7%	0.2%
	Gas	33%	26%	21%	17%
	Hydro	19%	19.2%	19.5%	20%
	Nuclear	19%	26%	31%	34%
	Oil	10%	6%	4.5%	2%
	Solar	12%	15%	16.3%	18%
	Wind	5%	6.8%	7%	8.8%

Table 3 - 5

 35

  Analyse des tendances et des ruptures sur le marché automobile français. Modélisation du taux de diésélisation dans le parc Décembre 2010.

			# 69.	E. HACHE
	Les majors pétroliers vers le multi énergies : mythe ou	Commodities Markets: New paradigm or new fashion?
	réalité ?		Juillet 2008
	Juin 2003
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	# 55.	P.R. BAUQUIS	Investment project valuation: A new equity perspective
	What energy sources will power transport in the 21st	Février 2009
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	Janvier 2004	# 71.	O. MASSOL, S. TCHUNG-MING
			Stratégies coopératives dans l'industrie du GNL :
	# 56.	A. PIERRU, D. BABUSIAUX	l'argument de la rationalisation est-il fondé ?
	Evaluation de projets d'investissement par une firme	Février 2009
	multinationale : généralisation du concept de coût
	moyen pondéré du capital et conséquences sur la	# 72.	A. PIERRU, D.BABUSIAUX
	valeur de la firme.	Valuation of investment projects by an international oil
	Février 2004	company: A new proof of a straightforward, rigorous
			method
	# 57.	N. BRET-ROUZAUT, M. THOM	Février 2009
	Technology Strategy in the Upstream Petroleum Supply
	Chain.		# 73.	E. SENTENAC CHEMIN
	Mars 2005	Is the price effect on fuel consumption symmetric?
			Some evidence from an empirical study
	# 58.	A. PIERRU	Avril 2009
	Allocating the CO2 emissions of an oil refinery with
	Aumann-Shapley prices.	# 74.	E. HACHE
	June 2005	OBAMA : Vers un green New Deal énergétique ?
			Septembre 2009
	# 59.	F. LESCAROUX
	The Economic Consequences of Rising Oil Prices.	# 75.	O. MASSOL
	Mai 2006	Cost function for the natural gas transmission industry:
			further considerations
	# 60.	F. LESCAROUX, O. RECH	Septembre 2009
			# 76.	F. LANTZ, E. SENTENAC CHEMIN
	Transatlantic Natural Gas Price and Oil Price
	Relationships -An Empirical Analysis.	# 77.	B. CHÈZE, P. GASTINEAU, J. CHEVALLIER
	Septembre 2006	Forecasting air traffic and corresponding Jet-Fuel
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	# 62.	E. HACHE	Décembre 2010.
	Une analyse de la stratégie des compagnies
	pétrolières internationales entre 1999 et 2004.	# 78.	V. BREMOND, E. HACHE, V. MIGNON
	Juillet 2006	Does OPEC still exist as a cartel? An empirical
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	# 63.	F. BERNARD, A. PRIEUR	Mars 2011.
	Biofuel market and carbon modeling to evaluate
	French biofuel policy.	# 79.	I. ABADA, O. MASSOL
	Octobre 2006	Security of supply and retail competition in the
			European gas market. Some model-based insights.
	# 64.	E. HACHE	Mars 2011.
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	# 65.	A. PIERRU	Avril 2011.
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			Construction of a fuel demand function portraying
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	# 67.	F. LESCAROUX	The bioenergies development: the role of biofuels and
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L'origine des disparités de demande de carburant dans l'espace et le temps : l'effet de la saturation de l'équipement en automobiles sur l'élasticité revenu. Juin 2006 # 61.

C. I. VASQUEZ JOSSE, A. NEUMANN E. LE CADRE, F. LANTZ, A. FARNOOSH

Bioenergies usages in electricity generation utility means through a modelling approach: application to the French case. Décembre 2011

Increasing use of contracts may mitigate this historical relationship by pricing each fuel more on the basis of its costs. However, history suggests that when shortages for a particular fuel occur, the cost of alternative fossil fuels rises. This is also the case in hydrocarbon producing countries.

An OptQuest optimization model has four major elements: an objective, optional requirements, decision variables (already simulated by Crystal Ball) and optional constraints.

Optimization Objectives: Elements that represents the target goal of the optimization, such as maximizing profit or minimizing cost, based on a forecast and related decision variables.

Requirements: Optional restrictions placed on forecast statistics. All requirements must be satisfied before a solution can be considered feasible. Decision Variables: Variables over which you have control; for example, the amount of product to make, the number of dollars to allocate among different investments, or which projects to select from among a limited set.

Constraints: Optional restrictions placed on decision variable values. For example, a constraint might ensure that the total amount of money allocated among various investments cannot exceed a specified amount, or at most one project from a certain group can be selected.

The whole stochastic simulation-optimization model to be constructed by Crystal Ball and OptQuest tools would be summarized in the following figure: Summary of the power generation mix structure of three above-mentioned portfolio possibilities and the current one, are given in table 3-3.

Power Generation Units Current Portfolio

Least-Cost Scenario Median Case Least-Risk Scenario investment in solar power must become the priority of the Iranian authorities as Iran with its vast desert areas can benefit from very smooth and reliable solar firms.

Last but not the least, it should not be disremembered that this gradual non-fossilization of the Iran's generation portfolio must be fulfilled in parallel with a solid and efficient policy regarding the decrease and eventually total removal of fossil fuels subsidies.

The "Cahiers de l'Économie" Series

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All these papers are available upon request at IFP School. All the papers issued after 2004 can be downloaded at: www.ifpen.fr

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