Renewable Energy Market Competence Index and Its Application to CSP Technology

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1 Renewable Energy Market Competence Index and Its Application to CSP Technology Hatem Elrefaei Associate Professor, Department of Engineering Physics, Faculty of Engineering, Ain Shams U 1 Elsarayat St., Abbassia, Cairo, Egypt. elrefaei.hatem@gmail.com, hatem@mcit.gov.eg Amel Bida Senior Expert in Regional Center for Renewable Energy and Energy Efficiency (RCREEE) Hydro Power Plants Execution Authority Ministry of & Energy (7th floor) Block 11 - Piece 15, Melsa District - Ard El Golf Cairo, Egypt. amel.bida@rcreee.org, amelbida@yahoo.fr Mohab Hallouda Professor, Electrical Power and Machines Department, Faculty of Engineering, Cairo U University St., University Square, Giza, Egypt. hallouda@yahoo.com Mohamed Elsobky Professor, Electrical Power and Machines Department, Faculty of Engineering, Cairo U University St., University Square, Giza, Egypt. sobki54-2@hotmail.com 1

2 Abstract A methodology for quantitative and objective Renewable Energy Market Competence Index is developed. The proposed index includes 18 different quantitative indicators; among them 13 are general indicators covering the political, economical situation of the country, and the energy sector, and 5 are technology specific that change from a renewable energy technology to another. In a previous work published in 2012, CSP Market Competence Index was evaluated for the period , where the data were collected and countries scores and ranks were calculated for 18 different countries (8 Arab countries in addition to 10 other countries covering different parts of the world). In this work, we are updating the CSP Market Competence index for the period , for the same group of countries except of Syria. 1. Introduction With the world energy crisis intensifying year after year, more governments, investors, and research institutions are exerting extensive efforts to exploit the available potential of Renewable Energy (RE) resources. Different governments implement different incentive policies to attract investors, and investors prefer to work in markets that are competitive, larger, and open, and want to be sure they will be able to sell the generated energy by different RE technologies. One important method that can quickly differentiate the strength of different countries regarding the competence of the RE market is through developing a numerical index that combines the many parameters that affect the RE market in the country. In previous work, [1][2][3], we developed a Renewable Energy Market Competence Index and implemented it for CSP technology, where the aim was to develop an index that is completely objective and doesn t depend on reviewer or expert opinion about the RE market of the countries under study. The work that was published in 2012 reflected the situation of CSP market competence during the period from 2008 till 2011 in 18 countries (8 Arab countries and 10 other countries). In the work presented here, we update the index, countries scores, and ranking for the period from 2009 till This paper is organized as follows; section 2 gives a summary of the Renewable Energy Market Competence Index along with the formulae used, where the detailed description of the 2

3 index was previously detailed in [1][2][3]. Section 3 discusses the variations of this year (2013) version of the index from that of 2012 issue. Section 4 illustrates the countries scores and ranks for 2013, along with a comparison between 2012 and 2013 results. This is followed by the conclusion in section5. 2. The RE Market Competence Index structure and formulae A country s market competence towards RE projects involves many factors. Some of these factors are common to all types of RE technologies and are related to the political, economic, and financial situation of the country, and also related to the characteristics of the country s energy sector itself. On the other hand, each RE technology type has certain specific factors that also determine the competence of the market (e.g. one of these specific factors is the existence of an economic potential to be utilized by this specific technology). Thus, the proposed index is made of a number of indicators that are categorized into two groups, a General s group and technology specific indicators group as shown in Figure (1), where the General s are divided into three subgroups: 1. Political and Economic (3 indicators). 2. Energy Sector (8 indicators). 3. Financial and Environmental s (2 indicators). The technology specific indicators group is composed of 5 indicators, resulting in a total of 18 indicators. For different RE technology types (e.g. CSP, PV, wind one shore, wind off shore, biomass, geothermal), only the Technology Specific s will be changed where in this work we are going to consider CSP technology only. RE Market Competence Index General s Technology Specific s (5) Political and Economic s (3) Energy Sector s (8) Financial and Environmental s (2) Figure (1) The hierarchy of the Renewable Energy Market Competence Index. 3

4 One of the core motivations of this work was to develop a completely objective index that doesn t depend on the opinion of the expert(s) conducting the counties evaluation. This is achieved through either mathematically define the indicators scores in relation to certain parameters of importance, or devise a point system questionnaire to be able to quantitatively measure an inherently qualitative parameter. It is worth mentioning that each indicator value and the final index value ranges from 0, meaning least competence, to 100, meaning most competence. The full description of the indicators used along with the index methodology were previously detailed in our previous work [1][2][3]. For the sake of completeness, we only present here a summary of the formulae used and the point system questionnaires. The 18 indicators for CSP Market Competence Index are defined as follows: 1. Global Competitive Global Copmetitive Index ( Adjusted) = Global Copmetitive Index ( country 100 Global Copmetitive Index value) Global Copmetitive Index ( min value) ( ) ( ) max value Global Copmetitive Index min value 2. Corruption Perception Corruption Perception Index ( Adjusted) = Corruption Perception Index ( country value) Corruption Perception Index (min value) 100 Corruption Perception Index (max value) Corruption Perception Index (min value) (2) 3. Political Instability Political Instability Index ( Adjusted) = Political Instability Index ( country value) Political Instability Index (min value) Political Instability Index (max value) Political Instability Index (min value) (3) 4. Energy Intensity Total Pr imary Energy Supply ( TEPS) Energy Intensity Value ( EIV ) = ( toe/1000u$ ) (4) Total GDP Taking PPP int o Account EIV ( country value) EIV (min value) Energy Intensity = (5) EIV (max value) EIV (min value) (1) 4

5 Non 5. Non Final Non Total Final Value ( NEFV ) = Final energy Consumption ( TFC) Total Final 6. consumption consumption = Consumption Consumed ( TWh) ( country value) NEFV ( min vlaue) ( ) ( ) max vlaue NEFV min vlaue NEFV = 100 (7) NEFV Gross production + imports exports transmission/distribution losses (8) Growth Consumption Consumption Vlaue in Growth ( ECGV ) = = 7. Net Imported Net Im ported Net Non Value ECGV 100 ECGV Consumption in 2007 ( TWh) ( country value) ECGV ( min value) ( ) ( ) max value ECGV min value (6) (9) (10) ( NIEV ) = Im ported Exported ( ktoe) ( country value) NIEV ( min value) ( ) ( ) max value NIEV min value NIEV Im ported = 100 (12) NIEV 8. Non RE Production Non RE RE Electricit y Pr oduction Pr oduction 9. Oil Insecurity s Years of Supply ( YoS) ( Oil) Value Electricit y Electricit y Electricit y Electricit y ( NREEPV ) Pr oduced Pr oduced Pr oduced Pr oduced = by Oil + by by Coal + by Natural Nuclear NREEPV = 100 NREEPV Gas + Energy ( GWh ) (11) (13) ( country value) NREEPV ( min value) ( ) ( ) max value NREEPV min value ( Oil) ( Oil) (14) Pr oven Re serve = ( Years) (15) Current Annual Consumption 5

6 ( country value) YoSOil ( min value) ( ) ( ) max value YoSOil min value YoSOil Insec urity ( Oil) = (16) YoSOil 10. Natural Gas Insecurity s Years of Supply ( YoS) ( Gas) ( OGas) ( Gas) Pr oven Re serve = ( Years) (17) Current Annual Consumption ( country value) YoS ( ) ( ) ( ) Gas min value max value YoSGas min value YoSGas Insec urity ( Gas) = (18) YoSGas 11. RE Target Expected Annual new RE ( 1+ g) f i T Ei REi Pr oduction ( EAREP) = (19) f i ( country value) EAREP( min value) ( ) ( ) max value EAREP min value EAREP RE T arg et = 100 (20) EAREP Where i: Initial year when index is evaluated f: Final RE target year announced by the country g: Average electricity consumption growth rate E i : consumption in initial year (i) T: The target ratio of electricity production by RE announced by the county RE i : The total amount of electricity generated by RE resources in the initial year 12. Financial 13. Environmental Environmental Financial = Policy Item i (21) 45 CO2 Emission = 100 CO2 Emission 1 ( country value) CO2 Emission ( min value) ( ) ( ) max value CO2 Emission min value (22) 6

7 14. CSP Manufacturability 0.35 Average Glass Industry Score CSP Manufacturability Indicaor Score = Average Electronics Industry Average Steal Industry Score 15. Economic Potential Economic Potential = Economic Potential 100 Economic Potential ( country value) Economic Potential ( min value) ( ) ( ) max value Economic Potential min value Score (23) (24) 16. Institute Table (1) Answers of the questionnaire for the Institute. Points Answer 0 No, there isn t an institution for that specific technology 20 Yes, an institution exists but it is part of another one or a national research center 60 There is an institution that supports all RE technologies 100 Yes, there is an institution that solely supports this specific technology 17. Technology Target Expected Annual new Installed Capacity IC IC = (25) f i f i ( EAIC) MW / year ( country value) EAIC( min value) ( ) ( ) max value EAIC min value EAIC Technolog y T arg et = 100 (26) EAIC Where i: Initial year when index is evaluated f: Final target year announced by the country IC i : The amount of current installed capacity in the initial year. IC f : The amount of target installed capacity in the final target year. 7

8 18. Feed-in Tariff Points Power Period Table (2) Point system to assess Feed-in Tariff law. Inflation recovery Hybrid Plant 0 <=5MW <=5 years Digression No 1 5years<x<=10years <=10% 2 5MW<x<=50MW 10years<x<=15years No 10%<x<=20% 3 15years<x<=20years 20%<x<=30% 4 50MW<x<=250MW 20years<x<=25years 30%<x<=50% 5 Unlimited Unlimited Yes Yes The price of electricity 1st year of the law Less than 1.25 times the lower electricity bracket price times the lower electricity bracket price times the lower electricity bracket price times the lower electricity bracket price times the lower electricity bracket price >2.75 times the lower electricity bracket price After evaluating each of the 18 previous indicators, the Index score is evaluated using the following equations: X 2 X 1 Economic Potential + Technolog y T arget = (27) 2 Manufacturability + Institute + Feed in Tariff = (28) 3 Technolog y 13 1 X 3 = All General s (29) 13 Specific 1 Market Competence Index = X [ αx + ( α ) X ] α 1 (30) This set of equations, from Equation (1) to Equation (30), was used before to calculate the index score for 8 Arab countries and 10 other countries, and was published in 2012 [1][2][3]. Because the data used then to calculate the CSP Market Competence Index Scores were not available for a single year but rather over a period of time that extended from 2008 to 2011, the final index score and rank measured the countries competence over that period of time. Here we 8

9 updates the values of the indicators and the final index score and ranks with some minor changes as illustrated in the next section. 3. Variations from 2012 index results While the former CSP Market Competence Index published in 2012 used the data of Global competitive Index of 2010, the Political Instability Index covering , and Corruption Perception Index of 2010 [1][2][3], the current update of the CSP Market Competence Index for 2013 uses the updated data of both Global Competitive Index [4] and Corruption Perception Index of 2012 [5] where the Political Instability Index was not updated by Economist Intelligence Unit. While the Energy Intensity, the Non Final, the Net Imported, the Non-RE, and the Environmental data for the former CSP Market Competence Index published in 2012 used IEA free published data of 2008 that was available at the time of forming the index, this updated version of the index for 2013 uses the only available IEA free data which is for year All ingredient data needed for the calculations of these indicators according to equations (4) (6) (11) (13) and (20) respectively are available on IEA portal pages [6] [7] [8]. While the Consumption Growth for the former CSP Market Competence Index published in 2012 used the electricity consumption of 2007 and 2009 to calculate an annual average electricity consumption growth rate over these 2 years, the index for 2013 used the consumption of 2007 and 2010 to calculate the annual average electricity consumption growth rate over these 3 years. In both cases we relied on annual IEA fact book data [9]. While the Oil and Gas Insecurity s of 2012 edition of the index relied on the available free data of CIA fact book of 2010 for annual oil and gas consumption and total proven reserve, this edition of the index for 2013 uses the updated data of CIA fact book, where it indicates that the data are for 2011 or 2012 or early 2013 [10]. While the RE Target, Financial, and CSP Target of the previous edition of the index of 2012 relied mostly on the data published in REN21 Global Status Report (GSR) of 2011, In this updated version of the index we rely on the 9

10 newer version of the GSR for 2012 [11]. This newer version of REN21-GSR2012 report gives more detailed information about countries targets for renewable energies and for different technologies showing that countries are becoming more specific about their future plans. One complementary information regarding renewable energy support policies is the announcement by the Egyptian government in early 2013 the GSR report was published in June 11 th 2012 of setting the regulation allowing for net metering mechanism for electricity generation from solar energy [12]. We did not notice any change in CSP manufacturability capabilities, CSP economic potential, and institutions related to CSP technology, and thus the corresponding three indicators are the same for both versions of the index. Finally, concerning the CSP Feed-in Tariff, the only change is Spain suspension of the Feed-in Tariff for new projects starting in Jan While in 2012 edition of the index we applied the natural logarithmic function before calculating the final score for 6 indicators; Non Final, Consumption Growth, Net Imported electricity, Non RE Production, RE Target, and CSP Economic Potential, in this year update of the index we applied the natural logarithmic function for the same indicators except for the RE Target. Because of the devastating war in Syria, we believe Syrian market is not competitive by any means to attract investment in CSP, or any other RE technology. So, we have omitted Syria from this year version of the index. Tunis s score for Global Competitive Index published by the World Economic Forum was missing for 2012, and so we set Tunis score for this indicator to Results Here we present the final results of applying the CSP Market Competence Index as detailed in [1][2][3] with the variations and modifications detailed above to calculate the scores and ranks of the 17 countries under evaluation. Table (3) gives the score for each individual indicator for 17 countries. 10

11 Table (3) All 18 indicators values of the CSP Market Competence Index, 2013 edition. Country Global Competitive Index Political Instability Index Corruption Perception Index Energy Intensity Non Final [Ln] Consumption Growth [Ln] Net Imported electricity [Ln] Non RE Production [Ln] Oil Insecurity Gas Insecurity RE Target Financial Environmental CSP Manufacturability CSP Economic Potential (Ln) CSP Institute CSP Target CSP Feed-in Tariff Algeria Egypt Jordan Lebanon Libya Morocco Tunisia Brazil Turkey Spain Greece South Africa Malaysia India China USA Germany

12 Table (4) and (5) give the index score and rank of the 17 countries respectively after applying Equations (27-30), where Arab countries are given gray background. Countries order in all following tables and charts in this section are organized in a descending score order for the case of α=0. Table (4) Scores for 17 countries according to the CSP Market Competence Index, 2013 edition. Country α=0 α=0.2 α=0.4 α=0.6 α=0.8 α=1 Spain China India Morocco USA Egypt Algeria Jordan Brazil Tunisia Libya Turkey South Africa Lebanon Greece Malaysia Germany Table (5) Ranks for 18 countries according to the CSP Market Competence Index, 2013 edition. Country α=0 α=0.2 α=0.4 α=0.6 α=0.8 α=1 Spain China India Morocco USA Egypt Algeria Jordan Brazil Tunisia Libya Turkey South Africa Lebanon Greece Malaysia Germany

13 As it was previously discussed in [1][2][3], more detailed study is needed to decide a proper value for the weighting parameter α, and thus we explore countries scores and ranks for different values of α. In order to compare the changes from CSP Market Competence Index results of 2012 to that of the present edition of 2013, we plot in Figure (2), (3), and (4) the values of X1, X2, and X3 as defined in Equations (27), (28), and (29) respectively. 90 Values of X1 for CSP Market Competence Index 2012 and X X Figure (2) A comparison among X1 values of different countries for 2012 and 2013 editions of the CSP Market Competence Index. On one hand, the major positive change in X1 are of Algeria, Libya and China due to the increased CSP Target from 10, 0, and 31 in 2012 to 40, 14 and 71 in 2013 respectively. On the other hand, the major negative change is X1 are of Morocco and India due to the decreased CSP Target from 34 and 53 in 2012 to 18 and 28 in 2013 respectively. 13

14 90 Values of X2 for CSP Market Competence Index 2012 and X X Figure (3) A comparison among X2 values of different countries for 2012 and 2013 editions of the CSP Market Competence Index. While the values of X2 for all countries are the same, Spain shows a negative change because of Spain suspension of the Feed-in Tariff in Jan 2012 reducing its value of X2 from 52 in 2012 to 31 in Values of X3 for CSP Market Competence Index 2012 and X X Figure (4) A comparison among X3 values of different countries for 2012 and 2013 editions of the CSP Market Competence Index. 14

15 The general trend of X3 (which is the arithmatic average of all 13 indicators) is a small positive shift from 2012 to 2013 results. We also plot the countries scores for the two years editions of the index for the two extreme cases of α=0 and α=1 in Figure (5) and (6) respectively. Figure (5) (the case of α=0, where Manufacturability, Institution, and Feed-in Tariff indicators disappear from the calculation of the index according to Equation (30)) reveals a great improvement of chain s and Algeria s scores followed by a moderate increase of Egypt s score. Figure (6) (the case of α=1, where all general indicators disappear from the calculation of the index) shows how the cancelation of Spain s Feed-in Tariff reduces its score, and in opposite way the Feed-in Tariff of Algeria enhances its position in the positive direction. Though Algeria had issued its Feed-in Tariff law long time ago, the law is not implemented on the ground and thus is not making the expected effect in Algerian market as it should do. One important future improvement to the index is to quantitatively evaluate and include the effectiveness of the Feedin Tariff in improving the RE market in the countries CSP Market Competence Index Score for 2012 and 2013 (ξ=0) Figure (5) A comparison among countries scores for the case of α=0 for 2012 and 2013 editions of the CSP Market Competence Index. 15

16 CSP Market Competence Index Score for 2012 and 2013 (ξ=1) Figure (5) A comparison among countries scores for the case of α=1 for 2012 and 2013 editions of the CSP Market Competence Index. Finally, Table(6) shows the countries scores for the two editions of the index for the two extreme cases of α=0 and α=1. Again, the major change is revealed in the upward move of China and Algeria, and downward move of Spain in case of α=1. Table (6) Countries ranks for the two extremes of α=0 and α=1 for 2012 and 2013 editions of the CSP Market Competence Index. α=0 α=1 Country Spain China India Morocco USA Egypt Algeria Jordan Brazil Tunisia Libya Turkey South Africa Lebanon Greece Malaysia Germany

17 5. Conclusion The CSP Market Competent Index for 2013, that reveals the situation of 17 countries in the period between 2009 and 2012, is calculated. Setting larger CSP targets by China and Algeria greatly improved their rank from 4 th and 10 th place in 2012 to 2 nd and 7 th place in 2013 for the case of α=0, and from 5 th and 3 rd place in 2012 to 2 nd and 1 st place in 2013 for the case of α=1. Also the methodology of the index shows that when Spain Canceled its Feed-in Tariff in Jan 2012, it resulted in a major decrease in its score from 64 in 2012 to 49 in 2013 and a retreat in its rank from the 1 st place in 2012 to 4 th place in 2013 for the extreme case of the weighting parameter α=1. Another major setback occurs for Tunisia in the case of α=0 because of the absence of its score in Global Competitive Index published by World Economic Forum in 2012, thus reducing its X3 value from 44 in 2012 to 36 in 2013 and moving its position from the 7 th place in 2012 to 10 th place in The rank change of the remaining countries is not exceeding one place either in the upper or lower direction. References [1] Hatem Elrefaei, A Methodology for Deriving a Renewable Energy Market Competence Index with Application to CSP Technology, Master thesis in Renewable Energy and Energy Efficiency for the Middle East and North Africa (MENA) Region (REMENA), Cairo U and Kassel U, March [2] Hatem Elrefaei, Amel Bida, Mohab Hallouda, and Mohamed Elsobky; Renewable Energy Market Competence Index Part 1: The Methodology ; Renewable Energy 2013; 53; ; [3] Hatem Elrefaei, Amel Bida, Mohab Hallouda, and Mohamed Elsobky; Renewable Energy Market Competence Index Part 2: Application to CSP Technology ; Renewable Energy 2013; 53; ; 17

18 [4] Global Competitive Index [5] Corruption Perception Index [6] [7] [8] [9] [10] [11] REN , Renewable Energy Policy Network for 21 st century (REN21); Renewables 2012: Global Status Report; [12] 18

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