INOGATE Technical Secretariat and Integrated Programme in support of the Baku Initiative and the Eastern Partnership energy objectives

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1 Technical assistance for capacity building in compilation and analysis of the fuel and energy balance for the Kyrgyz Republic (AHEF 113.KG) FINAL REPORT INOGATE Technical Secretariat and Integrated Programme in support of the Baku Initiative and the Eastern Partnership energy objectives Contract No 2011/ A project within the INOGATE Programme Implemented by: Ramboll Denmark A/S (lead partner) EIR Development Partners Ltd. The British Standards Institution LDK Consultants S.A. MVV decon GmbH ICF International Statistics Denmark Energy Institute Hrvoje Požar INOGATE Technical Secretariat

2 Document title Technical assistance for capacity building in compilation and analysis of the fuel and energy balance for the Kyrgyz Republic (AHEF 113.KG), Final Report Document status Draft Prepared by Name Damir Pešut Alenka Kinderman Lončarevid Date 01/12/2014 Checked by Approved by This publication has been produced with the assistance of the European Union. The contents of this publication are the sole responsibility of the authors and can in no way be taken to reflect the views of the European Union. INOGATE Technical Secretariat

3 Contents 1 Executive summary Data collection and availability Input data for short-term energy demand forecast Air temperature Gross Domestic Product (GDP) Natural Gas Electricity Heat Input data for medium and long-term energy demand forecast Population Gross Domestic Product (GDP) Final energy consumption Short-term energy demand forecast for the period Assessment of electricity demand for the period Assessment of heat demand for the period Long-term energy demand forecasts for the period baseline scenario Assessment of the final energy consumption in industry Assessment of the final energy consumption in agriculture Assessment of the final energy consumption in the transport sector The final energy consumption in households The final energy consumption in the service sector Total final energy consumption Structure of the final electricity consumption in Kyrgyzstan Long-term energy demand forecasts for the period scenario with measures Measures in industry and agriculture Measures in transport Measures in households Measures in the service sector Total final energy consumption in the scenario with measures Structure of the final electricity consumption in Kyrgyzstan in the scenario with 3

4 measures Total energy efficiency and total savings in the final consumption Annex 1. Assessment of the short-term electricity demand for DSOs Sever Electro Vostok Electro Osh Electro Annex 1. Assessment of the short-term heat demand Bishkek Teploset Bishkek Teploenergo

5 List of figures Figure 1. Comparison of average monthly temperatures among the selected cities: Bishkek, Chuy, Jalal Abad, Osh Figure 2. Comparison of average annual HDD for cities: Bishkek, Chuy, Jalal Abad and Osh Figure 3. Average shares of GDP realised by months in the total annual GDP for the period Figure 4. Electricity consumption structure in Kyrgyzstan in 2013 according to the reports from DSOs Figure 5. Heat distribution in 2013 in Bishkek Teploset, Bishkek Teploenergo i Кыргызжилкоммунсою Figure 6. The structure of heat consumption in Kyrgyzstan Figure 7. Population in Kyrgyzstan in the period (Source: UN Population Division) Figure 8. Comparison between realized consumption and estimated total electricity consumption Figure 9. Influence of model parameters on total electricity consumption in Kyrgyzstan Figure 10. Results of the adjustment of the model for short-term electricity demand projections Figure 11. Total electricity demand forecast for the period Figure 12. Comparison between realized consumption and estimated total heat consumption Figure 13. Influence of model parameters on total heat consumption Figure 14. Results of the adjustment of the model for short-term projections of heat Figure 15. Total heat demand forecast for the period for the average year Figure 16. The intensity of electricity consumption in industry Figure 17. The intensity of heat consumption in industry Figure 18. Final energy consumption forecast in industry by Figure 19. The intensity of electricity consumption in agriculture Figure 20. The intensity of heat consumption in agriculture Figure 21. Final energy consumption forecast in agriculture by Figure 22. Tonne-kilometres per capita in Kyrgyzstan and the European countries Figure 23. Persons per car in Kyrgyzstan and the European countries Figure 24. Final energy consumption forecast in transport Figure 25. Household size in Kyrgyzstan and the European countries Figure 26. Average residential floor area in Kyrgyzstan and the European countries Figure 27. Household heat losses in Kyrgyzstan and the European countries Figure 28. Non-thermal electricity consumption per household in Kyrgyzstan and the European countries Figure 29. Final energy consumption forecast in households by Figure 30. Electricity intensity in services Figure 31. Heat intensity in services Figure 32. Final energy consumption forecast in services by Figure 33. Total final energy consumption forecast by Figure 34. Structure of the forecasted total final energy consumption by Figure 35. Structure of the forecasted final electricity consumption Figure 36. Specific consumption of new cars and stock average (EU) Figure 37. Heat losses in households in the scenario with measures Figure 38. Total final energy consumption forecast in the scenario with measures Figure 39. Structure of total final energy consumption forecasts in the scenario with measures

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7 List of tables Table 1. Average monthly temperatures for the city of Bishkek Table 2. Thirty-year averages of monthly air temperatures for the selected cities Table 3. Population in Kyrgyzstan in the period (Source: World Development Indicators) Table 4. Population in KG in the period (Source: Estimates by the ITS experts) Table 5. Total GDP and structure by economic activities Table 6. Final energy consumption in 2012 (Extract from IEA energy balance; 18 Table 7. Final energy consumption in 2012 (reconstructed) Table 8. Monthly electricity consumption in the period in Kyrgyzstan Table 9. Electricity consumption assessment for the period in average weather conditions Table 10. Electricity consumption assessment for the period in extremely warm weather conditions Table 11. Electricity consumption assessment for the period in extremely cold weather conditions 23 Table 12. Heat distribution in the period in thousand Tcal Table 13. Heat consumption assessment for the period in average weather conditions Table 14. Heat consumption assessment for the period in extremely warm weather conditions Table 15. Heat consumption assessment for the period in extremely cold weather conditions Table 16. Final energy consumption forecast in industry by Table 17. Final energy consumption forecast in agriculture by Table 18. Final energy consumption forecast in transport Table 19. Final energy consumption forecast in households by Table 20. Final energy consumption forecast in services by Table 21. Total final energy consumption forecast by Table 22. Structure of the forecasted total final energy consumption by Table 23. Structure of the final electricity consumption by sectors (TWh) Table 24. Savings with regard to the baseline scenario in industry Table 25. Savings with regard to the baseline scenario in transport Table 26. Savings with regard to the baseline scenario in households Table 27. Savings with regard to the baseline scenario in services Table 28. Total final energy consumption forecast in the scenario with measures Table 29. Structure of total final energy consumption forecast in the scenario with measures Table 30. Savings in the scenario with measures with regard to the baseline scenario Table 31. Structure of the final electricity consumption forecast in the scenario with measures (TWh)

8 Table 32. Total savings of final energy consumption by Table 33. Sever Electro: Total electricity consumption in the period Table 34. Sever Electro: Assessment of electricity consumption in average weather conditions Table 35. Sever Electro: Assessment of electricity consumption in extremely cold conditions Table 36. Sever Electro: Assessment of electricity consumption in extremely warm conditions Table 37. Vostok Electro: Total electricity consumption in the period Table 38. Vostok Electro: Assessment of electricity consumption in average weather conditions Table 39. Vostok Electro: Assessment of electricity consumption in extremely cold conditions Table 40. Vostok Electro: Assessment of electricity consumption in extremely warm conditions Table 41. Osh Electro: Total electricity consumption in the period Table 42. Osh Electro: Assessment of electricity consumption in average weather conditions Table 43. Osh Electro: Assessment of electricity consumption in extremely cold weather conditions Table 44. Osh Electro: Assessment of electricity consumption in extremely warm conditions Table 33. Bishkek Teploset: Total heat consumption in the period Table 34. Bishkek Teploset: Assessment of heat consumption in average weather conditions Table 35. Bishkek Teploset: Assessment of heat consumption in extremely cold conditions Table 36. Bishkek Teploset: Assessment of electricity consumption in extremely warm conditions Table 37. Bishkek Teploenergo: Total heat consumption in the period Table 38. Bishkek Teploenergo: Assessment of heat consumption in average conditions Table 39. Bishkek Teploenergo: Assessment of heat consumption in extremely cold conditions Table 40. Bishkek Teploenergo: Assessment of heat consumption in extremely warm conditions

9 1 Executive summary The overall objective of the project Technical assistance for capacity building in compilation and analysis of the fuel and energy balance for the Kyrgyz Republic is to support the State Department for Fuel and Energy Balance in the development of mid- and long-term energy strategies and plans by providing capacity building in demand forecasting and an introduction to the optimisation of expansion plans based on the best practice methodologies used in the EU, while taking into account the energy policy objectives of the country. The specific objectives of the project were the following: Providing capacity building of the relevant staff of the State Department for Fuel and Energy Balance with a comprehensive appreciation and understanding of the methods deployed in the EU countries for establishing forecasted energy balances; Introducing the subject of energy system optimisation and describing the principles and tools used to develop the least cost expansion plans in the context of the overall energy policy of the country. Specific training on the methods and software used to develop plans is beyond the scope of this assistance, but may be considered for a follow up AHEF, if the beneficiary wishes so. Recommending appropriate techniques for extrapolating changes in demand under scenarios proposed and conducting training within the recommended solutions. This Report describes growth of energy demand in the period by This time period assumes a five-year-short-term period and a long-term period which includes 20 years. For these two horizons, different approaches in data collection and modelling future energy demand were applied. The Report describes the results of modelling the long-term total energy demand forecasts of electricity and heat for the period , and a separate assessment of demands for individual distribution companies. Short-term demand assessment is based on the expected weather conditions, GDP growth and seasonal impact. Long-term energy demand includes assessment of the consumption of all energy forms by the final consumption sectors (households, services, industry, agriculture, transport and construction) for two defined scenarios baseline and the one with measures. The data input and methodology for calculating energy demand was discussed and analysed during the workshops organised for the representatives of relevant Ministries, energy companies, agencies etc. 9

10 2 Data collection and availability In order to make projections of future needs, it is necessary, together with data that are collected regularly every year in the National Statistics Committee, Ministry for Energy and Industry and other institutions, to collect additional data that are used in models for calculating future energy needs. The structure of data that should be processed in models and the method of collecting data have already been agreed upon during the 1 st Workshop at the Ministry of Energy and Industry which was held in the period 24th-25th July, Within the first report, prepared after the 1 st Workshop, the Plan of collecting data and questionnaires for data providers were developed. The questionnaires, which were sent to data providers, were designed in an excel format document under titles Short-term energy demand forecasting Data Specification and Long-term energy demand forecasting Data Specification. The ITS experts assessed that the following institutions and companies were crucial data providers and had available data for energy demand forecasting: - Macroeconomics indicators (population, GDP) National Statistics Committee - Temperatures, Degree Days - National Hydro-Meteorological Service - Electricity consumption DSOs - Sever Electro, Vostok Electro, Osh Electro, Djalal-Abad Electro; - Natural gas consumption Kyrgyzgaz; - Heat consumption Bishkekteposet. 2.1 Input data for short-term energy demand forecast This chapter describes input tables and data structure necessary for short-term energy demand assessment. Questionnaires are designed for reporting on weather conditions, GDP on monthly level, heat consumption, natural gas consumption and electricity consumption. In Energy data collection plan it was recommended to collect as much as possible detailed data concerning the end-use consumption categories and territorial units. It was particularly requested to report on data series for at least eight uninterrupted previous years Air temperature According to data collection plan, necessary data for modelling short-term energy demand are data on weather conditions, more precisely: outdoor air temperature and Heating Degree Days (HDD). The ITS experts recommended using data on air temperatures from National Hydro - Meteorological Service and National Statistics Committee. A part of the data on monthly temperatures was delivered to the ITS experts but the remaining data were downloaded from Weather Underground site as this site receives data from National Hydro Meteorological institutes. The site provides monthly data only for the city of Bishkek and the 30-year averages for Bishkek, Chuy, Jalal Abad and Osh. Average monthly temperatures for the city of Bishkek and 30-year monthly temperatures for selected cities are shown in the following tables. 10

11 Table 1. Average monthly temperatures for the city of Bishkek C Month Jan Feb Mar April May June July Aug Sept Oct Nov Dec ,32 3,32 9,13 14,43 18,45 23,03 24,45 25,19 18,03 14,74 6,03-2, ,55 1,64 4,77 15,77 18,81 24,37 26,29 24,68 19,50 9,52 6,03-5, ,13-4,66 10,19 13,07 21,45 25,90 27,23 25,19 18,07 11,71 4,73-1, ,81 1,04 7,48 11,37 17,23 22,37 25,23 23,94 17,83 12,35 4,30-0, ,42-3,32 6,81 13,10 17,90 23,43 25,13 25,42 18,47 13,55 7,00-2, ,61-1,68 4,13 14,70 19,16 23,83 25,71 24,97 19,60 12,48 2,30-6, ,00-7,34 3,61 16,47 19,10 23,57 25,32 25,23 19,47 12,42 2,10-6, ,23-1,14 9,77 11,53 16,06 20,67 24,06 22,13 17,53 11,68 4,77-0,13 Table 2. Thirty-year averages of monthly air temperatures for the selected cities C Bishkek -9,32 3, 3 2 9, ,43 18,45 23,03 24,45 25,19 18,03 14,74 6, 0 3-2, 9 0 Chuy -3,55 1, 6 4 4, ,77 18,81 24,37 26,29 24,68 19,50 9, 5 2 6, 0 3-5, 0 3 Jalal Abad -12,13-4,66 10,19 13,07 21,45 25,90 27,23 25,19 18,07 11,71 4, 7 3-1, 4 5 Osh -3,81 1, 0 4 7, ,37 17,23 22,37 25,23 23,94 17,83 12,35 4, 3 0-0, 4 2 Figure 1. Comparison of average monthly temperatures among the selected cities: Bishkek, Chuy, Jalal Abad, Osh The special indicator used in models for calculating the short energy demand forecast is Heating Degree Days (HDDs). Heating degree days (HDDs) is a measurement designed to reflect the demand 11

12 for energy needed to heat a building. It is derived from measurements of outside air temperature. The heating requirements for a given structure at a specific location are considered to be directly proportional to the number of HDDs at that location. Comparison of Heating Degree Days among the selected cities is shown in the graph below. The figure shows that the region Chuy is the coldest region in Kyrgyzstan, while Jalal Abad is the warmest region, and according to the number of HDDs it can be assumed that energy consumption in buildings in the region Chuy is about 25 percent higher than the consumption in Jalal Abad region. Figure 2. Comparison of the average annual HDD for cities: Bishkek, Chuy, Jalal Abad and Osh Gross Domestic Product (GDP) In the model for short-term energy demand forecasting, the Gross Domestic Product is used to analyse the impacts of economic activities in the country on energy consumption. Data that were submitted to the ITS experts concern the period and show Gross Domestic Product for monthly periods in current prices. Besides the total annual realized level of GDP, monthly realization can also have an impact on seasonal energy demand. Experience and calculations for some European countries have shown that periodical/monthly realisation of GDP is closely connected to the realised industry production, tourism activities etc. Periodical realisation of GDP in Kyrgyzstan is characterised by the peaks in the last quarter of the year. The diagram on the figure below shows average monthly shares of GDP realised in the period

13 Figure 3. Average monthly shares of GDP in total annual GDP for the period Natural Gas Kyrgizgaz did not deliver data on natural gas consumption Electricity The data on electricity consumption that have been collected for making this study correspond to the structure described in the Data collection plan. Three electricity distribution companies (Distribution System Operators - DSOs)- Sever Electro, Osh Electro and Vostok Electro - provided data on monthly consumption for the period for the following consumer categories: the industry sector, public institutions (budgetary organizations), agricultural households, households, others. Since the electricity distribution company of Djalal Abad did not submit data, the estimation of total energy consumption in Kyrgyzstan was based on the data from the National Statistical Committee, which make the total electricity consumption balance every year according to the data collected from electricity distributions and from consumers themselves. The assessment of electricity distribution in the distribution area DSO Djalal Abad is about 28 percent of total electricity consumption in the Republic of Kyrgyzstan. Total electricity consumption in 2013, which was the basis for making short-term forecasts, amounted to 9993 GWh, and this amount complies with the information that the National Statistical Committee (NSC) submitted to the International Energy Agency. The structure of the distribution of energy consumption among the sectors is different comparing to the structure represented by the National Statistical Committee (NSC), which was created on the basis of extensive data collection on energy consumption in the sector of enterprises and households. The NSC classifies the energy consumption according to NACE classification of economic activities companies are sending a report on electricity consumption, while distribution companies have their own internal classification that includes five categories, as stated in the introductory 13

14 paragraph of this chapter. The consumption data submitted by electricity distribution companies include losses of electricity within the system (technical and commercial), and for the purpose of short-term forecasts, of which the aim and purpose are to make the projection of needs on energy companies level, the consumption structure of electricity distribution companies has been retained. When making longterm forecasts, total distribution of final electricity demand in 2012 is retained with the balance of the NSC, or the IEA reports. The structure of total electricity consumption in 2013 is shown in the following figure and indicates that households are the sector with the largest share of energy consumption on the level of electricity distribution, and their share within total consumption amounts to 66 %. The amount of consumption share in the industry sector, services and others is about 10 %, while the agricultural sector has the lowest share in energy consumption which amounts to 1 %. Figure 4. Electricity consumption structure in Kyrgyzstan in 2013 according to the reports from DSOs Electricity consumption has been steadily increasing in the recent years, and the consumption in 2013 was higher by 46 % compared to Electricity consumption has a highly seasonal character and the impact of the outside air temperature is significant. This dependence could be seen, for example, in electricity consumption in January 2008, when, due to extremely low temperatures, electricity consumption was about 28 % higher than in months with average monthly outdoor temperature Heat The data on heat energy distribution were acquired from three heat distribution companies: Bishkek Teploset, Bishkek Teploenergo i Кыргызжилкоммунсою. Bishkek Teploset and Bishkek Teploenergo submitted their data on heat energy distribution on monthly basis for the period , while Кыргызжилкоммунсою provided data only for the years 2011, 2012, 2013 and It is assumed 14

15 that the heat distribution in 2014 has been estimated. Comparison of the total consumption for the three enterprises mentioned above shows that the Bishkek heating system has had the largest share, which amounts approximately to 80 %. Analyses of total annual heat consumption in the last ten years show steady decrease in heat consumption. Figure 5. Heat distribution in 2013 in Bishkek Teploset, Bishkek Teploenergo i Кыргызжилкоммунсою All three distribution enterprises have submitted data on the structure of heat consumption for certain categories of consumers on monthly basis: households, budgetary organizations and other services, industry and other consumers. Households are, according to the analysis, the biggest consumer of heat in all three distribution areas and account for over 60 % of total consumption. The structure of consumption in 2013 per particular distribution company is shown in figures below. From the very structure of heat consumption it can be concluded that consumption has a highly seasonal nature which is determined by space heating needs. 15

16 Figure 6. The structure of heat consumption in Kyrgyzstan 2.2 Input data for medium- and long-term energy demand forecast Population According to UN Population Division (UNPD), the estimated population in Kyrgyzstan in the past 60 years ( ) and forecasts of the population growth by 2100 are shown in the following table and diagram. Table 1. Population in Kyrgyzstan in the period (Source: UN Population Division) Year Population, Year Population, Year Population,

17 Population (000) - UN forecast Figure 7. Population in Kyrgyzstan in the period (Source: UN Population Division) At the same time World Development Indicators (WDI) estimate that the number of inhabitants in the period from 2010 to 2012 is different from the data published by UN population division. The estimates from the WDI sources are shown in the Table 3. Table 3. Population in Kyrgyzstan in the period (Source: World Development Indicators) Year Population The ITS experts suggest checking the availability of official statistics on the population for the years 2011, 2012 or 2013 and even for earlier periods. If this figures are different from the UN and WDI data, then the ITS expert team requires from the MEI to decide which data should be used in the model for energy demand forecasting. The ITS experts assume that this data is available in the NSC and they will require from the NSC to deliver this data in the table format, if possible for five consecutive year period as presented in the table. Regarding estimates of the population growth in the future period , the ITS experts are suggesting to use the official Kyrgyz growth population estimates for planning, if such exists and if the MEI thinks that this data are relevant for planning in Kyrgyzstan. The analysis of the available official data on population growth and discussion of its relevance with MEI will be performed by the Country Expert. After the discussion of its relevance with MEI, the ITS experts suggest to use the figures shown in Table 4. which are based on ITS expert s assessment for the energy demand forecasting. Table 4. Population in KG in the period (Source: Estimates by the ITS experts) Item Unit Population* *million+ 5,607 5,790 6,162 6,557 6,871 7,145 Pop. gr. rate* *%p.a.+ 1,075 1,253 1,250 0,940 0,785 17

18 2.2.2 Gross Domestic Product (GDP) According to data from the World Development Indicators (WDI) for 2012, the total Gross Domestic Product for Kyrgyzstan and structure by economic activities (in constant price for 2005) are shown in the following table. The GDP growth estimates by 2019 are taken from the International Monetary Fund and the remaining figures in the table, data for the period , are ITS expert s estimates based on the 6 % GDP growth assessment. Table 5. Total GDP and structure by economic activities Item Unit GDP* *bill US$ ,209 3,874 4,999 6,700 9,000 12,000 GDP gr. rate* *%p.a.+ 6,485 5, GDP/cap US$ Agriculture *%+ 20,2 18,9 16,8 15,0 13,4 12,0 Industry *%+ 22,0 22,4 23,1 23,8 24,6 25,4 Service *%+ 53,8 54,4 55,4 56,4 57,4 58,4 Energy *%+ 4,0 4,3 4,7 4,8 4,7 4, Final energy consumption Final energy consumption for the defined consumer categories for 2012 is shown in Table 6. The source of data is Kyrgyzstan energy balance published on the IEA s official web site. The NSC filled out for the first time five joint annual energy questionnaires and submitted them to the IEA late in 2013 and since then, the NCS had continuously worked on their improvements, and consequently the energy balance published on IEA web site was improved. Table 6. Final energy consumption 2012 (Extract from IEA energy balance; 18

19 The weakest part of energy balance are still renewables, since up to this day there is no official data which could, for example, confirm the use of fuel wood. Therefore, because of the large deficit of energy consumption in the household sector, the ITS experts searched for other available data and studies which were developed in recent period and the results of which could improve understanding of energy consumption in the household sector. According to a World Bank study from 1994, the annual use of fuel wood in Kyrgyzstan was estimated to 300 ktoe (12.56 TJ), and the ITS experts concluded that this comprehensive research could be used for improving energy consumption in the household sector. For the purpose of this Report, the final energy consumption in 2012 has been reconstructed as it is shown in Table 7. Besides energy consumption in the household sector, the ITS experts performed a more detailed analysis of electricity use and connected losses in electricity distribution companies. Total electricity losses amount to 289 ktoe and consist of technical and commercial losses. Total electricity losses represent 25 % of total electricity delivered to energy system in The ITS experts believe that technical losses cannot be higher than 15 % in distribution system, so therefore the remaining 10 % can be considered commercial losses, i.e. 116 ktoe. It is assumed that 70 % of commercial losses occur in the household sector and 30 % in the service sector. These amounts of commercial losses were added as the real consumption into the electricity consumption of households and services in Table 6. Table 7 shows the final correction of the final energy consumption in 2012 which is used for modelling future energy demand forecasts. Table 7. Final energy consumption in 2012 (reconstructed) 2012 ktoe Coal Petroleum Products Natural Gas Electricity District Heating Firewood TOTAL Final consumption Household Industry Agriculture Transport Service Non-energy use

20 3 Short-term energy demand forecast for the period The model for the forecast of the future seasonal energy demand is based on the multiple variable linear regression concepts which are used to calculate energy demand for particular parts of the year (seasons, months, weeks and days). The selection and evaluation of the appropriate demand model applied for the modeling of the demand in Kyrgyzstan is developed based on testing various combinations, using standard parameters and indicators in the regression analysis. The basic aim of the model is to find and test significant variables which can explain energy consumption and which can be used to forecast future expectations. Taking into consideration the timeframe of planning (short- and medium-term period), it is obvious that weather conditions and seasonal components are the main consumption drivers. Besides the weather and seasonal components, the intention was to include an economical parameter which would reflect the economic situation and economic development in the country. Firstly, consumer prices were tested and statistical analysis showed that this was an insignificant variable in most cases. The variable of realized level of Gross Domestic Product (GDP) was introduced instead of the price effect. This variable was tested and the results showed its influences and significances related to the consumption in all consumption sectors. The basic mathematical formulation of the forecasting model for the monthly energy consumption equations is set and presented in the following way: Energy consumption = Temperature component +Seasonal component + Economy component Temperature comp. - reflects consumption as an effect of the outdoor air temperature; Seasonal comp. - reflects consumption depending on the season, month; GDP comp. - reflects influence of the Gross Domestic Product. Creating scenarios of future short-term and medium-term energy needs is based on assumptions of expected weather conditions and the expected growth of the GDP. Weather conditions are calculated on the basis of standard deviation (δ), statistical parameter that measures dispersion of temperatures in a 30-year period around the arithmetic mean (t av ). On the basis of statistical data, monthly standard deviation was calculated, and the expected air temperatures for cold and warm winters and extremely cold and extremely warm winters were assumed. The growth of GDP is usually based on macroeconomic scenarios which are adopted and published by the authority responsible for economic development. In case of Kyrgyzstan, the growth of GDP in the next year is based on the assumptions that the rate of the growth will amount to 6 %. The same growth is used for medium- and long-term energy demand forecasting. By inserting data on expected weather conditions and GDP trends into equations obtained for particular consumption sectors and energy forms, projections of future energy needs on both annual and monthly levels were obtained. The analysis of obtained results showed that changes in weather conditions similarly influenced all consumers. Decrease in the outdoor air temperature results in the increase of electricity and heat consumption, and the increase of temperature has the opposite effect. On the other hand, the changes of GDP in particular consumption sectors and energy sources 20

21 have a different effect. 3.1 Assessment of electricity demand for the period Total electricity consumption for every month in the period is summarized in the following table. Table 8. Electricity consumption in the period in Kyrgyzstan shown in months GWh Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total ,7 795,9 668,5 508,5 403,5 373,0 382,6 381,1 360,2 433,5 598,3 878, , ,5 785,8 743,8 492,3 423,0 391,6 391,5 390,8 387,4 591,8 757,0 870, , ,6 964,1 670,4 478,7 407,6 403,6 404,5 383,1 370,6 433,6 632,9 654, , ,0 649,1 619,0 523,4 478,1 431,3 432,3 428,6 429,1 500,1 693,7 835, , ,0 738,7 634,0 505,2 435,4 414,9 415,2 419,8 420,1 496,8 723,1 937, , ,9 934,2 794,2 621,7 481,6 462,3 467,0 473,5 459,4 566,0 964, , , , , , , , , 8 640,2 515,6 483,3 480,8 500,1 469,6 618,1 962, , ,2 721,2 575,7 522,2 533,5 531,6 519,5 624, , , ,0 On the basis of actual monthly electricity consumption, the outside air temperature and GDP, the parameters of multiple linear regression equations were calculated by using the least square method, and then the analysis of statistical parameters of the equation (regression coefficient, standard error values for coefficients, standard error value for the constant b, coefficient of determination, F test and other) was conducted. All analyses have shown that the regression model is significant. The following tables contain estimates of future energy consumption for the year with average weather conditions, extremely cold and extremely warm year. The adjustment of models or estimates of generated electricity consumption for the period is shown in Figure 6, while the impact of individual model components on energy consumption is shown in Figure 7. According to this model, it can be concluded that the temperature component affects 18 % of the consumption; GDP affects 43 % of the consumption, while seasonal influence (month of the year) affects 38 % of the consumption Estimated consumption, GWh Realised consumption, GWh

22 Figure 8. Comparison between realized consumption and estimated total electricity consumption GDP Temperature Season Figure 9. Influence of model parameters on the total electricity consumption in Kyrgyzstan Distribution of the difference between observed and calculated values Figure 10. Results of the adjustment of the model for short-term electricity demand projections Estimates of total monthly and annual electricity consumptions for the period are shown in the tables below. From tables and the following pictures it can be concluded that in cases of extremely warm or cold weather conditions the total consumption will change by 10 % compared to the average year. On monthly level those differences are bigger. For example, if January is extremely cold in 2015, then it is expected that electricity consumption will be 15 % higher compared to the average year. 22

23 Table 9. Electricity consumption assessment for the period for average weather conditions GWh Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total Table 10. Electricity consumption assessment for the period for extremely warm weather conditions GWh Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total Table 11. Electricity consumption assessment for the period for extremely cold weather conditions GWh Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total

24 Consumption , MWh Assessments for , MWh Very cold Cold Average Warm Very warm Figure 11. Total electricity demand forecast for the period Consumption projections for individual distribution systems companies are shown in Annex Assessment of heat demand for the period The input data collected by the heat distribution companies - Bishkek Teploset, Bishkek Teploenergo and Кыргызжилкоммунсою - are shown in the table below. The data for Кыргызжилкоммунсою in the period are estimated data because they were not provided by the distribution company. Considering that the share of Кыргызжилкоммунсою is very low in total heat distribution, it is not expected that the estimated consumption for this enterprise could cause significant errors in the assessment of future needs. 24

25 Table 12. Heat distribution in the period in thousand Tcal Tcal Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total ,7 190,5 158,8 73,3 26,8 45,2 76,3 72,6 75,4 82,2 154,3 271, , ,7 204,0 198,4 73,0 27,6 44,7 58,0 57,2 57,9 72,8 167,5 263, , ,4 244,6 133,7 57,8 25,8 30,8 51,7 52,2 52,5 52,9 157,1 225, , ,1 196,8 158,1 53,8 23,5 31,1 50,7 50,8 51,8 52,6 150,1 238, , ,4 227,3 126,4 49,7 23,3 26,3 44,4 44,3 45,9 47,1 137,8 227, , ,6 213,4 197,5 48,2 23,7 31,9 43,6 44,1 46,3 59,3 196,0 270, , ,9 251,3 193,7 47,8 26,4 24,2 42,1 43,0 44,4 45,6 184,8 275, , ,6 213,1 144,5 47,1 24,6 25,3 42,0 42,0 44,1 58,0 179,3 236, ,5 On the basis of actual monthly heat distribution, the outside air temperature, the realised level of GDP, similar to the distribution of electricity, parameters of multiple linear regression equation were calculated by using the least square method; and then statistical parameters of the equation (regression coefficient, standard error values for the coefficients, standard error value for the constant b, coefficient of determination, F test and other) were analysed. All analyses have shown that the regression model is significant. The following tables contain estimates of future energy consumption for the year with average weather conditions, extremely cold and extremely warm year. The adjustment of the model, i.e. the comparison of realized and estimated amounts of heat for the period , is shown in the first graph in the following figure, out of which it can be concluded that the difference between the estimated value and the realized value is very small and that the model is very significant. If the analysis of the graph in Figure 11 is continued, it can be concluded that heat consumption has a significantly seasonal character and is completely dependent on the outdoor air temperature. The model has eliminated the influence of GDP, and the statistical analysis of this variable indicates that it is completely insignificant and that its impact on consumption is very small and currently can be ignored. The third graph in the figure shows the reliability of the model, i.e. the distribution of differences between actual and estimated values. The model is significant in cases where there is no dispersion correlation of this variable, which is again the case with this model. 25

26 Figure 12. Comparison between realized consumption and estimated total heat consumption Figure 13. Influence of model parameters on total heat consumption Figure 14. Results of the adjustment of the model for short-term projections of heat 26

27 Table 13. Heat consumption assessment for the period for average weather conditions Gcal Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total ,9 206,0 167,3 58,2 16,6 23,5 41,5 41,6 39,5 56,7 154,2 221, , ,7 204,5 165,6 56,5 14,8 21,5 39,2 39,4 36,7 54,2 151,8 218, , ,3 202,8 163,7 54,6 12,8 19,4 36,7 36,9 33,6 51,5 149,2 215, , ,7 201,0 161,7 52,6 10,6 17,0 33,9 34,2 30,2 48,6 146,4 211, ,5 Table 14. Heat consumption assessment for the period for extremely warm weather conditions Gcal Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total ,0 174,3 129,8 19,9 16,6 23,5 41,5 41,6 39,5 8,1 116,9 186, , ,8 172,8 128,1 18,2 14,8 21,5 39,2 39,4 36,7 5,6 114,5 183,1 978, ,4 171,1 126,2 16,3 12,8 19,4 36,7 36,9 33,6 2,9 111,9 180,0 951, ,9 169,4 124,1 14,3 10,6 17,0 33,9 34,2 30,2 0,0 109,1 176,5 921,1 Table 15. Heat consumption assessment for the period for extremely cold weather conditions Gcal Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total ,6 237,7 204,8 98,9 64,2 23,5 41,5 41,6 83,9 101,2 191,5 256, , ,3 236,2 203,1 97,2 62,4 21,5 39,2 39,4 81,1 98,7 189,1 253, , ,9 234,5 201,2 95,3 60,4 19,4 36,7 36,9 77,9 96,0 186,6 250, , ,4 232,7 199,2 93,3 58,2 17,0 33,9 34,2 74,5 93,1 183,7 246, ,9 27

28 The analysis of heat energy consumption in the period shows that the trend of total consumption is negative and that consumption is gradually reducing every year. The model for shortterm forecasts also recognized the downward trend of the consumption and calculated average annual decline of 3 %. It can be concluded, on the basis of the figure on the next page, that in cases of extremely warm or cold weather conditions, the difference of total consumption would amount to approximately 20 % compared to the average year. On monthly basis differences are bigger. For example, if January is extremely cold in 2015, it is expected that heat consumption will be 25 % higher compared to the average year. Figure 15. Total heat demand forecast for the period for the average year Consumption projections for individual heat distribution companies are shown in Annex 2. 28

29 4 Long-term energy demand forecasts for the period baseline scenario 4.1 Assessment of the final energy consumption in industry Energy intensity of industry Regarding the fact that intensity is measured in the amount of energy consumed per one USD 2005, Kyrgyzstan intensities can be compared to energy intensities of the European countries, Figures 9 and 10. It is evident that Kyrgyzstan is, according to those indicators, in group with the transition countries with the greatest intensity of industry. Intensities in these countries are even more than ten times higher than intensities in most developed European countries. According to development analogy of those indicators for more developed countries in the past, the development of energy intensities of processing industry in Kyrgyzstan is predicted for the future. The expected intensities are going to decrease very soon, but despite that, they are still going to be very high. Their movement will go slightly above the development which has already been reached in Estonia and Slovakia and later on in Slovenia. Energy intensity decrease shown in this way is the result of structural changes, technically efficient technologies in industrial processes and above all, more competitive products which attain higher prices on market with the same material output and with higher additional value, i.e. GDP of the belonging industrial field of activity. The intensity of the final electricity consumption in industry is very high, while the intensity of heat consumption is quite moderate. Figure 16. The intensity of electricity consumption in industry 29

30 Figure 17. The intensity of heat consumption in industry Assessment of the final energy consumption in industry After useful energy needs are predicted, the final energy consumption has to be predicted by defining shares (in scenarios) of individual energy carrier in the future. Another step is also the prediction of the improvement of the energy efficiency of technologies for transforming the final energy into useful energy. The product of useful energy, the share of the final power source and the reciprocal value of energy efficiency provide the final energy consumption of energy carriers (generic formula in MAED model). Relatively intensive growth is expected in energy consumption in the industry sector. This is due to faster growth of GDP in the industry sector contribution than in other national economy activities and due to the improvement of energy intensity in industrial production. Total final consumption would increase by 3 times by the year 2035, while electricity consumption would increase by 2.8 times. In the same period, the GDP of manufacturing industry will increase by 4.3 times. Natural gas consumption was assessed according to the prediction that by 2035 natural gas would account for 40 % of the industrial heating market, representing, in turn, a 37 % share or 640 million m 3 of the total final consumption in industry. 30

31 Figure 18. Final energy consumption forecast in industry by 2035 Table 16. Final energy consumption forecast in industry by 2035 Item Unit Traditional fuels PJ 0,000 0,000 0,000 0,000 0,000 0,000 Modern biomass PJ 0,000 0,000 0,000 0,000 0,000 0,000 Electricity PJ 7,159 8,740 10,900 13,491 16,641 20,272 District heat PJ 0,628 0,858 1,343 2,127 3,375 5,318 Solar PJ 0,000 0,000 0,000 0,000 0,000 0,000 Fossil fuels PJ 17,157 20,526 25,973 33,133 42,253 53,312 Motor fuels PJ 0,470 0,578 0,769 1,064 1,475 2,029 Coke PJ 0,000 0,000 0,000 0,000 0,000 0,000 Feedstock PJ 3,433 3,950 4,544 5,227 6,013 6,918 Total manufacturing PJ 28,847 34,652 43,529 55,041 69,757 87,849 LPG PJ 0,000 0,000 0,000 0,000 0,000 0,000 Coal and Coal Pr. PJ 11,807 13,448 16,200 19,674 23,886 28,692 Other Petroleum Pr. PJ 0,954 1,141 1,444 1,842 2,349 2,964 Natural Gas PJ 4,396 5,937 8,329 11,617 16,018 21,657 Fossil fuels PJ 17,157 20,526 25,973 33,133 42,253 53,312 31

32 4.2 Assessment of the final energy consumption in agriculture Energy intensity of agriculture Although it has been predicted that GDP share of agriculture would decrease, the absolute amount will increase. The intensity of electricity consumption in Kyrgyzstan is high compared to transitional countries, but also to developed European countries. Heat intensity is on developed European countries level. In accordance, the intensity of electricity consumption is declining and the intensity of heat consumption is increasing compared to the starting year. Figure 19. The intensity of electricity consumption in agriculture 32

33 Figure 20. The intensity of heat consumption in agriculture Assessment of the final energy consumption in agriculture Together with electricity and heat energy, motor fuel consumption in agriculture is also dominant, mostly consumption of diesel for tractors. The need for motor fuel in agriculture is determined by the structural analysis compared to developed countries (relation of transport diesel in agriculture against the total energy consumption of motor fuel in some countries). Compared to 2012, the consumption of the agriculture sector will double until Motor fuels will also have a dominant share in energy consumption. 33

34 Figure 21. Final energy consumption forecast in agriculture by 2035 Table 17. Final energy consumption forecast in agriculture by 2035 Item Unit Traditional fuels PJ 0,000 0,000 0,000 0,000 0,000 0,000 Modern biomass PJ 0,000 0,000 0,000 0,000 0,000 0,000 Electricity PJ 0,795 0,824 0,893 1,006 1,137 1,276 Solar PJ 0,000 0,000 0,002 0,004 0,009 0,021 Fossil fuels PJ 0,084 0,177 0,253 0,378 0,566 0,838 Motor fuels PJ 3,056 3,447 3,969 4,748 5,693 6,774 Total Agriculture PJ 3,936 4,448 5,117 6,137 7,405 8,909 LPG PJ 0,000 0,000 0,000 0,000 0,000 0,000 Coal and Coal Pr. PJ 0,000 0,000 0,000 0,000 0,000 0,000 Other Petroleum Pr. PJ 0,000 0,000 0,000 0,000 0,000 0,000 Natural Gas PJ 0,084 0,177 0,253 0,378 0,566 0,838 Fossil fuels PJ 0,084 0,177 0,253 0,378 0,566 0, Assessment of the final energy consumption in transport sector Energy intensity of transport sector For making a precise MAED transport model, detailed and reliable statistical database is needed. Unfortunately, published statistical data on traffic of Kyrgyzstan were not sufficient. Consequently, reconstruction was made where data model was unavailable. The main determinants of future energy consumption in freight and passenger traffic are also presented. 34

35 Tonne-kilometres are in principle bound to GDP of industry, including agriculture. Parallel to the growth of total GDP, GDP in the service sector also grows; increase of tonne-kilometre is a little bit slower than the growth of total GDP. However, total tonne-kilometres determined for Kyrgyzstan in 2012 were low. According to Figure 15, it is evident that freight transport of countries in transition is on freight transport of developed countries level, although there is a big difference in economic development. By 2035, further increase in the number of cars is expected, because the number of persons per car will decrease from 8 to 4.5. With a bigger number of cars, total passenger kilometres will also rise, and their amount per capita will rise rapidly. By 2035, the fuel consumption of gasoline motorcars is expected to reach 6 l per 100 km, and that of diesel motorcars 4.5 l per 100 km. These are the amounts that will soon be reached by newly manufactured motorcars in the European Union. Figure 22. Tonne-kilometres per capita in Kyrgyzstan and the European countries 35

36 Figure 23. Persons per car in Kyrgyzstan and the European countries It has been noticed that in the countries in transition with slower economic development the number of vehicles increases faster than in the countries which have already gone through that development way. Therefore, shortly before 2035 Kyrgyzstan will have the same number of personal vehicles at the level of 2,000 USD 2005 /per capita as Denmark when it was at the level of 15,000 USD In this baseline scenario the further growth of personal vehicles which are using LPG is predicted. The occurrence of buses which use compressed natural gas is also predicted, because a certain number and types of city transport lines are more competitive than transport diesel buses. By 2035, they would make 3 % of total passenger kilometres of public city transportation. Assessment of the final energy consumption in transport sector An increase of nearly 2.5 times is expected in energy consumption of the transport sector. Gasoline cars will retain a dominant share in the total number of cars, with the share of LPG and CNG cars increased by 5 %. Freight traffic would develop according to the growth of the economy. Under the assumption that by % tkm would be transported via railway, diesel and electric pull would account for roughly the same share. Diesel will remain a dominant fuel in the future. CNG consumption values are expected to reach 100 million m 3 by

37 Figure 24. Final energy consumption forecast in transport Table 18. Final energy consumption forecast in transport Item Unit Electricity PJ 0,000 0,047 0,267 0,563 1,042 1,794 Diesel PJ 23,663 30,875 37,399 47,208 59,037 72,601 Gasoline PJ 31,896 35,121 40,961 47,592 54,220 60,970 LPG+CNG PJ 0,000 0,282 0,644 1,207 2,064 3,389 Total PJ 55,559 66,324 79,270 96, , , Final energy consumption in households Energy intensities Along with the population growth, population in urban areas will rather significantly increase. It is expected that, according to the experience and situation in developed countries, the household size in Kyrgyzstan will evidently decrease, from 3.9 household members today to 3.3 in

38 Figure 25. Household size in Kyrgyzstan and the European countries This will result in the increase of number of households by The floor area of an average housing unit will also increase. In a long-term period, the size of a floor area will move towards 76 m 2. 38

39 Figure 26. Average residential floor area in Kyrgyzstan and the European countries A rising standard of living will be accompanied by a rise in the heated area. In 2035 the share of newly built housing units will be considerable. This will have a powerful impact on improved thermal insulation of the housing fund, because new buildings will have much better thermal properties than the existing ones. Envisaged reduction of heat losses is in line with the reduction achieved by European countries in the last thirty years. 39

40 Figure 27. Household heat losses in Kyrgyzstan and the European countries Electricity consumption for non-heating purposes is assumed to have a high growth rate in the region up to 3000 kwh per household, and a very slow growth is expected afterwards, slightly above 3000 kwh per household. 40

41 Figure 28. Non-thermal electricity consumption per household in Kyrgyzstan and the European countries Assessment of the final energy consumption in households Firewood, coal and electricity are the primary sources of energy in Kyrgyzstan. It is expected that by 2035, natural gas would account for a 17 % share in the space heating in households. The final energy consumption in households would increase by 2.22 times by 2035, despite the presumed relative improvements in heat insulation in the housing stock of 15 % until then. Electrical energy consumption would increase from 4287 kwh per household to 6060 kwh. Natural gas consumption would reach 620 million m 3 by Figure 29. Final energy consumption forecast in households by 2035 Table 19. Final energy consumption forecast in households by 2035 Item Unit Traditional fuels PJ 12,560 13,664 15,293 17,539 19,910 22,532 Modern biomass PJ 0,000 0,000 0,000 0,000 0,000 0,000 Electricity PJ 22,187 25,302 29,924 35,600 41,257 46,908 41

42 District heat PJ 7,829 9,012 10,943 13,593 16,628 20,188 Solar PJ 0,000 0,000 0,000 0,000 0,000 0,000 Fossil fuels PJ 17,920 19,655 23,993 30,074 36,931 44,819 Total Households PJ 60,496 67,633 80,154 96, , ,447 LPG PJ 0,485 0,507 0,551 0,598 0,640 0,679 Coal and Coal Pr. PJ 13,398 13,977 15,471 17,605 19,744 21,986 Other Petroleum Pr. PJ 0,353 0,468 0,580 0,737 0,923 1,146 Natural Gas PJ 3,684 4,704 7,392 11,133 15,625 21,008 Fossil fuels PJ 17,920 19,655 23,993 30,074 36,931 44, Final energy consumption in the service sector Energy intensities The size of the service sector will rise from 3.3 m 2 during the long-term period to 5.3 m 2 per capita. In spite of a moderate decrease in thermal and electric energy consumption intensity, the effective energy needs in this sector will continue to rise due to rising GDP services and the total floor area. Figure 30. Electricity intensity in services 42

43 Figure 31. Heat intensity in services Assessment of the final energy consumption in service sector The service sector is very dynamic in terms of energy consumption. The fundamental determinant is the area taken up by the service sector, in case of Kyrgyzstan, it is expected to grow from 3.3 m 2 per person to 5.3 m 2 by

44 Figure 32. Final energy consumption forecast in services by 2035 Energy consumption would increase by nearly 2 times. Electricity remains dominant, while natural gas would increase its share in the structure of the fossil fuel consumption. By 2035, natural gas would make up 35 % of heating demands, i.e. an 18 % share in the final consumption (180 million m 3 ). Table 20. Final energy consumption forecast in services by 2035 Item Unit Traditional fuels PJ 0,000 0,000 0,000 0,000 0,000 0,000 Modern biomass PJ 0,000 0,000 0,000 0,000 0,000 0,000 Electricity PJ 9,239 10,837 12,466 14,297 16,535 19,153 District heat PJ 1,884 2,037 2,333 2,681 3,042 3,433 Solar PJ 0,000 0,000 0,000 0,000 0,000 0,000 Fossil fuels PJ 5,066 5,788 7,128 8,649 10,251 11,999 Total Services PJ 16,190 18,662 21,927 25,626 29,828 34,585 LPG PJ 0,000 0,000 0,000 0,000 0,000 0,000 Coal and Coal Pr. PJ 2,303 2,438 2,706 3,003 3,282 3,559 Other Petroleum Pr. PJ 2,052 2,060 2,087 2,120 2,125 2,116 Natural Gas PJ 0,712 1,290 2,334 3,526 4,845 6,324 Fossil fuels PJ 5,066 5,788 7,128 8,649 10,251 11,999 44

45 4.6 Total final energy consumption By 2035, total final energy consumption in Kyrgyzstan would increase by 2.45 times, while electricity consumption would increase by nearly 2.3 times. The economy will then be 3.7 times bigger. Electricity consumption per person is expected to increase from 1951 kwh to 3476 kwh. Natural gas would represent a 12 % share of the final energy consumption, or 1570 million m3. In terms of consumption per sector, industry share is expected to grow slower than the share of transport and households. Figure 33. Total final energy consumption forecast by

46 Figure 34. The structure of a forecasted total final energy consumption by 2035 Table 21. Total final energy consumption forecast by 2035 Item Unit Traditional fuels PJ 12,560 13,664 15,293 17,539 19,910 22,532 Modern biomass PJ 0,000 0,000 0,000 0,000 0,000 0,000 Electricity PJ 39,381 45,749 54,451 64,957 76,611 89,404 District heat PJ 10,341 11,907 14,619 18,400 23,045 28,938 Solar PJ 0,000 0,000 0,002 0,004 0,009 0,021 Fossil fuels PJ 40,226 46,146 57,347 72,233 90, ,968 Motor fuels PJ 59,085 70,303 83, , , ,764 Coke PJ 0,000 0,000 0,000 0,000 0,000 0,000 Feedstock PJ 3,433 3,950 4,544 5,227 6,013 6,918 Total PJ 165, , , , , ,545 LPG PJ 0,485 0,507 0,551 0,598 0,640 0,679 Coal and Coal Pr. PJ 27,507 29,863 34,377 40,283 46,911 54,236 Other Petroleum Pr. PJ 3,358 3,669 4,111 4,699 5,396 6,226 Natural Gas PJ 8,876 12,107 18,308 26,654 37,054 49,827 Fossil fuels PJ 40,226 46,146 57,347 72,233 90, ,968 46

47 Table 22. The structure of a forecasted total final energy consumption by 2035 Item Unit Industry PJ 28,847 34,652 43,529 55,041 69,757 87,849 Agriculture PJ 3,936 4,448 5,117 6,137 7,405 8,909 Transportation PJ 55,559 66,324 79,270 96, , ,754 Freight transp. PJ 15,895 19,062 23,212 30,062 38,595 48,400 Passenger transp. PJ 39,664 47,262 56,058 66,509 77,767 90,354 Household PJ 60,496 67,633 80,154 96, , ,447 Service PJ 16,190 18,662 21,927 25,626 29,828 34,585 Total PJ 165, , , , , , Structure of the final electricity consumption in Kyrgyzstan The growth of industrial consumption is the fastest one in the estimated structure of electricity consumption. Consumption in households and services will also grow more than twice. Table 23. Structure of the final electricity consumption within sectors (TWh) Sector Agriculture 0,221 0,229 0,248 0,280 0,316 0,354 Industry 1,989 2,428 3,028 3,747 4,623 5,631 Transport 0,000 0,013 0,074 0,156 0,289 0,498 Household 6,163 7,028 8,312 9,889 11,460 13,030 Service 2,567 3,010 3,463 3,971 4,593 5,320 TOTAL 10,939 12,708 15,125 18,044 21,281 24,834 47

48 Figure 35. The structure of a forecasted electricity final consumption 48