Technical Assistance for Capacity Building in Compilation and Analysis of the Fuel and Energy Balance for the Kyrgyz Republic (AHEF 113.KG) FINAL REPORT

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 2011/278827

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 http://www.inogate.org

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

Name Date Prepared by Damir Pešut 01/12/2014 Alenka Kinderman Lončarevid

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 http://www.inogate.org

Contents

1 Executive summary...... 9 2 Data collection and availability ...... 10 2.1 Input data for short-term energy demand forecast ...... 10 2.1.1 Air temperature ...... 10 2.1.2 Gross Domestic Product (GDP) ...... 12 2.1.3 Natural Gas ...... 13 2.1.4 Electricity ...... 13 2.1.5 Heat ...... 14 2.2 Input data for medium and long-term energy demand forecast ...... 16 2.2.1 Population ...... 16 2.2.2 Gross Domestic Product (GDP) ...... 18 2.2.3 Final energy consumption ...... 18 3 Short-term energy demand forecast for the period 2014-2017 ...... 20 3.1 Assessment of electricity demand for the period 2014-2017 ...... 21 3.2 Assessment of heat demand for the period 2014-2017...... 24 4 Long-term energy demand forecasts for the period 2014 – 2035 - baseline scenario ... 29 4.1 Assessment of the final energy consumption in industry ...... 29 4.2 Assessment of the final energy consumption in agriculture ...... 32 4.3 Assessment of the final energy consumption in the transport sector ...... 34 4.4 The final energy consumption in households ...... 37 4.5 The final energy consumption in the service sector ...... 42 4.6 Total final energy consumption ...... 45 4.7 Structure of the final electricity consumption in Kyrgyzstan ...... 47 5 Long-term energy demand forecasts for the period 2014 – 2035 - scenario with measures ...... 49 1.1 Measures in industry and agriculture ...... 49 1.2 Measures in transport ...... 50 1.3 Measures in households ...... 51 1.4 Measures in the service sector ...... 53 1.5 Total final energy consumption in the scenario with measures ...... 54 1.6 Structure of the final electricity consumption in Kyrgyzstan in the scenario with

measures ...... 56 1.7 Total energy efficiency and total savings in the final consumption ...... 57 6 Annex 1. Assessment of the short-term electricity demand for DSOs...... 58 6.1 Sever Electro ...... 58 6.2 Vostok Electro...... 59 6.3 Osh Electro ...... 60 7 Annex 1. Assessment of the short-term heat demand ...... 61 7.1 Bishkek Teploset ...... 61 7.2 Bishkek Teploenergo ...... 62

List of figures

Figure 1. Comparison of average monthly temperatures among the selected cities: Bishkek, Chuy, Jalal Abad, Osh ...... 11 Figure 2. Comparison of average annual HDD for cities: Bishkek, Chuy, Jalal Abad and Osh ...... 12 Figure 3. Average shares of GDP realised by months in the total annual GDP for the period 2006 - 2013 ...... 13 Figure 4. Electricity consumption structure in Kyrgyzstan in 2013 according to the reports from DSOs ...... 14 Figure 5. Heat distribution in 2013 in Bishkek Teploset, Bishkek Teploenergo i Кыргызжилкоммунсою ...... 15 Figure 6. The structure of heat consumption in Kyrgyzstan ...... 16 Figure 7. Population in Kyrgyzstan in the period 1950 – 2100 (Source: UN Population Division) ...... 17 Figure 8. Comparison between realized consumption and estimated total electricity consumption ...... 22 Figure 9. Influence of model parameters on total electricity consumption in Kyrgyzstan ...... 22 Figure 10. Results of the adjustment of the model for short-term electricity demand projections ...... 22 Figure 11. Total electricity demand forecast for the period 2014 - 2017 ...... 24 Figure 12. Comparison between realized consumption and estimated total heat consumption ...... 26 Figure 13. Influence of model parameters on total heat consumption ...... 26 Figure 14. Results of the adjustment of the model for short-term projections of heat...... 26 Figure 15. Total heat demand forecast for the period 2014 - 2017 for the average year ...... 28 Figure 16. The intensity of electricity consumption in industry ...... 29 Figure 17. The intensity of heat consumption in industry ...... 30 Figure 18. Final energy consumption forecast in industry by 2035 ...... 31 Figure 19. The intensity of electricity consumption in agriculture ...... 32 Figure 20. The intensity of heat consumption in agriculture ...... 33 Figure 21. Final energy consumption forecast in agriculture by 2035 ...... 34 Figure 22. Tonne-kilometres per capita in Kyrgyzstan and the European countries ...... 35 Figure 23. Persons per car in Kyrgyzstan and the European countries ...... 36 Figure 24. Final energy consumption forecast in transport ...... 37 Figure 25. Household size in Kyrgyzstan and the European countries ...... 38 Figure 26. Average residential floor area in Kyrgyzstan and the European countries ...... 39 Figure 27. Household heat losses in Kyrgyzstan and the European countries...... 40 Figure 28. Non-thermal electricity consumption per household in Kyrgyzstan and the European countries ...... 41 Figure 29. Final energy consumption forecast in households by 2035 ...... 41 Figure 30. Electricity intensity in services ...... 42 Figure 31. Heat intensity in services ...... 43 Figure 32. Final energy consumption forecast in services by 2035...... 44 Figure 33. Total final energy consumption forecast by 2035 ...... 45 Figure 34. Structure of the forecasted total final energy consumption by 2035 ...... 46 Figure 35. Structure of the forecasted final electricity consumption ...... 48 Figure 36. Specific consumption of new cars and stock average (EU) ...... 51 Figure 37. Heat losses in households in the scenario with measures ...... 52 Figure 38. Total final energy consumption forecast in the scenario with measures ...... 54 Figure 39. Structure of total final energy consumption forecasts in the scenario with measures ...... 55

List of tables

Table 1. Average monthly temperatures for the city of Bishkek ...... 11 Table 2. Thirty-year averages of monthly air temperatures for the selected cities ...... 11 Table 3. Population in Kyrgyzstan in the period 2010 – 2012 (Source: World Development Indicators) ...... 17 Table 4. Population in KG in the period 2012 – 2035 (Source: Estimates by the ITS experts) ...... 17 Table 5. Total GDP and structure by economic activities ...... 18 Table 6. Final energy consumption in 2012 (Extract from IEA energy balance; www.iea.org) ...... 18 Table 7. Final energy consumption in 2012 (reconstructed) ...... 19 Table 8. Monthly electricity consumption in the period 2006 -2013 in Kyrgyzstan ...... 21 Table 9. Electricity consumption assessment for the period 2014-2017 in average weather conditions ...... 23 Table 10. Electricity consumption assessment for the period 2014-2017 in extremely warm weather conditions ...... 23 Table 11. Electricity consumption assessment for the period 2014-2017 in extremely cold weather conditions 23 Table 12. Heat distribution in the period 2006 -2013 in thousand Tcal ...... 25 Table 13. Heat consumption assessment for the period 2014-2017 in average weather conditions ...... 27 Table 14. Heat consumption assessment for the period 2014-2017 in extremely warm weather conditions ..... 27 Table 15. Heat consumption assessment for the period 2014-2017 in extremely cold weather conditions ...... 27 Table 16. Final energy consumption forecast in industry by 2035 ...... 31 Table 17. Final energy consumption forecast in agriculture by 2035 ...... 34 Table 18. Final energy consumption forecast in transport ...... 37 Table 19. Final energy consumption forecast in households by 2035 ...... 41 Table 20. Final energy consumption forecast in services by 2035 ...... 44 Table 21. Total final energy consumption forecast by 2035 ...... 46 Table 22. Structure of the forecasted total final energy consumption by 2035 ...... 47 Table 23. Structure of the final electricity consumption by sectors (TWh) ...... 47 Table 24. Savings with regard to the baseline scenario in industry ...... 50 Table 25. Savings with regard to the baseline scenario in transport ...... 51 Table 26. Savings with regard to the baseline scenario in households ...... 53 Table 27. Savings with regard to the baseline scenario in services ...... 53 Table 28. Total final energy consumption forecast in the scenario with measures ...... 55 Table 29. Structure of total final energy consumption forecast in the scenario with measures ...... 55 Table 30. Savings in the scenario with measures with regard to the baseline scenario ...... 56 Table 31. Structure of the final electricity consumption forecast in the scenario with measures (TWh) ...... 56

Table 32. Total savings of final energy consumption by 2035 ...... 57 Table 33. Sever Electro: Total electricity consumption in the period 2006 -2013 ...... 58 Table 34. Sever Electro: Assessment of electricity consumption in average weather conditions ...... 58 Table 35. Sever Electro: Assessment of electricity consumption in extremely cold conditions ...... 58 Table 36. Sever Electro: Assessment of electricity consumption in extremely warm conditions ...... 58 Table 37. Vostok Electro: Total electricity consumption in the period 2006 -2013 ...... 59 Table 38. Vostok Electro: Assessment of electricity consumption in average weather conditions...... 59 Table 39. Vostok Electro: Assessment of electricity consumption in extremely cold conditions...... 59 Table 40. Vostok Electro: Assessment of electricity consumption in extremely warm conditions ...... 59 Table 41. Osh Electro: Total electricity consumption in the period 2006 -2013 ...... 60 Table 42. Osh Electro: Assessment of electricity consumption in average weather conditions ...... 60 Table 43. Osh Electro: Assessment of electricity consumption in extremely cold weather conditions ...... 60 Table 44. Osh Electro: Assessment of electricity consumption in extremely warm conditions...... 60 Table 33. Bishkek Teploset: Total heat consumption in the period 2006 -2013 ...... 61 Table 34. Bishkek Teploset: Assessment of heat consumption in average weather conditions ...... 61 Table 35. Bishkek Teploset: Assessment of heat consumption in extremely cold conditions ...... 61 Table 36. Bishkek Teploset: Assessment of electricity consumption in extremely warm conditions ...... 61 Table 37. Bishkek Teploenergo: Total heat consumption in the period 2006 -2013 ...... 62 Table 38. Bishkek Teploenergo: Assessment of heat consumption in average conditions ...... 62 Table 39. Bishkek Teploenergo: Assessment of heat consumption in extremely cold conditions ...... 62 Table 40. Bishkek Teploenergo: Assessment of heat consumption in extremely warm conditions ...... 62

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 ben- eficiary 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 2035. 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 2014-2017, 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.

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 1st Workshop at the Ministry of Energy and Industry which was held in the period 24th-25th July, 2014. Within the first report, prepared after the 1st 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 Elec- tro; - 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.

2.1.1 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.

Table 1. Average monthly temperatures for the city of Bishkek

°C Month Jan Feb Mar April May June July Aug Sept Oct Nov Dec 2006 -9,32 3,32 9,13 14,43 18,45 23,03 24,45 25,19 18,03 14,74 6,03 -2,90 2007 -3,55 1,64 4,77 15,77 18,81 24,37 26,29 24,68 19,50 9,52 6,03 -5,03 2008 -12,13 -4,66 10,19 13,07 21,45 25,90 27,23 25,19 18,07 11,71 4,73 -1,45 2009 -3,81 1,04 7,48 11,37 17,23 22,37 25,23 23,94 17,83 12,35 4,30 -0,42 2010 -1,42 -3,32 6,81 13,10 17,90 23,43 25,13 25,42 18,47 13,55 7,00 -2,90 2011 -6,61 -1,68 4,13 14,70 19,16 23,83 25,71 24,97 19,60 12,48 2,30 -6,03 2012 -8,00 -7,34 3,61 16,47 19,10 23,57 25,32 25,23 19,47 12,42 2,10 -6,71 2013 -1,23 -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 , 1 3 14,43 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 , 7 7 15,77 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 , 4 8 11,37 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

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

2.1.2 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 2000–2013 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 2006–2013.

Figure 3. Average monthly shares of GDP in total annual GDP for the period 2006 - 2013

2.1.3 Natural Gas

Kyrgizgaz did not deliver data on natural gas consumption.

2.1.4 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 2006–2013 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

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 long- term 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 2006. 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.

2.1.5 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 2000–2013, while Кыргызжилкоммунсою provided data only for the years 2011, 2012, 2013 and 2014. It is assumed

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.

Figure 6. The structure of heat consumption in Kyrgyzstan

2.2 Input data for medium- and long-term energy demand forecast

2.2.1 Population

According to UN Population Division (UNPD), the estimated population in Kyrgyzstan in the past 60 years (1950-2010) and forecasts of the population growth by 2100 are shown in the following table and diagram.

Table 1. Population in Kyrgyzstan in the period 1950 – 2100 (Source: UN Population Division)

Year 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 Population, 000 1740 1902 2173 2573 2964 3299 3627 4013 4395 4592 4955

Year 2005 2010 2015 2020 2025 2025 2030 2035 2040 2045 2050 Population, 000 5042 5334 5708 6162 6557 6557 6871 7145 7429 7720 7976

Year 2055 2060 2065 2070 2075 2080 2085 2090 2095 2100 2100 Population, 000 8167 8304 8415 8523 8631 8726 8802 8856 8894 8924 8924

Population (000) - UN forecast 10000 9000 8000 7000 6000 5000 4000 3000 2000 1000 0 1940 1960 1980 2000 2020 2040 2060 2080 2100 2120

Figure 7. Population in Kyrgyzstan in the period 1950 – 2100 (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 2010 – 2012 (Source: World Development Indicators)

Year 2010 2011 2012 Population 5.447.900 5.514.600 5.607.200

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 2015-–2035, 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 2012 – 2035 (Source: Estimates by the ITS experts)

Item Unit 2012 2015 2020 2025 2030 2035 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

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 2025–2035, are ITS expert’s estimates based on the 6 % GDP growth assessment.

Table 5. Total GDP and structure by economic activities

Item Unit 2012 2015 2020 2025 2030 2035 GDP* *bill US$2005+ 3,209 3,874 4,999 6,700 9,000 12,000 GDP gr. rate* *%p.a.+ 6,485 5,229 6 6 6 GDP/cap US$ 572 669 811 1022 1310 1679 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 4,3

2.2.3 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; www.iea.org)

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 2012. 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)

Petroleum Natural District 2012 ktoe Coal Electricity Firewood TOTAL Products Gas Heating Final consumption 657 1584 213 941 247 300 3942 Household 320 20 88 530 187 300 1445 Industry 282 34 105 171 15 607 Agriculture 73 2 19 94 Transport 1327 0 0 1327 Service 55 49 17 221 45 387 Non-energy use 81 1 82

3 Short-term energy demand forecast for the period 2014-2017

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 (tav). 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

have a different effect.

3.1 Assessment of electricity demand for the period 2014-2017

Total electricity consumption for every month in the period 2006-2013 is summarized in the following table.

Table 8. Electricity consumption in the period 2006 -2013 in Kyrgyzstan shown in months

GWh Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total

2006 957,7 795,9 668,5 508,5 403,5 373,0 382,6 381,1 360,2 433,5 598,3 878,8 6.741,6 2007 889,5 785,8 743,8 492,3 423,0 391,6 391,5 390,8 387,4 591,8 757,0 870,1 7.114,5 2008 1.015,6 964,1 670,4 478,7 407,6 403,6 404,5 383,1 370,6 433,6 632,9 654,2 6.818,8 2009 721,0 649,1 619,0 523,4 478,1 431,3 432,3 428,6 429,1 500,1 693,7 835,4 6.741,2 2010 798,0 738,7 634,0 505,2 435,4 414,9 415,2 419,8 420,1 496,8 723,1 937,2 6.938,3 2011 1.122,9 934,2 794,2 621,7 481,6 462,3 467,0 473,5 459,4 566,0 964,9 1.191,4 8.539,2 1.263, 1.148, 2012 1.274,8 640,2 515,6 483,3 480,8 500,1 469,6 618,1 962,1 1.268,7 9.625,2 6 3 1.189, 1.010, 1.097, 2013 1.334,4 721,2 575,7 522,2 533,5 531,6 519,5 624,4 1.332,4 9.993,0 9 8 5

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 2006- 2013 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 1.400

1.200

1.000

800

600

400

200

0 2006 2007 2008 2009 2010 2011 2012 2013

Figure 8. Comparison between realized consumption and estimated total electricity consumption

GDP Temperature Season 1.400

1.200

1.000

800

600

400

200

0 2006 2007 2008 2009 2010 2011 2012 2013

Figure 9. Influence of model parameters on the total electricity consumption in Kyrgyzstan

Distribution of the difference between observed and calculated values 100 80 60 40 20 0 -20 -40 -60 -80 -100 2006 2007 2008 2009 2010 2011 2012 2013

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 2014-2017 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.

Table 9. Electricity consumption assessment for the period 2014-2017 for average weather conditions

GWh Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total 2014 986 1.033 980 752 633 612 633 625 681 783 1.022 1.195 9.935 2015 1.009 1.060 1.011 782 665 648 674 665 732 827 1.064 1.246 10.383 2016 1.034 1.089 1.044 815 701 686 718 709 787 875 1.110 1.303 10.870 2017 1.061 1.121 1.080 852 740 728 767 756 847 927 1.160 1.364 11.402

Table 10. Electricity consumption assessment for the period 2014-2017 for extremely warm weather conditions

GWh Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total 2014 931 935 863 633 633 612 633 625 681 631 906 1.085 9.168 2015 953 961 894 663 665 648 674 665 732 675 948 1.137 9.615 2016 978 991 927 696 701 686 718 709 787 723 993 1.194 10.103 2017 1.005 1.022 963 732 740 728 767 756 847 775 1.043 1.255 10.634

Table 11. Electricity consumption assessment for the period 2014-2017 for extremely cold weather conditions

GWh Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total 2014 1.154 1.132 1.097 879 781 612 633 625 819 921 1.138 1.304 11.095 2015 1.176 1.159 1.127 909 814 648 674 665 870 965 1.180 1.356 11.542 2016 1.201 1.188 1.161 942 849 686 718 709 925 1.013 1.226 1.412 12.030 2017 1.228 1.220 1.197 978 888 728 767 756 985 1.065 1.276 1.474 12.562

Consumption 2006-2013, MWh Assessments for 2014 - 2017, MWh 12.000.000 14.000.000

12.000.000 10.000.000

10.000.000 8.000.000 8.000.000 6.000.000 6.000.000 Very cold 4.000.000 Cold 4.000.000 Average Warm 2.000.000 2.000.000 Very warm

0 0 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Figure 11. Total electricity demand forecast for the period 2014 - 2017

Consumption projections for individual distribution systems companies are shown in Annex 1.

3.2 Assessment of heat demand for the period 2014-2017

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 2006-2010 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.

Table 12. Heat distribution in the period 2006 -2013 in thousand Tcal

Tcal Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total 2006 305,7 190,5 158,8 73,3 26,8 45,2 76,3 72,6 75,4 82,2 154,3 271,3 1.532,6 2007 266,7 204,0 198,4 73,0 27,6 44,7 58,0 57,2 57,9 72,8 167,5 263,1 1.491,0 2008 311,4 244,6 133,7 57,8 25,8 30,8 51,7 52,2 52,5 52,9 157,1 225,7 1.396,2 2009 241,1 196,8 158,1 53,8 23,5 31,1 50,7 50,8 51,8 52,6 150,1 238,7 1.299,0 2010 221,4 227,3 126,4 49,7 23,3 26,3 44,4 44,3 45,9 47,1 137,8 227,2 1.221,3 2011 277,6 213,4 197,5 48,2 23,7 31,9 43,6 44,1 46,3 59,3 196,0 270,4 1.451,9 2012 286,9 251,3 193,7 47,8 26,4 24,2 42,1 43,0 44,4 45,6 184,8 275,4 1.465,6 2013 243,6 213,1 144,5 47,1 24,6 25,3 42,0 42,0 44,1 58,0 179,3 236,8 1.300,5

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 2006-2013, 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.

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

Table 13. Heat consumption assessment for the period 2014-2017 for average weather conditions

Gcal Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total 2014 223,9 206,0 167,3 58,2 16,6 23,5 41,5 41,6 39,5 56,7 154,2 221,1 1.250,3 2015 222,7 204,5 165,6 56,5 14,8 21,5 39,2 39,4 36,7 54,2 151,8 218,2 1.225,1 2016 221,3 202,8 163,7 54,6 12,8 19,4 36,7 36,9 33,6 51,5 149,2 215,0 1.197,5 2017 219,7 201,0 161,7 52,6 10,6 17,0 33,9 34,2 30,2 48,6 146,4 211,6 1.167,5

Table 14. Heat consumption assessment for the period 2014-2017 for extremely warm weather conditions

Gcal Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total 2014 206,0 174,3 129,8 19,9 16,6 23,5 41,5 41,6 39,5 8,1 116,9 186,1 1.003,9 2015 204,8 172,8 128,1 18,2 14,8 21,5 39,2 39,4 36,7 5,6 114,5 183,1 978,6 2016 203,4 171,1 126,2 16,3 12,8 19,4 36,7 36,9 33,6 2,9 111,9 180,0 951,1 2017 201,9 169,4 124,1 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 2014-2017 for extremely cold weather conditions

Gcal Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total 2014 277,6 237,7 204,8 98,9 64,2 23,5 41,5 41,6 83,9 101,2 191,5 256,2 1.622,7 2015 276,3 236,2 203,1 97,2 62,4 21,5 39,2 39,4 81,1 98,7 189,1 253,3 1.597,4 2016 274,9 234,5 201,2 95,3 60,4 19,4 36,7 36,9 77,9 96,0 186,6 250,1 1.569,9 2017 273,4 232,7 199,2 93,3 58,2 17,0 33,9 34,2 74,5 93,1 183,7 246,6 1.539,9

The analysis of heat energy consumption in the period 2006-2013 shows that the trend of total consumption is negative and that consumption is gradually reducing every year. The model for short- term 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 2014 - 2017 for the average year

Consumption projections for individual heat distribution companies are shown in Annex 2.

4 Long-term energy demand forecasts for the period 2014 – 2035 - 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 USD2005, 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

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 m3 of the total final consumption in industry.

Figure 18. Final energy consumption forecast in industry by 2035

Table 16. Final energy consumption forecast in industry by 2035

Item Unit 2012 2015 2020 2025 2030 2035 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

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

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 2035. Motor fuels will also have a dominant share in energy consumption.

Figure 21. Final energy consumption forecast in agriculture by 2035

Table 17. Final energy consumption forecast in agriculture by 2035

Item Unit 2012 2015 2020 2025 2030 2035 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 0,838

4.3 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.

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

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 USD2005/per capita as Denmark when it was at the level of 15,000 USD2005. 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 2035 25 % 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 m3 by 2035.

Figure 24. Final energy consumption forecast in transport

Table 18. Final energy consumption forecast in transport

Item Unit 2012 2015 2020 2025 2030 2035 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 96,571 116,363 138,754

4.4 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 2035.

Figure 25. Household size in Kyrgyzstan and the European countries

This will result in the increase of number of households by 2035. 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 m2.

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.

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.

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 m3 by 2035.

Figure 29. Final energy consumption forecast in households by 2035

Table 19. Final energy consumption forecast in households by 2035

Item Unit 2012 2015 2020 2025 2030 2035 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

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 96,805 114,725 134,447 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 44,819

4.5 Final energy consumption in the service sector

Energy intensities The size of the service sector will rise from 3.3 m2 during the long-term period to 5.3 m2 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

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 m2 per person to 5.3 m2 by 2035.

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 m3).

Table 20. Final energy consumption forecast in services by 2035

Item Unit 2012 2015 2020 2025 2030 2035 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

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 2035

Figure 34. The structure of a forecasted total final energy consumption by 2035

Table 21. Total final energy consumption forecast by 2035

Item Unit 2012 2015 2020 2025 2030 2035 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 90,001 110,968 Motor fuels PJ 59,085 70,303 83,742 101,820 122,488 145,764 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 165,027 191,718 229,998 280,181 338,078 404,545 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 90,001 110,968

Table 22. The structure of a forecasted total final energy consumption by 2035

Item Unit 2012 2015 2020 2025 2030 2035 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 96,571 116,363 138,754 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 96,805 114,725 134,447 Service PJ 16,190 18,662 21,927 25,626 29,828 34,585 Total PJ 165,027 191,718 229,998 280,181 338,078 404,545

4.7 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 2011 2015 2020 2025 2030 2035 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

Figure 35. The structure of a forecasted electricity final consumption

5 Long-term energy demand forecasts for the period 2014–2035 - scenario with measures

The scenario with measures assumes that together with state’s interventions and activities, the institutional framework will be built (legal-regulatory and organizational) for providing additional energy activities in the whole energy system. This also implies the formation of economic and fair energy prices as a ground for establishment of Energy Service Companies (ESCO) for further action to reduce the consumption of energy and other energy products. The assumed framework also enables the business enterprises to be engaged in activities and to improve their own energy efficiency with the help and organization of national and regional energy agencies or centres. In case of establishment of special instruments and organizations, such as, energy efficiency funds, these effects may be greater.

1.1 Measures in industry and agriculture

In scenarios without measures it is assumed that production lines based on proven and applicable technology solutions will be the bases for growth in industrial activities. This means that the technical level of energy efficiency of new industrial processes is already at a relatively high level in the baseline scenario. Thus, energy intensity and consumption of electricity and heat in industry will gradually decrease in scenarios without measures by 2035, as a result of structural changes, increased quality and value of industrial products and technical improvements in energy efficiency, i.e. market mechanisms. This also includes the increased level of energy technology outputs for generation of thermal energy, increased share of cogeneration in generation of electricity and heat energy where biomass is also foreseen as a fuel, and certain use of solar radiation energy in the food industry. In 2012, energy consumption in the processing industry was not small. The electricity consumption was 2 TWh, and by 2035 it will be increased by about three times. It is clear that all these industry plants will use new technologies, given that it is almost certain that private investors will make investments. Of course, most of the new production lines will be imported from developed countries. Given that over the next twenty years the processing industry is completely new, significantly important measures have not been designed in this sector of the economy. The improvements by 2020 are foreseen just in intensity of electricity (5 %) and in intensity of heat energy consumption (10 %). One of the measures in the processing industry is encouraging the use of industrial wood waste as a substitute for fossil fuels and fuel for cogenerations, which in the initial calculations of energy balance has shown as fuel wood. In the baseline scenario it is assumed that by 2035, 10 % of heat at average-temperature level will be generated from industrial cogenerations, and in the scenario with measures, it was estimated that with special incentives this generation will be 5 % higher, and the share of biomass in cogeneration will also reach 5 %. In agriculture, it is estimated that with incentives, 2 % of energy used for heating purposes will be generated directly from biomass. The following table shows savings in the final energy consumption in the scenario with measures with regard to baseline scenario. In 2025 those savings would reach the highest relative decrease of 4.5 %.

Table 24. Savings with regard to the baseline scenario in industry

Item Unit 2012 2015 2020 2025 2030 2035 Traditional fuels PJ Modern biomass PJ -0,026 -0,079 -0,272 -1,011 Electricity PJ 0,312 0,623 0,580 0,440 0,456 District heat PJ 0,073 0,036 0,000 Solar PJ Fossil fuels PJ 0,238 0,620 2,190 2,254 2,938 Motor fuels PJ Coke PJ Feedstock PJ Total MAN PJ 0,000 0,550 1,217 2,763 2,457 2,383 % 0,00 1,41 2,50 4,52 3,18 2,46

1.2 Measures in transport

For all countries in transition, and for Kyrgyzstan, the level of technical efficiency in industrial technology processes and means of transportation are directly related to the quality of those technologies and means of transportation that are imported from or produced under license in economically more developed countries. Energy efficiency can be improved even further by institutional, legislative and organizational measures. The reduction or motor fuels consumption in private vehicles, in baseline scenarios without measures, is the result of technological progress in the economically developed world, from where the vehicles are imported. As an alternative to the former, licensed production in the region is organised. The average consumption per 100 km in the EU today is only 6.7 litres of fuel. New vehicles consume 5.4 litres of fuel. These trends are therefore already included in scenarios without measures.

Figure 36. Specific consumption of new cars and stock average (EU) Furthermore, transport policy that supports public transport and increased use of alternative energy sources (liquid petroleum gas - LPG and compressed natural gas - CNG) and electricity in transport is envisaged. In the sector of transport in Kyrgyzstan, the effects can be achieved by transport policy that directs most of freight traffic to railways. Baseline scenario assumes that by 2030, 25 % of freight traffic will be diverted to railways and, in scenario with measures, this has increased to 35 %. In the sector of transport the sets of measures that are not capital intensive and which achieve sensitive effects can be organized. In High and Medium scenarios with measures the following measures are designed: eco-driving, bonus-malus system, and speed limit on highways. It was estimated that by designing individual measures, energy consumption in the sector of transport would be reduced up to 6 % by 2020, and even up to 11 % by 2030. More intensive substitution of diesel buses with buses that use CNG is foreseen, where natural gas would be available, and gradual increase of share of biodiesel as a fuel for buses. Furthermore, it is foreseen that the share of buses in urban passenger transport will grow up to 60 %, according to the scenario with measures, and respectively up to 42 % according to the baseline scenario. It is assumed that until 2035 the “eco-drive” and similar measures will decrease the motor fuel consumption of personal cars by 5 %. The following table shows savings of the final energy consumption in the transport sector in the scenario with measures with regard to baseline scenario. In 2035 those savings would reach the highest relative decrease by 12,4 %.

Table 25. Savings with regard to the baseline scenario in transport

Item Unit 2012 2015 2020 2025 2030 2035 Electricity PJ -0,020 -0,068 -0,160 -0,315 -0,595 Diesel PJ 0,028 0,179 0,812 2,326 5,760 Gasoline PJ 1,173 2,878 4,845 6,978 9,238 Total PJ 0,000 1,181 2,988 5,497 8,989 14,403 % 2,1 4,5 6,9 9,3 12,4

1.3 Measures in households

The largest share of energy consumption in households is related to space heating, and the greatest reduction in energy consumption in households can be achieved through specific activities which relate to the improvement of thermal insulation and heating systems. In this matter the possibilities for reducing heat losses of newly constructed buildings and residential buildings constructed by 2012 can be distinguish. Heat losses in newly constructed buildings can be defined by laws and regulations, and the monitoring of their compliance to the laws and regulations is easier to implement in the newly constructed residential buildings than in newly constructed houses. Future reduction of heat losses of existing housing stock is the most difficult task, but at the same time it is the biggest potential for action. In the scenario with measures, implementation of very strict regulations on thermal insulation of residential buildings is envisaged. For the newly constructed buildings it is assumed that after 2012 a

regulation on heat losses of only 150 kWh per square meter of heating surface will be applied. For existing housing stock or old houses, it is assumed that each year 33,000 housing units should be rehabilitated from 2015 onwards. Of course, this requires legislative and organizational preparation that includes financial incentives. It is estimated that the improvement of heat losses per rehabilitated resident unit is 50 %. By 2035, 660,000 resident units would be rehabilitated, i.e. nearly 30 % of the housing stock in 2035. As it is expected that the newly constructed buildings would comprise 38 % of the total housing stock, it is presumed that 2/3 of the housing stock would have noticeably better thermal insulation. The overall impact of described improvements of thermal insulation from 2012 to 2035 leads to reduction of heat losses by 35 %.

Figure 37. Heat losses in households in scenario with measures

Despite the assumption of significant improvements in thermal insulation of the housing stock, the useful thermal consumption for space heating will rise because the total number of dwellings will also rise, but also because the share of average heated floor area will rise, from 25 % up to 50 %. In the baseline scenario very intensive electricity consumption used for space heating, hot water preparation and cooking is foreseen. That intensity could be mitigated with the policy of household gasification. While the household gasification by 2035 is foreseen to be 17 % in the baseline scenario, according to the scenario with measures, it would be 25 %. In the scenario without measures growth of electricity for non-heating purposes in the household sector is envisaged, but in a manner which takes into account technical progress in terms of household appliances. It was estimated that non-thermal energy consumption per household, without technical progress, would be higher by 400 kWh in 2035. However, with measures on the demand side (DSM - Demand Side Management) and grade labelling of household appliances consumption, it is possible to even further reduce this consumption in the same period. This mainly 52

refers to acceleration of introducing more efficient appliances by promotion and incentives that refer to replacement of old and conventional technologies with the new ones that are more efficient. This usually refers to energy saving light bulbs, old refrigerators and freezers and washing machines. Today, there are available noticeably more efficient household appliances on the market than those which households own, and they are gradually replacing the old ones. With measures of promotion and incentives, the process of replacing household appliances is accelerating. Measures can be implemented by distribution system operators and state, regional or local agencies or centres for energy efficiency and renewable energy. It is estimated that by 2035, non-thermal energy consumption per household would be reduced by 200 kWh with such measures.

Table 26. Savings with regard to the baseline scenario in households

Item Unit 2012 2015 2020 2025 2030 2035 Traditional fuels PJ 0,012 0,046 1,067 2,662 4,790 Modern biomass PJ 0,000 -0,087 -0,222 -0,540 -1,281 Electricity PJ 0,315 1,019 4,005 7,297 10,571 District heat PJ 0,030 0,106 0,774 2,059 4,002 Fossil fuels PJ 0,155 0,635 0,213 1,538 4,991 Total Households PJ 0,512 1,720 5,838 13,015 23,073 % 0,8 2,1 6,0 11,3 17,2

1.4 Measures in the service sector

Given that major construction of new buildings is expected in services by 2035 almost doubling the total area compared to 2012, which will have growth of heating standards as a consequence according to the scenario with measures in comparison with the scenario without measures, additional measures to improve thermal insulation would reduce energy for space heating by 15 % by 2035. Regarding the consumption of electricity for non-thermal purposes, it is estimated that by applying DSM measures it would be possible to reduce this consumption by 5 % by 2035. These results can be achieved through good organization of activities of distribution network operator and all types of energy agencies, including implementation through ESCO companies. With organized actions, these results can be achieved also in commercial and, especially, in public service sector.

Table 27. Savings with regard to the baseline scenario in services

1.5 Total final energy consumption in the scenario with measures

In the scenario with measures, the total final energy consumption will grow by 2.2 times by 2035. The natural gas consumption will be higher, and the consumption of electricity will be lower than in the baseline scenario.

Figure 38. Total final energy consumption forecast in the scenario with measures

Figure 39. The structure of the total final energy consumption forecasts in the scenario with measures

Table 28. Total final energy consumption forecast in the scenario with measures

Item Unit 2012 2015 2020 2025 2030 2035 Traditional fuels PJ 12,560 13,653 15,247 16,472 17,248 17,742 Modern biomass PJ 0,000 0,000 0,113 0,301 0,812 2,292 Electricity PJ 39,381 45,135 52,771 60,071 68,347 77,703 District heat PJ 10,341 11,876 14,427 17,362 20,636 24,478 Solar PJ 0,000 0,000 0,002 0,004 0,009 0,021 Fossil fuels PJ 40,226 45,754 55,832 69,349 85,396 101,794 Motor fuels PJ 59,085 69,102 80,686 96,164 113,187 130,769 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 MAN PJ 165,027 189,470 223,622 264,949 311,648 361,716 LPG PJ 0,485 0,502 0,538 0,576 0,607 0,636 Coal and Coal Pr. PJ 27,507 28,712 31,296 33,322 35,950 38,171 Other Petroleum Pr. PJ 3,358 3,652 3,986 4,364 4,902 5,499 Natural Gas PJ 8,876 12,887 20,013 31,087 43,937 57,488 Fossil fuels PJ 40,226 45,754 55,832 69,349 85,396 101,794

Table 29. The structure of the total final energy consumption forecast in the scenario with measures

Item Unit 2012 2015 2020 2025 2030 2035 Industry PJ 28,847 34,102 42,312 52,277 67,298 85,458

Agriculture PJ 3,936 4,448 5,118 6,138 7,408 8,916 Transportation PJ 55,559 65,144 76,283 91,075 107,375 124,354 Freig. transp. PJ 15,895 19,012 23,051 29,443 36,848 43,951 Pass. transp. PJ 39,664 46,133 53,231 61,632 70,527 80,403 Household PJ 60,496 67,121 78,434 90,968 101,710 111,374 Service PJ 16,190 18,656 21,475 24,492 27,856 31,613 Total PJ 165,027 189,470 223,622 264,949 311,648 361,716

The comparison of the baseline scenario and the scenario with measures shows that the savings within total final energy consumption will be achieved. By 2035, savings according to the baseline scenario will amount to 10.6 %.

Table 30. Savings in the scenario with measures with regard to the baseline scenario

Item Unit 2012 2015 2020 2025 2030 2035 Industry PJ 0,550 1,218 2,764 2,459 2,391 Agriculture PJ Transportation PJ 1,181 2,988 5,497 8,989 14,403 Household PJ 0,512 1,720 5,838 13,015 23,073 Service PJ 0,007 0,452 1,135 1,971 2,973 Total PJ 2,249 6,377 15,233 26,432 42,832 % 1,2 2,8 5,4 7,8 10,6

1.6 Structure of the final electricity consumption in Kyrgyzstan in the scenario with measures

Electricity consumption in this scenario is, for 2035, 13 % lower than in the baseline scenario. It is the result of higher energy efficiency, but also the substitution of thermal electricity consumption with natural gas.

Table 31. Structure of the final electricity consumption forecast in the scenario with measures (TWh)

Sector 2011 2015 2020 2025 2030 2035 Agriculture 0,221 0,229 0,248 0,280 0,316 0,354 Industry 1,989 2,341 2,855 3,586 4,500 5,504 Transport 0,000 0,019 0,093 0,201 0,377 0,664 Household 6,163 6,941 8,029 8,777 9,433 10,094 Service 2,567 3,008 3,433 3,843 4,359 4,968 TOTAL 10,939 12,538 14,659 16,686 18,985 21,584

1.7 Total energy efficiency and total savings in the final consumption

Savings achieved in the scenario with measures with regard to the baseline scenario are savings that are arising from measures undertaken by the state and other authorities in Kyrgyzstan. However, savings are achieved in the baseline scenario too, because of the general technical development. Those savings are calculated as a difference between the “frozen scenario” and the baseline scenario. The frozen scenario is the baseline scenario in which all technical efficiencies in the future years will have been retained on the level of the base year (2012). Total savings in the final energy consumption consist of savings achieved in the baseline scenario and savings achieved in the scenario with measures with regard to the baseline scenario.

Table 32. Total savings of the final energy consumption by 2035

Unit 2012 2015 2020 2025 2030 2035 frozen-baseline PJ 0,591 8,683 20,650 37,858 61,785 baseline- measures PJ 2,249 6,377 15,233 26,432 42,832 total PJ 2,840 15,060 35,883 64,291 104,617

Unit 2012 2015 2020 2025 2030 2035 frozen-baseline % 0,3 3,6 6,9 10,1 13,2 baseline- measures % 1,2 2,7 5,1 7,0 9,2 total % 1,5 6,3 11,9 17,1 22,4

The baseline scenario contributes slightly more to total savings, and by 2035 total savings with regards to the frozen scenario would be 22.4 %, i.e. approximately 1 % per year.

6 Annex 1. Assessment of the short-term electricity demand for DSOs

6.1 Sever Electro

Table 33. Sever Electro: Total electricity consumption in the period 2006-2013

GWh Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total 2006 411,2 341,6 277,3 229,0 184,8 169,5 166,5 168,9 161,5 188,2 242,9 342,9 2.884,4 2007 357,7 328,5 299,9 213,5 186,0 178,1 174,5 181,5 190,3 294,4 359,5 379,8 3.143,6 2008 450,0 429,1 294,3 216,4 185,4 185,1 186,6 178,3 178,5 208,3 299,6 308,5 3.120,0 2009 328,6 296,8 278,6 241,9 217,0 197,0 196,4 195,0 204,4 237,6 312,7 382,5 3.088,6 2010 363,7 348,0 305,6 247,8 206,0 201,5 197,4 197,2 203,0 233,5 343,0 440,7 3.287,4 2011 530,5 431,0 339,5 303,8 226,4 217,3 216,1 219,0 217,8 260,3 457,6 560,3 3.979,6 2012 596,8 590,6 528,9 304,8 234,3 224,2 218,7 225,7 226,1 289,6 457,6 591,7 4.488,9 2013 627,9 573,7 475,9 349,8 267,7 244,7 241,3 244,1 248,2 286,3 500,4 602,5 4.662,6

Table 34. Sever Electro: Assessment of electricity consumption for average weather conditions

GWh Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total 2014 456 487 457 367 306 299 306 303 341 384 493 570 4.769 2015 468 501 473 383 323 318 327 324 368 408 515 597 5.006 2016 481 517 490 400 342 338 351 347 397 433 539 627 5.264 2017 496 534 510 420 362 360 377 373 429 461 566 660 5.546

Table 35. Sever Electro: Assessment of electricity consumption for extremely cold conditions

GWh Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total 2014 537 535 513 428 377 299 306 303 408 451 549 623 5.330 2015 549 549 529 444 395 318 327 324 435 475 571 650 5.567 2016 562 564 547 462 413 338 351 347 464 500 596 680 5.825 2017 576 581 566 481 434 360 377 373 496 528 622 713 6.106

Table 36. Sever Electro: Assessment of electricity consumption for extremely warm conditions

GWh Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total 2014 429 439 400 309 306 299 306 303 341 311 437 517 4.397 2015 441 454 416 325 323 318 327 324 368 334 459 544 4.634 2016 454 469 434 343 342 338 351 347 397 360 483 574 4.893 2017 469 486 453 362 362 360 377 373 429 387 510 607 5.174

6.2 Vostok Electro

Table 37. Vostok Electro: Total electricity consumption in the period 2006-2013

GWh Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total 2006 136,5 111,3 96,9 71,2 57,0 52,0 60,8 55,6 46,8 59,6 87,9 131,7 967,3 2007 133,3 111,3 103,5 69,2 58,5 52,5 55,8 56,6 44,5 75,0 97,3 128,2 985,6 2008 129,4 125,0 105,4 70,6 59,4 61,1 62,1 55,3 44,4 53,9 77,1 84,3 928,0 2009 97,5 86,4 91,0 68,3 68,9 60,4 63,5 62,0 53,5 62,1 97,1 110,3 921,0 2010 107,7 87,6 72,1 65,4 56,7 54,3 56,8 57,8 49,4 61,2 93,3 125,6 887,8 2011 154,0 123,4 116,5 77,6 61,8 59,1 60,6 63,0 53,0 75,6 120,4 141,7 1.106,7 2012 152,5 156,6 145,4 77,4 66,9 63,3 68,4 76,0 52,7 81,8 120,1 155,7 1.216,9 2013 162,6 146,0 125,5 81,2 75,2 66,1 73,8 71,8 58,4 76,1 139,6 173,2 1.249,4

Table 38. Vostok Electro: Assessment of electricity consumption for average weather conditions

GWh Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total 2014 133,3 131,7 127,5 92,5 80,6 77,1 83,0 81,7 76,6 94,0 127,2 153,8 1.258,9 2015 135,6 134,4 130,6 95,6 83,9 80,6 87,1 85,7 81,7 98,4 131,4 159,0 1.304,1 2016 138,0 137,3 134,0 98,9 87,5 84,5 91,6 90,2 87,3 103,3 136,1 164,7 1.353,4 2017 140,8 140,5 137,6 102,6 91,4 88,7 96,5 95,0 93,4 108,5 141,1 170,9 1.407,1

Table 39. Vostok Electro: Assessment of electricity consumption for extremely cold conditions

GWh Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total 2014 149,4 141,2 138,8 104,7 95,0 77,1 83,0 81,7 89,9 107,4 138,4 164,4 1.371,0 2015 151,7 143,9 141,9 107,8 98,2 80,6 87,1 85,7 95,1 111,8 142,7 169,6 1.416,3 2016 154,2 146,9 145,3 111,2 101,8 84,5 91,6 90,2 100,6 116,7 147,3 175,3 1.465,5 2017 156,9 150,1 148,9 114,8 105,7 88,7 96,5 95,0 106,7 121,9 152,4 181,5 1.519,2

Table 40. Vostok Electro: Assessment of electricity consumption for extremely warm conditions

GWh Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total 2014 127,9 122,1 116,2 80,9 80,6 77,1 83,0 81,7 76,6 79,3 116,0 143,2 1.184,7 2015 130,2 124,8 119,3 84,0 83,9 80,6 87,1 85,7 81,7 83,8 120,2 148,5 1.229,9 2016 132,7 127,8 122,7 87,4 87,5 84,5 91,6 90,2 87,3 88,6 124,8 154,2 1.279,1 2017 135,4 131,0 126,3 91,0 91,4 88,7 96,5 95,0 93,4 93,9 129,9 160,4 1.332,8

6.3 Osh Electro

Table 41. Osh Electro: Total electricity consumption in the period 2006-2013

GWh Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total 2006 200,3 168,8 147,9 97,0 73,3 69,9 71,4 73,1 73,1 90,8 136,5 211,7 1.413,9 2007 203,8 174,0 177,5 101,8 85,9 75,3 75,5 67,2 67,8 92,8 134,5 171,5 1.427,6 2008 213,8 198,9 123,9 86,8 73,6 69,0 67,3 65,6 66,6 76,5 117,7 118,1 1.277,9 2009 136,9 123,7 113,8 98,6 87,5 79,4 77,8 77,8 77,3 90,9 132,1 159,7 1.255,7 2010 151,9 141,4 117,5 81,4 77,4 68,2 70,1 72,9 75,7 93,3 128,6 165,7 1.244,1 2011 192,6 175,3 164,3 104,2 88,0 84,7 88,1 87,8 88,1 106,2 175,6 228,5 1.583,4 2012 246,4 239,8 222,5 117,8 101,6 90,0 88,5 88,9 88,0 111,4 173,8 243,6 1.812,1 2013 251,7 209,7 188,1 132,2 106,7 97,1 101,6 99,3 99,1 125,3 217,2 265,0 1.893,2

Table 42. Osh Electro: Assessment of electricity consumption for average weather conditions

GWh Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total 2014 195,3 201,8 193,6 137,9 117,5 111,5 115,5 113,0 125,3 144,2 192,4 233,5 1.881,5 2015 199,3 206,5 199,0 143,3 123,2 117,7 122,7 120,1 134,3 151,9 199,8 242,7 1.960,5 2016 203,7 211,7 204,9 149,1 129,5 124,5 130,5 127,8 144,0 160,4 207,9 252,6 2.046,7 2017 208,4 217,3 211,3 155,5 136,3 131,9 139,1 136,2 154,7 169,6 216,7 263,5 2.140,5

Table 43. Osh Electro: Assessment of electricity consumption for extremely cold weather conditions

GWh Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total 2014 227,3 220,7 216,0 162,2 145,9 111,5 115,5 113,0 151,8 170,7 214,6 254,4 2.103,5 2015 231,3 225,4 221,4 167,5 151,6 117,7 122,7 120,1 160,7 178,4 222,1 263,6 2.182,5 2016 235,7 230,6 227,2 173,4 157,9 124,5 130,5 127,8 170,5 186,9 230,2 273,6 2.268,7 2017 240,4 236,2 233,6 179,8 164,7 131,9 139,1 136,2 181,1 196,1 239,0 284,4 2.362,6

Table 44. Osh Electro: Assessment of electricity consumption for extremely warm conditions

GWh Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total 2014 184,7 182,9 171,2 115,1 117,5 111,5 115,5 113,0 125,3 115,2 170,1 212,6 1.734,5 2015 188,7 187,6 176,6 120,4 123,2 117,7 122,7 120,1 134,3 122,9 177,6 221,8 1.813,6 2016 193,0 192,8 182,5 126,3 129,5 124,5 130,5 127,8 144,0 131,4 185,7 231,7 1.899,7 2017 197,7 198,4 188,9 132,7 136,3 131,9 139,1 136,2 154,7 140,6 194,5 242,6 1.993,6

7 Annex 1. Assessment of the short-term heat demand

7.1 Bishkek Teploset

Table 45. Bishkek Teploset: Total heat consumption in the period 2006-2013

Gcal Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total 2006 305,7 190,5 158,8 73,3 26,8 45,2 76,3 72,6 75,4 82,2 154,3 271,3 1532,6 2007 266,7 204,0 198,4 73,0 27,6 44,7 58,0 57,2 57,9 72,8 167,5 263,1 1491,0 2008 311,4 244,6 133,7 57,8 25,8 30,8 51,7 52,2 52,5 52,9 157,1 225,7 1396,2 2009 241,1 196,8 158,1 53,8 23,5 31,1 50,7 50,8 51,8 52,6 150,1 238,7 1299,0 2010 221,4 227,3 126,4 49,7 23,3 26,3 44,4 44,3 45,9 47,1 137,8 227,2 1221,3 2011 277,6 213,4 197,5 48,2 23,7 31,9 43,6 44,1 46,3 59,3 196,0 270,4 1451,9 2012 286,9 251,3 193,7 47,8 26,4 24,2 42,1 43,0 44,4 45,6 184,8 275,4 1465,6 2013 243,6 213,1 144,5 47,1 24,6 25,3 42,0 42,0 44,1 58,0 179,3 236,8 1300,5

Table 46. Bishkek Teploset: Assessment of heat consumption for average weather conditions

Table 47. Bishkek Teploset: Assessment of heat consumption for extremely cold conditions

Table 48. Bishkek Teploset: Assessment of electricity consumption for extremely warm conditions

GWh Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total 2014 206,0 174,3 129,8 19,9 16,6 23,5 41,5 41,6 39,5 8,1 116,9 186,1 1.003,9 2015 204,8 172,8 128,1 18,2 14,8 21,5 39,2 39,4 36,7 5,6 114,5 183,1 978,6 2016 203,4 171,1 126,2 16,3 12,8 19,4 36,7 36,9 33,6 2,9 111,9 180,0 951,1 2017 201,9 169,4 124,1 14,3 10,6 17,0 33,9 34,2 30,2 0,0 109,1 176,5 921,1

7.2 Bishkek Teploenergo

Table 49. Bishkek Teploenergo: Total heat consumption in the period 2006-2013

Gcal Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total 2006 23,47 14,88 11,29 4,86 1,58 2,72 4,69 4,61 4,29 4,39 9,85 19,60 106,25 2007 19,52 14,98 12,64 4,32 1,71 0,65 3,49 4,96 4,81 4,88 12,96 20,52 105,43 2008 24,76 17,33 9,53 4,92 1,51 2,72 4,68 4,51 4,64 4,42 14,38 18,03 111,44 2009 19,15 14,98 11,16 4,94 1,75 3,16 4,69 4,70 4,31 4,85 14,09 18,54 106,32 2010 18,97 19,13 8,95 4,44 1,66 2,17 2,88 3,74 3,85 4,18 12,06 19,04 101,07 2011 22,59 17,90 13,52 3,86 1,37 0,67 2,94 3,10 2,54 4,80 16,24 21,28 110,80 2012 22,69 20,62 15,05 3,77 1,88 0,44 2,21 3,21 4,00 4,17 15,53 21,99 115,57 2013 19,32 17,96 9,97 1,84 1,73 1,97 3,65 3,20 4,02 5,72 15,03 20,00 104,41

Table 50. Bishkek Teploenergo: Assessment of heat consumption for average conditions

Gcal Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total 2014 18,2 16,8 12,4 4,9 1,6 1,8 3,6 4,0 4,0 5,3 13,7 18,5 104,7 2015 18,2 16,8 12,4 4,9 1,6 1,8 3,6 4,0 4,0 5,3 13,7 18,5 104,6 2016 18,2 16,8 12,4 4,9 1,6 1,8 3,6 3,9 4,0 5,3 13,7 18,5 104,5 2017 18,2 16,8 12,4 4,8 1,6 1,7 3,6 3,9 4,0 5,3 13,7 18,5 104,4

Table 51. Bishkek Teploenergo: Assessment of heat consumption for extremely cold conditions

Gcal Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total 2014 22,4 19,3 15,3 8,0 5,3 1,8 3,6 4,0 7,5 8,8 16,6 21,2 133,8 2015 22,4 19,3 15,3 8,0 5,3 1,8 3,6 4,0 7,4 8,8 16,6 21,2 133,7 2016 22,4 19,3 15,3 8,0 5,3 1,8 3,6 3,9 7,4 8,8 16,6 21,2 133,6 2017 22,3 19,3 15,3 8,0 5,3 1,7 3,6 3,9 7,4 8,8 16,6 21,2 133,4

Table 52. Bishkek Teploenergo: Assessment of heat consumption for extremely warm conditions

Gcal Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total 2014 16,8 14,4 9,5 1,9 1,6 1,8 3,6 4,0 4,0 1,5 10,8 15,8 85,5 2015 16,8 14,4 9,5 1,9 1,6 1,8 3,6 4,0 4,0 1,5 10,8 15,8 85,4 2016 16,8 14,3 9,4 1,9 1,6 1,8 3,6 3,9 4,0 1,5 10,8 15,7 85,3 2017 16,8 14,3 9,4 1,9 1,6 1,7 3,6 3,9 4,0 1,5 10,7 15,7 85,1