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Long-term energy scenarios for Estonia (DRAFT)

DATA REPORT

04-01-2013

Published by:

Ea Energy AnalysesFrederiksholms Kanal 4, 3. th.1220 Copenhagen KDenmarkT: +45 88 70 70 83F: +45 33 32 16 61Email: [email protected]: www.eaea.dk

2 | Long-term energy scenarios for Estonia, data report - draft

Contents

1 Introduction and background................................................................5

2 The Balmorel model..............................................................................7

3 Electricity grid infrastructure and transmissions..................................12

3.1 The electricity grid infrastructure....................................................12

3.2 Interconnector development...........................................................13

4 The existing energy system of the Baltic Sea region.............................17

4.1 Input data for Balmorel....................................................................17

4.2 Estonia.............................................................................................18

4.3 Lithuania..........................................................................................20

4.4 Latvia................................................................................................22

4.5 Poland..............................................................................................25

4.6 Finland.............................................................................................26

4.7 NW Russia........................................................................................27

4.8 Denmark..........................................................................................28

4.9 Norway.............................................................................................29

4.10 Sweden............................................................................................30

4.11 Germany..........................................................................................31

5 Investments in new generation...........................................................33

5.1 Exogenous investments...................................................................33

5.2 Endogenous investments.................................................................34

6 General assumptions..........................................................................38

6.1 Fuel and CO2 prices.........................................................................38

6.2 Electricity demand...........................................................................40

6.3 District heating demand...................................................................40

6.4 Implementation of renewable energy targets.................................41


7 Renewable energy potentials..............................................................43

7.1 Biomass potentials...........................................................................43

7.2 Municipal solid waste......................................................................45

7.3 Wind power.....................................................................................46

7.4 Solar power......................................................................................47

Appendix A: Assumptions about the development of energy consumption till 2050...........................................................................................................48

Economic development...........................................................................49

Energy intensity indicators......................................................................50

Forecasting the demand for energy service............................................53

Changes in end-use technologies............................................................55

Results.....................................................................................................59

Appendix B: Assessment of the potential for wind power in the BSR..........60

Onshore wind potential...........................................................................60

Off-shore wind potential.........................................................................63

Appendix C: Detailed Estonian data............................................................65

Appendix D: Detailed Latvian data..............................................................70

Appendix E: Detailed Lithuanian data.........................................................75

Appendix F: Detailed data on transmission and demand.............................83


1 Introduction and backgroundTJEK VINDSTUDIE RAPPORTERMODEL OUTPUT PÅ ALLE MULIGE FORUDSÆTNINGERThis project is part of the Estonian long-term energy strategy (ENMAK) that includes the period from now to 2030 and a vision to 2050. In order to explore Estonian energy strategies of the future, a number of scenarios are set up to show the economic consequences of different policy options and their implications for the energy systems, the environment and security of supply. The scenarios will be measured against a reference scenario.

The BALMOREL model will be used to simulate the different pathways for the Estonian combined heat and power system towards 2050. The model area will include the Baltic Sea Region, i.e. all three Baltic States, the Nordic countries, Germany, Poland and NW Russia.

For the whole model area for electricity and district heating we suggest, as a starting point, to use the data set as described in the ‘Energy Policy Strategies of the Baltic Sea Region for the Post-Kyoto Period’ (see references) project carried out by Ea Energy Analyses and published in the spring of 2012 for the intergovernmental organisation BASREC. All policy assumptions in this study here have been discussed with a group of government officials from all Baltic Sea Region countries. This data set includes existing generation capacity, development in electricity and district heating demand, cost of technology (generation and transmission), implementation of national renewable energy action plans towards 2020, and a decided investment plan for the development in nuclear power capacity towards 2050.

Scenario analyses are used to explore how the power markets may evolve in the future to comply with strategic targets to reduce CO2 emissions in the region. The intention is to show a least cost strategy – however, keeping in mind, that developing a least cost strategy is dependent on a number of uncertain factors such as the future fuel prices and the technological development of key technologies.

This data reportThe Balmorel model will be used to simulate the future energy system in the scenario analysis. Using this energy model it is possible to analyse various


Approach

Focus on electricity and district heating

scenarios for the development of the electricity and district heating supply in the region. The model is e.g. able to optimise investments in new production and transmission capacities from different assumptions – e.g. the electricity and heat demand, CO2 price, fuel prices, existing generation system, transmission links and investment costs.

This data report will document the data and assumptions used for the analysis and serves as the background for setting up an energy model for analysing the future energy system of the Baltic Sea Region with emphasis on Estonia.


2 The Balmorel modelThe quantitative analyses are made with BALMOREL, which is a least cost dispatch power system model. The model is based on a detailed technical representation of the existing power system; power and heat generation facilities as well as the most important bottlenecks in the overall transmission grid. The main result in this case is a least cost optimisation of the production pattern of all power units. The model, which was originally developed with a focus on the countries in the Baltic region, is particularly strong in modelling combined heat and power production.

In addition to simulating the dispatch of generation units, the model allows investments to be made in different new generation units (coal, gas, wind, biomass etc.) as well as in new interconnectors.

A limit is imposed on the potential to expand grid connections for each five year period. This limit is 1,000 MW on sea cables and 3,000 MW for grid reinforcement on land – except in Germany where the limit is 6,000 MW. These limitations are included to ensure a gradual development of the grid in the region.

The BALMOREL model is myopic in its investment approach, in the sense that it does not explicitly consider revenues beyond the year of installation. This means that investments are undertaken in a given year if the annual revenue requirement (ARR) in that year is satisfied by the market.

A balanced risk and reward characteristic of the market are assumed, which means that the same ARR is applied to all technologies, specifically 0.12, which is equivalent to 10 % internal rate for 20 years. This rate should reflect an investor’s perspective.

In practice, this rate is contingent on the risks and rewards of the market, which may be different from technology to technology. For instance, unless there is a possibility to hedge the risk without too high risk premium, capital intensive investments such as wind or nuclear power investments may be more risk prone. This hedging could be achieved via, feed-in tariffs, power purchase agreements or a competitive market for forwards/futures on electricity, etc.


Investment approach

It should be stressed that the recommended socio-economic discount rate in many countries is significantly lower than the 10 % rate applied in the present study (Germany: 2.2 %, Sweden and Norway: 4 %, Denmark and Finland: 5 %, UK: 1.0-3.5 %, EU: 3.5-5.5 %1). Applying a lower discount rate would favour capital intensive technologies like wind power, nuclear power and solar power as opposed to for example gas power plants. Estonia is using xx %.p.a.

The model will be set up to analyse 2012-2020 with yearly intervals, hereafter 2020-2030 are analysed with two year intervals and the period 2030-2050 is analysed in five year intervals.

In the work-in-progress-phase the yearly time resolution will be set to 4x6 time steps and in the final scenarios this resolution will be increased to 12x6 time steps.

BALMOREL is also capable of reflecting political framework conditions such as taxes and quotas and to assess the economic consequences for different stakeholder groups such consumers, producers, grid owners, countries or the region as a whole.

The analyses are carried out by the use of the Balmorel model, which is an economic and technical partial equilibrium model that simulates the power and heat markets.

The model optimises the production at existing and planned production units (chosen by the user) and allows new investments in the scenarios, chosen by the model on a cost minimising basis.

1 European Commission (2008): Guide to Cost-Benefit Analysis of investmentProjects; Concito (2011): Den samfundsøkonomiske kalkulationsrente – fakta og etik


Time resolution

Economic analysis

Figure 1: Map of the transmissions grid in the Baltic Sea Region (Source: Nordel)

The model contains data for the electricity and combined heat and power (CHP) systems in the Nordic countries (Denmark, Finland, Norway and Sweden), the Baltic countries (Estonia, Latvia and Lithuania), Poland, Germany and North West Russia.

The model considers the most important bottlenecks in the electricity systems. Norway consists of four electric areas with capacity constraints between them. Sweden consists of three areas, Denmark two, Germany three, Poland five, Russia eight, whereas Estonia, Latvia, Lithuania and Finland consist of one area each.

The model permits specification of geographically distinct entities. The types of geographical entities are Areas, Regions, and Countries.

Each country is constituted of one or more regions while each region contains zero or more areas. Any area must be included in exactly one region, and any region must be included in exactly one country. The areas are the building blocks with respect to the geographical dimension. Thus, for instance all


Geographical scope

generation and generation capacities are described at the level of areas, and so are all aspects of heat demand.

Countries

Regions

Areas

Electricaltransmission

Region Region

Figure 2: The geographical entities.

Electricity balances are given on a regional basis. Hence, for each region an electricity balance must be fulfilled, but unlike heat, electricity may be exchanged between regions. Such transmission, and their constraints, losses and costs, are the motivation for the concept of regions. A number of regions constitute a country.

The country does not have any generation or consumption apart from that which follows as the sum over the regions in the country. However, a number of characteristics may be identical for all entities (e.g. fuel prices and taxes) in a country. A country is constituted of more than one region when needed to represent bottlenecks in the electricity transmission system within the country.

The table below shows the geographical division of countries and regions in the model.


Country Region Place

Germany DE_CS Germany – Central and South

DE_NE Germany – North EastDE_NW Germany – North West

Denmark DK_E Denmark – EastDK_W Denmark – West

Finland FI_R FinlandNorway NO_M Norway – middle

NO_N Norway – NorthNO_O Norway – Oslo-regionNO_S Norway – South

Sweden SE_M Sweden – middleSE_N Sweden – NorthSE_S Sweden - South

Poland PL_NW Poland – North WestPL_W Poland – WestPL_Central Poland – CentralPL_S Poland – SouthPL_SE Poland – South East

Lithuania LT_R all LithuaniaLatvia LV_R all LatviaEstonia EE_R all EstoniaNorth West Russia RU_KAR Russia -Karelia

RU_KOL Russia - LeningradRU_PSK Russia - PskovRU_KAL Russia - KaliningradRU_ARK Russia - ArkhangelskRU_STP Russia – St. PetersburgRU_NOV Russia - NovgorodRU_KOM Russia - Komi

Table 1: Overview of countries and regions in the model.


3 Electricity grid infrastructure and transmissions

3.1 The electricity grid infrastructureThe model of power systems includes restrictions on the power transmission capacity between different areas in the model area.The grid infrastructure in the Baltic Sea Region comprises the Baltic grid (Estonia, Latvia and Lithuania), the Northen West part of the Russian grid (including the Kaliningrad region), the Nordic grid (Denmark, Finland, Norway and Sweden), the German and the Polish grid.

The existing transmission capacities can be found in the excel appendix to this report.

The Baltic gridThe electricity systems of Estonia, Latvia and Lithuania are closely connected. They also have strong links to Russia, and to Belarus.

A single DC interconnector, Estlink is connecting the Estonia and Finland. No other interconnectors link the Baltic States to the Nordic electricity system or the UCTE system today.

The Russian gridThe Baltic countries, especially Estonia and Lithuania have strong interconnectors to the Russian and Belarusian system and are also operated synchronously with these two systems. Estonia and Latvia have interconnectors to the Western part of Russia, whereas Lithuania has interconnectors to Belarus and Kaliningrad.

The transmission capacity between the Baltic countries is sometimes limited by loop flows going from Belarus up through the Baltic countries and to the Western part of Russia or vice versa.

The Polish gridThe electricity system in Poland has connections to Germany, Sweden, Czech Republic and Slovakia. In addition, Poland has connections with Belarus and Ukraine, currently only one is in operation between PL-UA.


The Nordic gridThe Nordic electricity system is tied together with strong interconnectors between the different countries. Furthermore the Nordic countries are connected to Germany and Poland with both AC and DC interconnectors and to the Baltic and Russian grid by DC connections. The Nordic countries are one synchronous area except from Western Denmark which is synchronous with the European system (UCTE), including Germany and Poland.

3.2 Interconnector developmentThe starting point of the analyses is the existing transmission grid of the Baltic Sea Region. In addition to this the development towards 2020 is based on decided and planned projects. A method for the model to make investments in transmission beyond 2020 is also developed, which is applied for determining the transmission capacities for the period from 2020 to 2050.

Interconnector development towards 2020It is assumed that the five prioritized Nordic cross sections have all been established by 2015. The five prioritized Nordic cross sections are:

Fenno - Skan 2 linking Finland and Sweden Great Belt in Denmark Nea - Järpströmmen between Sweden and Norway South Link in Sweden Skagerrak 4 between Denmark and Norway

In addition the Cobra link between Denmark and the Netherlands is also assumed to be commissioned by 2017 with a capacity of 700 MW.

A significant reinforcement of the internal grid between the North West and Central parts of Germany will take place (2500 MW) to accommodate for the planned expansion of wind power in the northern parts of Germany, particularly offshore, is assumed by 2015.

The LitPol link between Poland and Lithuania - 500 MW will be operational in 2015 and 1000 MW will be operational in 2020.

A second connection (Estlink 2) between Estonia and Finland at 650 MW will be implemented by 2014.


Reinforcement of the Nordic grid

Reinforcement of the German grid

Lines between the central part of Norway and neighbouring areas in South are planned upgraded to strengthen the security of supply. The planned upgrade will also facilitate increased hydro power generation.

The table below depicts future interconnections that are to be implemented in the analyses in the short run.

Connection new Area Capacity(MW)

In operation Status

Great Belt West and East Denmark 600 2011 Established

Fenno-Skan 2 Sweden – Finland 800 2011 Establised

Skagerrak 4 Norway – Denmark +700 2014 Decided

Sydvästlänken 1 Sweden Central – Sweden S

1400 2016 ?

Sydvästlänken 2 Norway Oslo –Norway S 1400 2020 ?

Sydvästlänken 3 Norway – Sweden 1400 2020 Planned

Cobra Denmark-Holland 700 2017 Planned

Nea – Järpströmmen Norway - Sweden +300+150 2010 In operation

Denmark-Germany #1 West Denmark – Germany

+280+550 2013 Decided

Denmark-Germany #2 West Denmark – Germany

+1000+1550

2018 Planned

Nord.Link Norway-Germany 1400 2019 Planned

Estllink 2 Estonia-Finland +650 2014 Decided

LitPol Link Poland-Lithuania 500/+500 2015/2020 Decided

NordBalt Sweden-Lithuania 700 2016 Under construction

All existing transmission capacities are presented in the excel appendix to this report.

Table 2: Development of interconnectors in the region as used in the model.

In the study it is assumed that one new nuclear reactor of 1150 MW capacity is commissioned in Kaliningrad, Russia by 2017. In the same year we also assume that a connection of 500 MW is established from Kaliningrad to Poland.

Interconnector development beyond 2020A separate analysis on the cost of establishing new interconnectors in the region has been prepared for the project (Ea Energy Analyses 2012, Costs of transmission capacity in the Baltic Sea Region), which estimates the cost of


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the individual potential new transmission lines of the region. A generic method is developed which is used for determining the expansion of the grid for the period beyond 2020 for both internal reinforcements and new interconnectors.

The figure below illustrates a plot of investments costs and a length/capacity factor for different recent transmission project in the Baltic Sea Region.

0 50 100 150 200 250 300 3500

100

200

300

400

500

600

Capacity*Cable Length Factor (1000 MW*km)

Tota

l Inv

estm

ent C

osts

(mil-

lion

EUR)

Figure 3: Relationship between Total Investment Costs and Capacity*Cable Length factor.

It can be observed from the above graph that the higher the infrastructure needs are (i.e. the higher the Capacity* Cable Length factor), the higher the required Total Investment Costs. This linear relationship between the examined sizes indicates that economy of scale is probably rather limited in the case of interconnection projects, which therefore is assumed in the model.

The costs of all new transmission lines beyond 2020 are based on the costs of the HVDC LCC technology. The assumptions for the different components of an HVDC LCC Interconnection scheme are presented in Table 3 and Table 4.

HVDC InterconnectionsRated Capacity (Converter) 600 MW

Voltage Level (Cables) 400-500 kV

AC Reinforcements 15% of Total Investment Cost

Contigency 10% of Total Investment Cost

Table 3: Assumptions for HVDC connections in the Baltic Sea Region.

For the analysis AC reinforcements are considered equal to 15% of the Total Investment Costs. This percentage is estimated by calculating the AC reinforcements from existing and planned projects. Moreover contingency


costs are considered to be 10% of the total investment costs to account for the risk of unpredicted costs.

The reference capacity assessed is considered equal to a line with a capacity of 600 MW corresponding to cables of voltage level 400 to 500 kV.

HVDC LCC Technology Costs ReferenceConverter Substations Costs 0.16 million EUR/MW Average of Table 26

HVDC Submarine Cable Cost 0.77 million EUR/km CESI

Medium Voltage Submarine Return Cable Cost

0.15 million EUR/km CESI

HVDC Land Cable Cost 0.4 million EUR/km Ea

HVDC OHL Cost 0.35 million EUR/km CESI

Bay Cost 1.5 million EUR CESI, ICF-Norway

Table 4: Cost Assumptions for evaluation of HVDC LCC connections in the Baltic Sea Region.

For each possible new transmission line the elements in the above are calculated depending on e.g. length of line, if it is on on- or offshore and etc.

In the model a limit is imposed on the potential to expand grid connections for each 5 year period. This limit is 1000 MW on sea cables and 3000 MW for grid reinforcement on land - except in Germany where the limit is 6000 MW. These limitations are included to ensure a gradual development of the grid in the region.


4 The existing energy system of the Baltic Sea region

The data on power plants is based on the models inventory, which is continuously updated as decisions on the commissioning and decommission of power plants in the region are made.

4.1 Input data for BalmorelThis section describes specific technical data for the power plants in the Baltic countries. Furthermore, the exogenously defined power plant capacity is described for all countries in the model for the base scenario.

In the Balmorel model, the individual power stations or types of power stations (aggregated groups) are represented by different technical and economic parameters, e.g.

Technology type Type of fuel Capacity Efficiency Cb and Cv values for extraction and backpressure CHP plants Desulphurisation NOx emission coefficient Variable production Fixed annual production Investment costs

The fuel type could for instance be oil, natural gas or biomass. It is possible to specify any type of fuel in the model.

The capacities in the model are given as net capacities for either electricity or heat. For extraction units, the capacity is given as the electrical capacity in condensing mode; while for backpressure units it is given as the electricity capacity in co-generation mode.

In full cogeneration mode at CHP units, the Cb-value specifies the ratio between electricity and heat. For extraction units, the Cv-value specifies the loss in electricity when producing heat for maintained fuel consumption. The fuel efficiencies in the model are for CHP units given as the fuel efficiency in condensing mode for extraction units and the total fuel efficiency in CHP


mode for back pressure units. Fuel efficiencies are defined on an annual average basis.

In the tables below the generation capacities are shown for hydro power. In the model, the generation on the hydro plants are calculated using this capacity and a set of full load hours, which are given specifically for each region.

Data on existing plants on an aggregated level is presented below. The data regarding heat-only boilers is not represented in this overview. The detailed data, as in the model, is shown in the Excel appendix to this report. This contains detailed technical data for each power unit2.

4.2 EstoniaThe Estonian electricity generation mainly takes place at large oil shale power plants in the northern part of Estonia nearby the oil shale mines. The two largest plants are Eesti PP and Balti PP, which are owned and operated by AS Narva Elektrijaamad (Narva EJ).

Iru CHP is the largest producer of thermal energy in Estonia and supplies heat to approx. 50% of the city of Tallinn. Iru CHP is located in the outskirts of Tallinn and is a natural gas fired plant.

The Estonian power system is modelled with the requirement of one must-run unit. This requirement is set for one of the 11 Narva units (Eesti and Balti PP).

Description of Estonian power stations by model nameFor modelling purposes the power stations and refurbishments of power stations have been given a short name. The following section describes the names used for power stations in Estonia:

Eesti PP unit 1-8 (ST-Cond-4-S-PP1 - ST-Cond-4-S-PP8 and ST-CFBCon-SPP9)Existing oil shale fired units in Eesti PP, using pulverized combustion and working in the condensing mode. In 2015 a new unit (ST-CFBCon-SPP9) will be put into operation with a total capacity of 270 MW.

Balti PP unit 9-12 (ST-Cond-4-S-BPP9 - ST-Cond-4-S-BPP12)Existing oil shale fired units in Balti PP, using pulverized combustion technology.

2 If you require this appendix, you can send a request to [email protected]


Iru CHP 1 (ST-Ext-Iru-1-OG)Existing CHP extraction unit in Iru power plant. The power plant is gas-fired and is only available for 2007, and will hereafter be decommissioned.

Iru CHP 2 (ST-BP-Iru-2-OG)The IRU CHP 2 is a back pressure unit with a total capacity of 96 MW. It is an extraction unit, and is able to produce electricity without heat production.

Tartu CHP (ST-Ext-Tartu-1)Tartu CHP was commissioned in 2009 and has a total capacity of 25 MW.

Parnu CHP (ST-Ext-Parnu-1)Panu CHP will be put into operation 2011 and is an extraction unit.

Vao CHP (ST-Ext-Vao-1)Vao CHP was put into operation 2009. This unit can only run in back pressure mode.

Ahtme CHP (ST-Ext-Ahtme-1)Ahtme CHP is planned to be decommissioned in 2011.

The specific data for existing power stations and new technologies planned for commissioning or only available in Estonia are listed in appendix.

The following oil shale units at Narva have a yearly limitation due to sulphur emissions. The limit can be seen in the table below. All other Estonian oil shale units will have filters installed before 2013, and therefore have no such limitations.[this assumption should be updated with an assumption saying only four units have a limited operation of 17000 hours in 7 years]

Unit Full load hoursEesti PP, unit 1 2400Eesti PP, unit 2 2400Eesti PP, unit 7 2400Balti PP, unit 9 1100Balti PP, unit 10 1100Balti PP, unit 12 2400

Table 5: Full load hour limitation on oil shale plants


4.3 LithuaniaLithuania also has several thermal oil/gas plants including thermal CHP, some hydro power stations and one hydro storage plant. Lithuanian Power Plant (LPP) in Elektrenai is the largest thermal power station in Lithuania. The power station consists of eight units with a total capacity of approx. 1,800 MW. The first unit at LPP was commissioned in 1963, and two units have been refurbished in 1992 and 1994.

The Lithuanian power system is modelled with the requirement of two must-run units. One of the two Mazeikiai CHP units has must-run requirements as well as the Kaunas hydro plant. The Kaunas hydro plant is run-of-river plant, meaning that the must-run requirement is default for this unit.

Description of Lithuanian power stations by model nameFor modelling purposes the power stations and refurbishments of power stations have been given a short name. The following section describes the names used for power stations in Lithuania.

Kruonis (Kruonis-1 – 4)Kruonis hydro pump storage power station used for storage of electricity when there is an electricity surplus in the system. Kruonis HPSPP is especially useful in combination with the operation of INPP. Kruonis HPSPP has a pumping capacity of 800 MW (net) and a generation capacity of 760 MW (net).

Kaunas (Hydro-LT-1 - Hydro-LT-4)Kaunas hydro power station (Kaunas HPP) has a capacity of app. 90 MW (net), and is located in Kaunas. The generation costs at Kaunas HPP are low.

Lithuanian PPLithuanian power plant (LPP) is the largest thermal power station in Lithuania, and it is located in Elektrenai. The power station consists of eight units with a total capacity of app. 1,800 MW. The first unit at LPP was commissioned in 1963, and two units have been refurbished in 1992 and 1994. In 1999-2000 LPP unit 6 (300 MW) have been refurbished. The old operating and control system facilities was replaced, new burners in steam boilers was installed, replaced regulation system of steam turbine, and implemented desulphurisation equipment.


Lithuanian PP, unit 2 and 3 (ST-Ext-L1-G-1 and 2)Two CHP extraction units based on steam technology at Lithuanian Power plant. Possible to be fuelled by oil or gas.

Lithuanian PP, unit 1 (ST-Con-L2-G)Two condensing units based on steam technology at Lithuanian Power plant. Possible to be fuelled by oil or gas, 2 x 144 MW (net). Both units will decommission by 2010.

Lithuanian PP, unit 4 – 7 (ST-Con-L3-G-1-4 - 7) Four condensing units based on steam technology at Lithuanian Power plant. Possible to be fuelled by oil or gas. Two units have been refurbished in 1992 and 1994.

Lithuanian PP (new) (ST-Cond-LM-F1-G)Condensing units will be refurbished. They are expected to commission at the latest by end of year 2012. New control- and regulation systems will be set up.

Vilnius CHP-3, unit 1 and 2 (ST-Ext-V3-G-1 and 2)Vilnius CHP-3 was commissioned in 1983. It is a CHP extraction unit based on steam turbine technology and currently fired by natural gas. It has recently been refurbished - replacing control and regulating systems. The total capacity of Vilnius CHP-3 used in the model is 342 MW.

Vilnius CHP-2 (ST-BP-V2-G)Vilnius CHP-2, a backpressure unit based on steam turbine technology and fired by gas. It was commissioned in 1951, 11 MW (net). It has been reconstructed to be fired by wood waste. In parallel with reconstruction CHP-2 used the existing 12 MW because the reconstruction "only" consists of making a new boiler.

Industry back pressure CHP (ST-BP-O)Industry backpressure CHP based on oil. Industry producers generate energy just for their own needs and don’t transmit energy to the network. Just in exceptional cases there could be 1-2 MW range exchanges.

Kaunas CHP PT-60 and Kaunas CHP T-110 ( ST-Ext-K1-OG and ST-Ext-K2-OG)Kaunas CHP 1 and 2 consists of two CHP extraction units based on steam turbine technology. Mazaikiai CHP, unit 1 and 2 (ST-Ext-M-O-1 and 2)


Mazeikiai CHP is an extraction unit and was originally planned to supply a nearby oil refinery with heat. Today, the heat demand at the refinery has been reduced dramatically, and Mazeikiai is quite oversized. It has been considered to connect Mazeikiai CHP to the district heating system in the city, but this has not been found feasible so far.

The specific data for existing power stations and new technologies planned for commissioning or only available in Lithuania are listed in appendix to this report.

In the table below the RE electricity generation capacity is shown for Lithuania for 2015 and 2020. This data is based on the NREAP.

(MW/year) 2015 2020Hydropower 133 141

Solar photovoltaic 10 10

Onshore wind 389 500

Biomass 150 224

Solid biomass 115 162

Biogas 35 62

Of which CHP 150 224

Table 6: RE generation electricity capacity according to NREAP, Lithuania

4.4 LatviaThe Latvian electricity production is dominated by hydro power (run of river), representing about two-third of the installed power capacity. Other production forms are two CHP units located in the city of Riga.

The three major hydroelectric plants on the Daugava are Kegums Hydro Electrical Station (HES), Plavinas HES and Riga HES. Kegums HES consists of two hydroelectric plants with at total capacity of 264.1 MW. Plavinas HES consists of 10 hydroelectric units and was taken into operation in 1968 having a total capacity of 825 MW. Plavinas is the largest hydroelectric plant in the Baltic States by capacity. Plavinas has been partly renovated, and today the total capacity is 868.2 MW. Riga HES started operation in 1974 and consists of 6 hydroelectric units with a total capacity of 402 MW.

The Latvian power system is modelled without any must-run requirements.


Description of Latvian power stations by model nameFor modelling purposes the power stations and refurbishments of power stations have been given a short name. The following section describes the names used for power stations in Latvia:

Kegugums, Plavinas, Riga HES (Hydro-LV)Hydro LV represents the following three major hydroelectric plants on the Daugava: 1) Kegums Hydro Electrical Station (HES), 2) Plavinas HES, 3) Riga HES.

Planivas-HPP (Planivas-HPP)Plavinas HES consists of 10 hydroelectric units and was put into operation in 1968 with a total capacity of 825 MW. Plavinas is the largest hydroelectric plant in the Baltic States by capacity. Plavinas has been partly renovated, and today the total capacity is 868.2 MW.

Riga HPP (Riga-HPP)Riga HES started operation in 1974 and consists of 6 hydroelectric units with a total capacity of 402 MW.

Rigas TEC-1 (Rigas_TEC-1-GT and Rigas_TEC-1-ST)Riga TEC-1 is a backpressure unit with installed electric capacity of 101 MW (net). Riga CHP1 has four turbine units, six steam boilers and two hot water boilers. Power is produced mainly in cogeneration mode with natural gas, peat and heavy fuel oil are used as main fuels. Riga CHP1 was built between 1954 and 1958 and has recently been refurbished. Heat and power blocks (built in the middle of fifties) were replaced by new combined cycle gas turbine (CCGT) CHP units.

Rigas TEC-2 (Rigas_TEC-2-ST and Rigas_TEC-2-GT)Riga CHP-2 (Riga TEC-2) is an extraction unit based on steam turbine technology with installed electric capacity of 352 MW (net). Power is produced mainly in cogeneration mode with natural gas, peat and heavy fuel oil are used as main fuels. Riga CHP2 is the largest thermal power station in Latvia. The plant was put into operation in the mid and late 1970's. In 2000, Riga CHP2 was refurbished and is now capable of competing on the market. In 2014 TEC-2 is set to be refurbished. This will results in an increase in capacity. The plan is to have two units – one back pressure and one gas turbine giving a total capacity of 110 MW.


In 2014 TEC-2 is set to be refurbished. This will results in an increase in capacity. The plan is to have two units – one back pressure and one gas turbine giving a total capacity of 421 MW.

Small rural hydro power (M-HPP-LV)Small rural hydro power in Latvia with total net capacity of app. 26 MW (net)

Small back pressure steam turbine CHP (ST-BP-LV-OG)Small back pressure CHP units fuelled by natural gas and based on steam turbine technology.

Small back pressure gas turbine CHP (GE-BP-LV-G)Small back pressure CHP units fuelled by natural gas and based on gas turbine technology.

Small cond. biogas engines (BIO-GAS-LV)Small existing condensing gas engines fired by biogas.

Imanta CHP (ST-BP-IM-G)Imanta CHP is a plant that has been decided to construct and was commissioned in year 2006. It is a backpressure CHP fuel by natural gas. Total capacity 47.9 MW (net).

Daugavpils HPP (NEW-HYDRO-LV) New HPP in Daugavpils, Jekabpils. Total capacity 5.5 MW (net)

Biomass CHP (ST-B1-Bio-LV)New backpressure CHP fired by biomass and based on steam turbine technology. Commissioning in year 2010 with a net capacity of 40 MW.

Waste CHP (ST-B1-waste-LV)New backpressure CHP fired by waste and based on steam turbine technology. Commissioning in year 2010 with a net capacity of 15 MW.

The specific data for existing power stations and new technologies planned for commissioning or only available in Latvia are listed in appendix.

In the table below the RE electricity generation capacity is shown for Latvia for 2015 and 2020. This data is based on the NREAP.


(MW/year) 2015 2020Hydropower 1,550 1,550


Onshore wind 104 236

Offshore wind 0 180

Biomass 110 200

Solid biomass 46 108

Biogas 64 92

Of which CHP 97 161

Table 7: RE generation electricity capacity according to NREAP, Latvia

4.5 PolandThe most dominant fuel in the Polish energy mix is hard coal. Lignite also plays a significant role. The largest power plant is Belchatow with a net electrical capacity of 4113 MW.3 Wind power is the dominant renewable source, followed by biomass.4

In Poland the government sees nuclear power as part of the solution to reduce CO2-emissisons and diversify energy sources. In accordance with the Baseline projection from the EU Directorate - General Energy and Transport (DG TREN) scenario, 2.1 GW of nuclear power capacity is expected to be commissioned by 2025, and a further 2.4 GW by 20305.

(MW) 2010

Biogas 70

Coal 22,135

Oil 224

Lignite 6,365

Waste 34

Natural gas 737

Straw 418

Hydro 610

Wind 1,094

Wood waste 115

Total 31,802

Pumped storage (MWh) 1948

Table 8: Electricity generation capacity in Poland 2010

3 ARE SA. Katalog elektrowni i elektrocieplowni zawodowych. Warszawa: ARE SA, 2010.4 http://www.ure.gov.pl/uremapoze/mapa.html (accessed November 20, 2010).

5 http://www.world-nuclear.org/info/inf102.html (2009-02-03).


http://www.world-nuclear.org/info/inf102.html

In the table below the RE electricity generation capacity is shown for Poland for 2015 and 2020. This data is based on the NREAP.



Onshore wind 3,350 5,600

Offshore wind 0 500

Biomass 1,530 2,530

Solid biomass 1,300 1,550

Biogas 230 980

Of which CHP 505 955

Table 9: RE generation electricity capacity according to NREAP, Poland

4.6 FinlandThe table below shows the electrical capacity for Finland. The Finnish power system is characterised by centralised condensing and CHP generation. Finland is planning further nuclear expansion.

The new Olkiluoto 3-reactor is assumed to be commissioned in 2012.Data for wind power is updated with the recent statistics from the NREAP.

(MW) 2010

Coal 3,849

Oil 1,608

Waste 500

Natural gas 2,107

Nuclear 2,656

Peat 1,772

Hydro 2,980

Wind 38

Wood chips 170

Wood waste 1,788

Total 17,468

Table 10: Electricity generation capacity in Finland 2010

In the table below the RE electricity generation capacity is shown for Finland for 2015 and 2020. This data is based on the NREAP.

(MW/year) 2015 2020Geothermal 0 0



Tidal, wave and ocean energy 10 10

Biomass 2,200 2,920

Of which CHP 2,080 2,760

Table 11: RE generation electricity capacity according to NREAP, Finland

4.7 NW RussiaThe Russian electricity sector is among the largest in the world. In 2006, the electricity generation reached 991.4 TWh.

The Russian electricity supply is provided by a mixture of thermal, hydro and nuclear capacity. Thermal capacity plays the key role, accounting for a significant majority of installed capacity and acting as the price-maker in the growing free market sphere. Key sector players include the Unified Energy System of Russia (RAO UES), a 53 % state controlled holding company owning stakes in companies spanning the entire electricity supply value chain (including wholesale and territorial generating companies); Rosenergoatom, a 100% state owned company which owns and operates Russian nuclear power plants; and independent producers, of which the largest are four integrated utilities - Irkutskenergo, Tatenergo, Bashkirenergo and Novosibirskenergo.In 2007 16% of total Russian electricity demand was supplied by nuclear power. Rosatom, the State Atomic Energy Corporation running all nuclear assets of the Russian Federation, announced in 2006 a target for nuclear power providing 23% of electricity by 2020 and 25% by 2030, but since then plans have been scaled back by the government6.

Almost 6,000 MW of nuclear power capacity is installed in North West Russia and a number of new nuclear power stations are being proposed, including plants in Kaliningrad and St. Petersburg. On the other hand the existing plants, among others in St. Petersburg, are scheduled to be closed or renovated within the next 15 years.

6 http://www.world-nuclear.org/info/inf45.html, 25 June 2009.



(MW) 2010

Coal 825

Oil 5,048

Natural gas 10,588

Nuclear 5,760

Hydro 2,862

Wind 5

Wood waste 646

Total 25,734

Table 12: Electricity generation capacity in Russia 2010

4.8 DenmarkThe Danish power system is characterised by both central and decentralized CHP and a relatively high proportion of wind. Large installations are located in bigger cities, where there are district heating networks and thus the opportunity to benefit from cogeneration and heat. Denmark uses a variety of fuels for electricity generation, mainly coal and gas, but also biomass centralised and decentralised.

The Danish electricity and cogeneration system is represented in detail in the model. The large power units are shown individually, while the decentralised plants are aggregated into groups according to plant type. The table below is an overview of the existing capacities in Denmark in 2010.

(MW) 2010

Coal 3,846

Oil 1,268

Waste 226

Natural gas 2,415

Straw 263

Wind 3,727

Wood chips 50

Wood pellets 570

Total 12,365

Table 13: Electricity generation capacity in Denmark 2010

In the table below the RE electricity generation capacity is shown for Denmark for 2015 and 2020. This data is based on the NREAP.


(MW/year) 2015 2020Hydropower 10 10



Offshore wind 1,251 1,339

Biomass 1,837 2,779


Biogas 95 349

Bioliquids 26 26


Table 14: RE generation electricity capacity according to NREAP, Denmark

4.9 NorwayVirtually all of the Norwegian electricity production is based on hydropower and to a small extent, natural gas and biomass.

The table is based on statistics from the EWEA as well as input from Norway.

(MW) 2010

Oil 188

Waste 20

Natural gas 900

Hydro 29,600

Wind 430

Wood chips 30

Total 29,199

Table 15: Electricity generation capacity in Norway 2010

4.10 SwedenSweden, like Norway, has a large share of hydropower in the electricity system. In addition, Sweden has tree operational nuclear power plants and cogeneration with a relatively high proportion of biomass.

The government has decided that new nuclear plants are allowed to be constructed to allow the replacement of the existing facilities. This analysis assumes nuclear capacity to increase by 410 MW during the period 2010-20 due to renovations of existing plants.

Data for wind power is updated with the recent statistics from the NREAP.


(MW) 2010

Coal 1,000

Oil 3,390

Waste 138

Natural gas 1,261

Nuclear 9,372

Peat 60

Straw 38

Hydro 16,553

Wind 1,820

Wood chips 490

Wood waste 1,875

Total 35,997

Table 16: Electricity generation capacity in Sweden 2010

In the table below the RE electricity generation capacity is shown for Sweden for 2015 and 2020. This data is based on the NREAP.


Pumped storage hydropower 43 43



Offshore wind 129 182

Biomass 2,799 2,914


Biogas 42 42

Table 17: RE generation electricity capacity according to NREAP, Sweden

4.11 GermanyThe following table shows net electrical capacity of German power plants, aggregated in different groups. Germany, like Sweden and Finland, has nuclear power production. In addition the country has a smaller share of cogeneration and hydro.

In accordance with the latest German decisions all nuclear power plants are assumed to be decommissioned by 2022/23.

The existing solar and wind capacities reflect the data in the NREAP.


(MW) 2010

Coal 24,714

Oil 6,395

Lignite 18,688

Waste 1,400

Natural gas 24,383

Nuclear 20,264

Solar 15,784

Hydro 8,765

Wind 27,676

Biomass 6,312

Total 154,381

Table 18: Electricity generation capacity in Germany 2010

In the table below the RE electricity generation capacity is shown for Germany for 2015 and 2020. This data is based on the NREAP.


Geothermal 79 298

Solar photovoltaic 34,279 51,753


Offshore wind 3,000 10,000

Biomass 7,721 8,825


Biogas 3,126 3,796

Bioliquids 237 237


Table 19: RE generation electricity capacity according to NREAP, Germany


5 Investments in new generation This chapter concerns the exogenous and endogenous generation capacity of the Balmorel model. Exogenous capacity is the predefined capacity, often existing plants, while endogenous investment is the new generation capacities made by the investments module of the model from an investment catalogue.

5.1 Exogenous investmentsThe development of hydropower in the model is exogenously defined. Hydro generation is largely dependent on the available water resources and other external factors, hence the model cannot endogenously invest in new hydropower, but it is assumed that the hydro generation will increase towards 2050.

Norway differs from the other countries in the regions, because it has a large unused potential for hydropower. For Norway, the projections are based on Statnetts Forecast scenario that implies increasing hydro power production in Norway to 130.2 TWh in 2020, 140.9 in 2035 and 151.5 TWh in 2050.

In Sweden, Finland and Lithuania there is a slight increase in hydropower production to 2050. In Sweden the generation increases to 75 TWh in 2020 and it is kept constant until 2050, while in Finland it is 14 TWh in 2020 and constant at 16 TWh in 2035 and 2050. This minor adjustment is based on the report Sustainable Energy Scenario-Energy Perspectives for the Baltic Sea Region. In Lithuania the hydropower capacity increases by 14 MW from 2010 to 2020 in accordance with the NREAP.

The German hydropower is kept constant until 2050, similarly in Poland, Latvia, Estonia and Russia.

Nuclear power accounted for about 23 % of electricity generation in 2010 in the Baltic Sea Region. We have chosen to specify a fixed development of nuclear power in the future electricity supply as opposed to letting the model make the “optimal investments”.

The reason for this approach is twofold. First of all the investment costs – and the cost of eventually decommissioning the plants – are associated with a high degree of uncertainty. Secondly, a number of environmental externalities are related to nuclear power including the risk of nuclear accidents, radio-active emissions from mine-tailings, long-term storage of radioactive waste and the


Hydropower

Nuclear power

decommissioning of the power plants. These externalities are extremely difficult to monetize and therefore, in reality, decisions on nuclear power are based as much on political assessments and risk assessments as on financial calculations.

The nuclear development in Lithuania, Poland and Sweden is based on the assumptions made in the EU report ‘EU energy trends to 2030’ (EU Commission, 2010). This means that new units are expected to come online in in Poland, whereas a stable development is assumed for Sweden. Finland has informed that they expect three new nuclear units to come online before 2030 (including Olkiluoto3, which is currently being constructed). Moreover, one new unit is included in Kaliningrad. In other parts of North West Russia nuclear power capacity is assumed to remain constant. In Germany the planned nuclear phase-out is expected to take place by 2022 in accordance with their phase-out plan. The proposed unit in Lithuania (Visaginas) has been not been included.

FINLAND GERMANY POLAND RUSSIA SWEDEN

2012 2,691 20,339 5,760 9,372

2015 4,291 12,003 5,760 9,782

2020 4,291 8,052 1,515 6,842 9,782

2025 5,691 2,776 6,842 9,782

2035 7,191 3,699 6,842 9,782

2050 7,191 3,699 6,842 9,782

Table 20: Development of nuclear capacity (MW) in the region.

New coal fired power plants without CCS are not considered to be accepted politically in Sweden, Denmark or Lithuania. In Norway gas fired capacity is only to be accepted if CCS is applied, this condition is applied in all scenarios7.

5.2 Endogenous investmentsIn this study the Balmorel investment module is used to analyse the future energy system of the Baltic Sea region. This module allows the model to invest in new electricity and heat capacity to satisfy electricity and heat demand for the period towards 2050. These investments are made from an overall least 7 Norway has no expressed policy on coal power, but we assume that a new coal power plant would only be accepted if equipped with CCS.


New coal fired power plants

cost and technical optimisation of the entire energy system in the Baltic Sea region.

In the model predefined technologies, which the model is allows to invest in, are categorised in different investment areas, depending on the characteristics of the area. The model is allowed to invest in various types of generation specified for this specific area depending on the characteristics of the area. The following categories exist in the model.

Area type TechnologyCentral condensing area Condensing power plantsCentral CHP area CHP onlyDecentralised small CHP area Natural gas-fired plantsDecentralised large CHP ares Natural gas-fired plantsDecentralised area without gas network Non-natural gas technologiesWaste incineration area Waste incinerationOffshore wind area without wave power Offshore wind technologies onlyOffshore wind area with wave power Offshore wind and wave powerOffshore wind are far from shore Offshore wind far from shore

The model has a technology catalogue with a set of new power generation technologies that it can invest in according to the input data. The investment module allows the model to invest in a range of different technologies including (among others) coal power, gas power (combined cycle plants and gas engines), straw and wood based power plants and wind power (on and off-shore). Thermal power plants can be condensing unit – producing only electricity, or combined heat and power plants. The model is also able to rebuild existing thermal power plants from the existing fuel to another. The model can, at a lower cost than building a new power station, rebuild a coal fired plant to a wood pellets or wood chips and natural gas fired plant to biogas. Wave power and solar power technologies are also included in the technology catalogue.

Power plants with carbon capture and storage (CCS) are not considered due to uncertainties regarding costs.

Basic technical and economic data for the power generation technologies that the model may invest in can be viewed in Table 21 below. The technology assumptions develop from now to 2050, which means costs and efficiencies are assumed to develop depending on the learning curve of the specific technology. This development can be seen from the intervals presented in the


Investment categories by area

Technology options

table below. Generally the technologies develop to have higher efficiencies and lower investments costs.

Technology type Fuel type

Investment cost (mil. €/MWel)

Fixed O&M (€1000/MWel)

Variable O&M (€/MWhel)

Electric eff. Condensing

mode

Electric efficiency CHP mode

Total efficiency

(Elec. + heat)

Condensing Coal 1,89-2,04 57,2-61,6 2-2,2 0,46-0,535 - 0,46-0,535

Condensing Wood pellets 1,89-2,04 57,2-61,6 2-2,2 0,46-0,535 - 0,46-0,535

Condensing Natural gas 1,3-1,4 38 0,82 0,465 - 0,465

Condensing with CCS Coal 3 79,5 18,07 0,43 - 0,43

Condensing with CCS Wood pellets 3 79,5 18,07 0,43 - 0,43

Extraction CHP Coal 1,89-2,04 57,2-61,6 2-2,2 0,46-0,47 0,38-0,51 0,84-0,98

Extraction CHP Wood pellets 1,89-2,04 57,2-61,6 2-2,2 0,46-0,47 0,38-0,51 0,84-0,98

Extraction CHP Natural gas 1,3-1,4 38 0,82 0,53-0,37 0,37-0,53 0,9-0,9

Extraction CHP Wood 1,98-2,13 24,2 3,4 0,64-0,39 0,34-0,69 0,98-1,08

Extraction CHP with CCS Coal 3 79,5 18,1 0,37 0,37 0,74-0,74

Extraction CHP with CCS Wood pellets 3 79,5 18,1 0,37 0,37 0,74-0,74

Condensing CCNatural gas/biogas* 0,711-0,783 25,5 2,13 0,56-0,62 - 0,56-0,62

Condensing CC with CCS Natural gas 1,29 35,6 7,6 0,53 - 0,525

Extraction CCNatural gas/biogas* 0,79-0,87 30 2,5 0 0 0-0

Extraction CC with CCS Natural gas 1,4 40,09 7,9 0 0 0-0

BackpressureNatural gas/biogas* 1,25 27,6 2,2-2,45 0,92 - 0,92

Backpressure Straw 4 19,2 0,92 1,01 - 1,01

Backpressure Municipal waste 8,5372,75-403,81 6,18-6,24 0,97-0,98 - 0,97-0,98

Backpressure Biogas 3,2-3,38 93,55 7,94-9,03 0,82-0,84 - 0,82-0,84

Onshore wind Wind 1,45-1,49 28,28-29,31 2,9-3,42 1 - 1

Onshore wind LCI Wind 1,82-2,06 31,6-32,88 2,9-3,16 1 - 1

Offshore wind (low**) Wind 1,64-1,99 49,82-53,15 3,68-4,22 1 - 1

Offshore wind (mid**) Wind 1,92-2,94 49,82-54,1 3,68-4,74 1 - 1

Offshore wind (deep**) Wind 2,31-2,81 49,82-53,15 3,68-4,22 1 - 1

Solar PV Solar 0,9-2 9,36-24,48 1,3-3,4 1 - 1

Wave power Wave 1,6-7,8 20-21 3,5-6,67 1 - 1

Table 21: Selected generation technologies, which the model can invest in. The intervals indicate the development in technology and costs from 2010 to 2050. * The biogas on this plant is upgraded biogas, meaning it has the same quality as natural gas but with higher fuel costs. ** Offshore wind power is categorised in three groups with different investment costs, i.e. low, mid and deep water depth. The technology catalogue is mainly based on Energinet.dk’s and the Danish Energy Agencies ‘Technology Data for Energy Plants’, May 2012 and own assumptions.

The model may also invest in heat generation capacity such as coal, biomass and gas boilers, as well as large-scale electric heat pumps, electric boilers, solar heating, electric storages and heat storage.


The opportunities to invest in the different technologies are not uniform across the region, for example because there are differences in the availability of resources in the different countries.

Similar political opinions about certain technologies like nuclear power and coal power influence their future role in some countries.


6 General assumptionsThis chapter describes the project’s key assumptions concerning the following elements:

Fuel and CO2 prices Electricity and heat consumption projections Wind profiles and full-load hours Time resolution

6.1 Fuel and CO2 pricesThe fuel prices of coal, oil and gas in this study are based on the IEA New Policies Scenario as presented in IEA World Energy Outlook, November 2012. The New Policies Scenario, dealing with the period 2012-2035, assumes that current G20 low carbon agreements are implemented. A linear projection is assumed for the period 2035-2050 for both fuel and CO2 prices.

The global efforts to combat climate change will reduce the demand for fossil fuels at the global level compared to a development with no low carbon regulations. Therefore, according to the International Energy Agency (IEA), increases in prices of coal, oil and natural gas will be relatively moderate. In 2035 the price of crude oil is projected to reach $125 per barrel in real terms (in 2011 dollars).

The price of natural gas has been increased in the ‘World Energy Outlook 2012’ by approximately 1 % in 2035 compared to the World Energy Outlook 2011. A decrease of 10 % in the natural gas price was expected when the ‘World Energy Outlook 2011’ was published, mainly due to the raise in expectations to unconventional gas, such as shale gas. Hence the decrease caused by raising gas resources is still expected.


20122014

20162018

20202022

20242026

20282030

20322034

20362038

20402042

20442046

20482050

-

2

4

6

8

10

12

14

16

18

20

22

Gas oil Natural gas Coal

EUR/

GJ

Figure 4 Fossil-fuel price assumptions in the World Energy Outlook New Policies Scenario (IEA, 2012).

WEO12 also forecasts the CO2 price in the EU ETS. This is, in line with the above fuel prices, based on the New Policies Scenario. The IEA only state a CO2 price for 2020. Therefore the historic 2012 price is used as a starting point and a linier projection is made for 2012-2020. A linear projection is also made for the period 2035-2050.

20122014

20162018

20202022

20242026

20282030

20322034

20362038

20402042

20442046

20482050

0

5

10

15

20

25

30

35

40

45

50

EUR/

ton

Figure 5: CO2 price (DKK/ton) assumptions in the World Energy Outlook New Policies Scenario (IEA, 2012).

6.2 Electricity demandThe gross electricity demand in the reference is based on the information provided in the NREAP for each of the EU countries in the region.


The projection for NW Russia is based on a projection provided by InterRAO UEA.

For Norway it is based on ENTSO-E report8 and own assumptions.

The projection for the period 2021-2050 is based on own assumptions. See appendix A for a more detailed description.

DENMARK ESTONIA FINLAND GERMANY LATVIA LITHUANIA NORWAY POLAND RUSSIA SWEDEN0.0

100.0

200.0

300.0

400.0

500.0

600.0

700.0

TWh/

year

Table 22: Net electricity demand 2010-2050 in TWh.

6.3 District heating demandIt is a challenge to obtain reliable data regarding the potential and (not least) the cost for expanding DH in the region.

The development in heat demand in for 2010-2020 is based on the figures from the EU Commissions scenario report (2010): “Energy Trends 2030”. A projection for 2021-2050 is done using own assumptions. See appendix A for details.

The net heat demand can be seen in the table below.

8 ENTSO-E “Scenario Outlook and System Adequacy Forecast 2011-2025”(ENTSO-E, 2011).


DENMARK ESTONIA FINLAND GERMANY LATVIA LITHUANIA NORWAY POLAND RUSSIA SWEDEN0.0

100.0

200.0

300.0

400.0

500.0

600.0

PJ/y

ear

Table 23: Net district heating demand 2010-2050in PJ.

6.4 Implementation of renewable energy targetsThe EU renewable energy directive requires all member states to increase their share of renewable energy towards 2020. The directive provides a legally binding target for the share of renewable energy of final energy in each member state, but not a separate target for the electricity sector.

To ensure progress and compliance with the directive each member state has to provide a detailed roadmap – a National Renewable Energy Action Plan (NREAP) – showing how it expects to reach its 2020 target for the share of renewable energy, which includes a detailed plan for the development of RE in the electricity system. In this study the information from the NREAPs are used to specify how the expansion of renewable energy in the electricity will take place in each country towards 2020. The concrete assumption about, which technologies will be promoted and timing of the implementation are stipulated in the data report.


Country 2010 2015 2020

Denmark 34.3% 45.7% 51.9%Estonia 1.7% 3.5% 4.8%Finland 26.0% 27.0% 33.0%Germany 17.4% 26.8% 38.6%Latvia 44.7% 51.4% 59.8%Lithuania 8.0% 17.0% 21.0%Poland 7.5% 13.0% 19.1%Sweden 54.9% 58.9% 62.9%

Table 24: Projected % share of renewable energy compared to gross final electricity consumption as reported in the National Renewable Energy Action Plans, 2010.

In the study we assume that the above shares of renewables in electricity generation as a minimum are maintained between 2020 and 2050. In the model the above targets are implemented on a technology specific level, which e.g. means that the German plans for development of solar PV will take place. Therefore, towards 2050 the target to reduce CO2 emissions – reflected in a price of emitting CO2 – takes over as the main driver for increasing renewable energy generation.

From 2012, Norway and Sweden form a common market for renewable energy certificates. Up to 2020, Norway and Sweden intend to expand their electricity production based on renewable energy sources by 26.4 TWh. This target is used to estimate renewable energy development in Norway to 2020, as Norway has not developed a National Renewable Energy Action Plan. The Norwegian system operator, Stattnett is preparing the grid to accommodate for at least 13.2 TWh of new renewable energy generation9. Norway has not published a NREAP but have agreed with the EU that the share of RE in their energy system should be 67.5 % in 2020. Statnett, the Norwegian TSO, expects 13.2 TWh of new RE generation in the electricity system by 2020 facilitated by their RE certificate scheme. It is therefore assumed that approx. 10 TWh of new wind power generation to come into operation by 2020, with half of it being commissioned by 2015.

In Denmark, the study takes into account the recent energy strategy decision to increase wind power generation to match 50 % of electricity demand in 2020 as well as a significant increase in solar power and biogas generation. This means that Denmark will exceed the projected share of renewable energy in the electricity supply which is stated in the NREAP above.

9 ‘Nettutviklingsplan 2011’, Statnett, 2011, p. 4.


7 Renewable energy potentialsThe chapter describes assumptions about renewable energy potentials used in the model.

7.1 Biomass potentialsExpansion with biomass fired power plants and boilers may to some extent be limited by the availability of resources locally.

The table below provides an overview of possible biomass resources in 2030 in each of the countries in the region divided into five general categories:

Energy crops and grass cuttings Forestry residues from felling and complementary felling Biogas (mainly from manure) Wood like biowaste (wood processing residues, black liquor) Straw like biowaste (mainly agricultural residues)

Municipal solid waste fractions are treated separately in the subsequent section.

PJEnergy crops and

grass cuttingsForestry residues Biogas

Biowaste - wood like

Biowaste - straw like

Total

Germany 980 201 149 133 177 1.640 Denmark 4 40 36 11 29 120 Finland 54 75 9 215 17 370 Sweden 59 100 15 347 21 542 Estonia 54 8 2 35 2 102 Lithuania 331 17 7 40 10 405 Latvia 63 25 3 1 4 96 Poland 1.273 50 79 59 121 1.583 Norway - 160 9 8 177 Russia 109 151 18 430 33 740 BALTIC SEA REGION 2.927 828 317 1.280 423 5.775

Table 25: Available bioenergy resources in the Baltic Sea Region. The figures are derived from the report “How much bioenergy can Europe produce without harming the environment?” (EEA 2006)10. Data for Russia is lacking. For the purpose of modelling a crude assumption has been made that the biomass resources in NW Russia equal twice the resources in Finland.

The total identified bioenergy potential will not be to the disposal of the electricity and district heating sector as the bioenergy will also be used in industry, households and for the transport sector.

10 The projection for NW Russia is based on a projection provided by InterRAO UEA and for Norway on the ENTSO-E (cooperation of system operators) report “Scenario Outlook and System Adequacy Forecast 2011-2025”


Therefore we assume that only 50 % of the total bioenergy resource will be available for the power and district heating sector. This assumes that the share of bioenergy used for transportation fuels is rather low.

The table below gives an estimate of the bioenergy resource available for the power and district heating sectors. It is assumed, that 90 % of the biogas resources is used electricity and district heating and 80 % of the straw resource, whereas only 40 % of energy crops, forestry residues and wood like biowaste fractions will be used for power and district heating generation. In total, for the Baltic Sea Region, this means that 50 % of the total bioenergy resource is available for the power and district heating sectors.

Interpretation of the biomass categories to the modelFor the purpose of modelling, the three biomass categories “Energy crops and grass cuttings”, “Forestry residues” and “Wood like biowaste” are merged into three fuel categories termed “Wood waste”, “Wood” and “Wood pellets”.

“Wood waste” is a cheap local resource used at existing power plants in Poland, Sweden and Finland. For this fraction a price close to zero is used.

“Wood” is a more expensive biomass resource, but also limited according to the available national resources, whereas “Wood pellets” can be traded across the countries in the region. For “Wood” a price of wood chips is used. For “Wood pellets” a higher price is applied, reasoned upon higher transportation and handling costs (see previous section). Wood pellets are more expensive than wood chips, but easier to transport and handle at the power plants.

The “Straw-like biowaste” resource is termed “Straw” in the model.Biogas is treated as two separate fractions in the model: “Biogas” and “Biogas-net” where the first fraction refers to biogas stand-alone plants (CHP plants or boilers) and the latter to biogas which has been upgraded for utilisaition in the grid. “Biogas-net” may be used at conventional power plants.

Both “Straw” and “Biogas” are considered to be domestic resource in the model.

For the purpose of modelling it is assumed that biogas may be used in connection with all local district heating schemes. This is a simplification of the


actual possibilities for utilisation of biogas. A negative CO2-factor (-29 kg/GJ) is used for biogas in order represent the abated fugitive emissions (methane and nitrous-oxide) related to the alternative use of the manure in the agricultural sector.

PJ "Wood" "Wood pellets" "Wood waste" "Biogas""Biogas

net""Straw" TOTAL

Germany 263 - 67 67 142 538 Denmark 11 - 33 16 23 83 Finland 45 120 8 4 13 190 Sweden 83 90 13 7 17 210 Estonia 19 - 2 1 2 25 Lithuania 77 - 6 3 8 95 Latvia 18 - 2 1 3 25 Poland 276 - 71 36 97 481 Norway 34 - - - 6 40 Russia 90 240 16 8 26 380 BALTIC SEA 917 917 450 219 338 2.983

917

Table 26: Available bioenergy resources in the Baltic Sea Region for electricity and district heating generation. Resources are distributed on the fuel categories used in the Balmorel model. The “Wood pellet” resource is only included in the grand total for the Baltic Sea Region (not in the country totals).

7.2 Municipal solid wasteThe figures for municipal waste available for energy purposes are based on a projection from RISØ DTU11. From 2010 to 2025 a growth rate of 1.3 % is assumed for all countries. From 2025 to 2050 the resources are assumed to remain constant.

90 % of the available municipal waste resource is used assumed to be available for the electricity and district heating generation.

The table below shows the data available for the simulations.

11 Assuming a decommissioning rate of 1 % per annum one third of existing building would be decommissioned between 2010 and 2050.


Year DENMARK

ESTONIA

FINLAND

GERMANY

LATVIA

LITHUANIA

NORWAY

POLAND

RUSSIA

SWEDEN

Total

2010 34

24

129

1

5

8

46

248

2015 34

2

25

220

5

3

10

63

10

49

421

2020 36

4

27

310

8

5

15

119

20

52

596

2025 38

6

29

401

11

7

19

174

30

56

772

2030 38

8

29

491

14

10

24

229

40

56

940

Table 27: Municipal solid waste used for power and district heat generation. After 2030 the waste quantities are kept constant (PJ/year).

7.3 Wind powerThe model’s investment module can choose to invest in wind power capacity based on the technical/economic potentials in each country. These are not the theoretical potentials for wind, but an estimate of a possible potential taking into consideration constraints related to access to sites, the economics of developing different sites and the available wind resources.

In 2009 the European Environment Agency (EEA) published the report ”Europe's onshore and offshore wind energy potential” with assessments of potentials for on- and offshore wind potentials in all EU member states. This analysis was done using a harmonised method in all countries.

Based on this report an assessment of wind power in potential in the Baltic Sea Region has been made (see Annex C).

With respect to Russia a crude estimate has been made. In NW Russia the double potential of Finland is used.

The potential for each country can be found in a separate in appendix to this report.

The investments costs of the off-shore wind farms are dependent on the distance to the coast and the depth of the turbines.

(MW TABEL)


Wind speed time seriesThe specific wind speed time series are based on actual wind measurements in the different areas. An extensive set of data covering a large of the Baltic Sea Region was obtained from a major Danish wind turbine manufacturer in connection with the project “Paths to a fossil-free energy supply” (Ea Energy Analyses, 2010). The model can then optimise wind power production based on the potential, wind speed, turbine features and turbine prices.

7.4 Solar powerThe annual generation from solar PV and solar panels for heating reflects the average annual solar irradiation in the different countries in the region.(MW TABEL)


Appendix A: Assumptions about the development of energy consumption till 2050

The projections of the demand for electricity and district heating until 2020 are based on the national prognoses included in the countries National Renewable Energy Action Plans12 (electricity) and the EU Commissions scenario report “EU energy Trends 2030” (district heating).

For the purpose of the study on Energy Strategies for the Post-Kyoto period a simple model has been developed to explore how final energy may develop in the long-term, i.e. towards 2050. This note describes key assumptions behind this projection.

Forecasting energy demand in a 40 year perspective is associated with great uncertainties related to:

- Economic development of the region- Implementation of energy saving measures- Transition to new end-use conversion technologies (electric vehicles,

heat pumps)

A simple spread-sheet model is used, which is structured around the three steps:

- Projection of GDP and population for each country in the region - Assessment of the development in energy intensity within different

categories of energy end-uses considering the long-term potentials to utilize energy in a more efficient way. The energy intensity factors are expressed as energy consumption per GDP and energy consumption per capita

o By multiplying the projected energy intensity factor per capacity with projected GDP an estimate of projected energy consumption is achieved using existing end-use conversion technologies.

o The demand for heat is projected according to a future “technical” level of energy consumption for heating per capita. Moreover, the different climatic conditions in the region are accounted for.

12 This assumption is in line with the analyses made by the Danish Commission on Climate Change Policy and the German “Energy concept”.

- Assessment of changes in end-use conversion technologies for heating, process energy and in the transport sector. This may lead to additional improvements in end use efficiency.

The projection is made with a view to complying with the long-term target of the EU to reduce GHG emissions by 80-95 %.

Economic developmentThe starting point for the economies in the Baltic Region is very different from country to country with GDP per capita varying between 7,500 to 58,000 €/capita. Norway is relatively seen the strongest economy in the region partly due to large revenues from sale of oil and gas which constitutes more than 20 % of GDP.13 Excluding oil and gas sale the GDP of Norway would be approx. 46,000 €/capita – this figure is used in the analyses.

The forecast assumes that the countries in the region with the lowest GDP per capita today will come closer to catching up with the richest economies in the region. This means that the economies with the lowest GDP per capita today are assumed to grow faster (approx. 2.7 % p.a.) than the more developed economies (0.8-1.5% p.a.). The GDP growth projections are based on the previously mentioned EU Commissions scenario report “EU energy Trends 2030”14, however in the case of Germany we have used the assumption from the German Energy Concept.

For the wealthiest economies the GDP projection from the EU is used between 2010 and 2030, whereas between 2030 and 2050 we assume that the annual GDP growth is only half of that. For example in the case of Denmark GDP is estimated to grow 1.6 % p.a. between 2010 and 2030 and 0.8% between 2030 and 2050. Hence, average GDP growth is 1.2 % p.a. between 2010 and 2050. For the countries with lowest GDP (Lithuania, Latvia, Estonia and Poland) we prolong the projected growth rates between 2010 and 2030 to the period 2030 to 2050.

Denmark Germany Lithuania Latvia Estonia Finland Sweden Poland Norway1,2% 0,8% 2,7% 2,5% 2,8% 1,3% 1,5% 2,7% 1,2%

Table 28: Assumptions about average annual economic growth between 2010 and 2050-

13 65 % of the demand for heating is assumed to be correlated with number of heating degree days.14 “Assessment with respect to long term CO2-emission targets for passenger cars and vans”, EU Commission, AEA (2009).

48 | Long-term energy scenarios for Estonia (DRAFT), DATA REPORT - 04-01-2013

Data on current and projected GDP is summarized in Figure 6.

0

10.000

20.000

30.000

40.000

50.000

60.000

70.000

80.000

90.000

GDP/capita [€/y] 2009 2050 projection

Exlc. gas/oil sale

Figure 6: GDP per capita in 2008 and projections towards 2050

Energy intensity indicatorsThe relative energy consumption needed to produce a unit of GDP also varies very significantly between countries.

Comparing total final energy consumption with GDP we see that the energy intensity is 7-9 MJ per € in Latvia, Poland, Estonia and Lithuania compared to 3-6 MJ per € in Denmark, Germany, Norway, Sweden and Finland. It should be stressed that in this assessment of energy demand no difference has been made between different types of energy (electricity, fuels, heat) with different qualities (levels of exergy).

The energy intensity has been assessed within different categories of end-uses (industry, heating, electricity in households and service) and the computations show a great divergence between countries within all groups.


-

2,0

4,0

6,0

8,0

10,0

12,0

14,0

MJ/€ Total final energyIndustryHeatingElectricity in households and serviceTransport

Figure 7: Energy intensity of the countries in the region within different types of end-uses.

The differences between countries may be explained by a number of factors, including:

- Differences in “efficiencies” of equipment, machinery, transport means and buildings

- Climatic conditions- Differences in life-style, habits etc.- Presence of large-scale energy intensive industries in some countries

and not in others- Differences in policies, including the use of norms, standards and

economic instruments

Moreover, it should be stressed that energy consumption can be explained by other factors than only GDP; such as numbers of inhabitants or heated square meters.


0

20

40

60

80

100

120

Denmark Germany Lithuania Latvia Estonia Finland Sweden Poland Norway

GJ/capita

Industry

Heating in households and services

Electricity (excl. heating) in households and serviceTransport

Figure 8: Final energy consumption per capita (GJ/capita) of the countries in the region within different types of end-uses.

Plotting energy intensity of the different countries with economic output per capita shows a relationship, which is better explained by a logarithmic function than a linear function. This indicates that the growth in the demand for energy services may be gradually saturated as the economics improve in a country.

y = 0,0019x + 63,7R² = 0,6517

y = 46,939ln(x) - 351,25R² = 0,7638

0

20

40

60

80

100

120

140

160

180

200

0 20.000 40.000 60.000 80.000

Energy per capita [GJ/cap.]

GDP per capity [€/y]

Lineær (Serie1)Log. (Serie1)

Figure 9: Energy intensity (final energy per compared to level of GDP/capita


Forecasting the demand for energy serviceIn order to forecast the demand for energy services we multiply a projected energy intensity indicator for each country with its projected GDP figure.

The projection of energy intensity indicators is done sector-wise based on the following assumptions.

Process energy (industry)Today’s energy intensity factors varies between 2.8 MJ/€ (Denmark) and 12.2 MJ/€ (Finland). This difference can be explained by differences in efficiency as well as in the location of energy intensive industries in the region. Based on experience from previous analyses we expect that the energy intensity can be reduced by about 1 % per annum until 2050 due to the uptake of more energy efficient machinery and processes. This assumption – which is associated with a high level of uncertainty – is used for all countries. Hence, we still presume very different energy intensity between countries because of the location of the energy intensive industries.

HeatingToday’s energy intensity factors varies between 1.3 MJ/€ (Denmark) and 4.8 MJ/€ (Latvia). The potential to reduce the demand for heating is very significant through renovation of existing buildings and by setting tough standards for new buildings. By 2050 a significant share of the existing building stock can be assumed to have been decommissioned and replaced by new constructions15.

If we look at the energy consumption for heating per capita, the figure varies from 19 GJ per capita in Lithuania to 59 GJ per capita in Sweden. The differences may be reasoned in differences in both energy efficiencies of the buildings as well as the number of heated square meters.

When forecasting the demand for heating we have chosen to base the forecast not on GDP, but on a projection of the needed energy consumption for heating per capita.

15 New cars sold in the EU had average emissions around 160-175 g CO2/km in 2000 http://www.eea.europa.eu/pressroom/highlights/carbon-efficiency-of-new-cars . According to the report “Assessment with respect to long term CO2-emission targets for passenger cars and vans”, EU Commission, AEA (2009), emissions could be reduce to 75 g CO2/km.


http://www.eea.europa.eu/pressroom/highlights/carbon-efficiency-of-new-cars

Among the five richest countries Denmark and Germany have the lowest energy consumption per capita, approx. 43 GJ per capita. In the long term we expect that this figure (43 GJ/cap.) it is possible to reduce this figure by 40% in spite of increasing economic growth16. The resulting figure of 26 GJ/cap is used as a best estimate for the long-term energy consumption for heating in all countries in the region. However, the figures are adjusted to take into account that the climate differs throughout the region. Therefore, in Finland for example, which has approx. 5200 heating degree days per annum compared to 3250 heating degree days in Germany, the heating demand is assumed to end at 36 GJ/cap in 205017.

The approach described above suggests that by 2050 all inhabitants in the region enjoy the same level of heat service.

Electricity (excluding electricity for heating, industry and transport)The energy intensity factors for electricity consumption in households and services sectors varies between 0.4 MJ/€ in Denmark, Sweden and Germany and 1.3 MJ/€ in Estonia. There is a significant potential for reducing the energy intensity even in the countries, which are the most efficient today.

The projection towards 2050 is based on an assumption that the three most efficient countries (Denmark, Sweden, Germany) are able to improve the energy intensity factor by 40%. Norway and Finland are assumed to reach a level corresponding to the average of three most efficient countries in 2050 and their current level minus 40%. Estonia, Lithuania, Latvia and Poland are assumed to reach a level corresponding to the average of the five richest economies. The chosen methodology assumes that all countries are brought closer to the level of the most efficient country, but still accounts for some level of national differences.

MJ/€ DEN GER LIT LAT EST FIN SWE POL NOR RUS

2008 0,40 0,37 1,02 1,03 1,44 0,84 0,40 1,02 0,99

2050 projection 0,24 0,23 0,30 0,30 0,30 0,37 0,24 0,30 0,42 0,30

Table 29: Energy intensity factors for electricity consumption (excluding electricity for heating, industry and transport)

16 Eerens & de Visser, December 2008.17 If you require this appendix, you can send a request to [email protected]


TransportThe energy intensity factor of the transport sector varies between 0.9 MJ/€ in Norway to 2.8 MJ/€ in Latvia. The intensity of the transport sector can be reduced through a number of different measures stretching from physical planning to more efficient transport means.

The potential for improving the efficiency of transport is considerable relying even using conventional Internal Combustion Engine (ICE) technologies or hybrid technology. The technical potentials concern improvement of engine efficiency, heat recovery and using advanced lightweight materials18 and could reduce the specific energy consumption by more than 50 % for conventional cars19. For trucks the potential is around 30-40% according to the IEA.

The projection towards 2050 is based on an assumption that the most efficient countries (Denmark, Norway, Germany) are able to improve the energy intensity factor by 50%. The other countries are assumed to reach a level corresponding to the average of three most efficient countries in 2050 and their current level minus 50%. The chosen methodology assumes that all countries are brought closer to the level of the most efficient country, but still accounts for some level of national differences.

MJ/€ DEN GER LIT LAT EST FIN SWE POL NOR

2008 1,02 1,01 2,41 2,80 2,43 1,18 1,23 1,82 0,93

2050 projection

0,51 0,50 0,85 0,95 0,86 0,55 0,56 0,71 0,48

Table 30: Energy intensity factors for transport

Changes in end-use technologiesChanges in end-use technologies is assessed for process energy, heating and the transport sector.

18 ARE SA. Katalog elektrowni i elektrocieplowni zawodowych. Warszawa: ARE SA, 2010.19 http://www.ure.gov.pl/uremapoze/mapa.html (accessed November 20, 2010).


Process energyThe following crude assumptions are made about the distribution of energy sources for process energy:

Fossils fuels are assumed to reduced significantly by increased use of district heating (could also be supplied by local CHP plants), electricity and biomass. District heating and electricity has the potential to be produced from CO2-neutral sources. The different types of energy are assumed to substitute each other 1:1, assuming that the boilers and other end use technologies have the same efficiency independent of the fuel used. However, an exception is made with respect to electricity, which is expected to be used with a higher efficiency for example through high-temperature heat pumps. Therefore an end-use efficiency of 125% is used for electricity.

Energy source (technology) Assumptions

Solid fuels (coal, lignite) Phased out

Petroleum Products Share is reduced to 25% of original share

Natural Gas Share is reduced to 33% of the original share

Electricity Share is increased by 10 %-points

District heat Share is increased to 20 %

Biomass Supplies the remaining demand for energy

Table 31: Assumptions regarding energy sources for process energy

HeatingHeating can be supplied at lower temperatures than process energy, which makes both district heating and heat pumps more attractive than in the Industry sector.

The starting points of the different countries in the region are very different. All countries except Norway and Germany have a relatively large share of their demand for heating covered by district heating. In Germany natural gas plays a greater role for heating than in any of the other countries (approx. 47%), whereas electricity is the most dominant source of heating in Norway (approx. 75 %).


In all countries we expect the role of oil and natural gas to be reduced very significantly to comply with the ambitious long-term reduction targets.

There are pros and cons of the different substitutes for oil and gas: District heating:

o high infrastructure cost makes it more attractive in urban areas with high energy intensities,

o more attractive if surplus energy is available from CHP plants or industrial plants,

o enables the use of geothermal energy, large scale solar energy and large scale heat pumps,

o some losses in the grid. Heat pumps (electric):

o high investment cost,o attractive source of energy in rural areas and single-family

houses,o costs depends on the price of electricity,o local heat storage is relatively costly

Biomass boilers:o depends on a single source of energy: biomass, the limitation

of biomass may lead to high priceso local emissions can be significant

Green gas:o utilization of the gas grid to distribute biogas or methane

produced from biomass or wind (via hydrogen).

It is difficult to predict which heating solutions are the most attractive, but we envision a greater role of both district heating and electric heat pumps. The following crude assumptions have been made about the supply of heat in 2050:


Energy source (technology) Assumptions

Solid fuels (coal, lignite) Phased out

Petroleum Products Share is reduced to 10% of its original share

Natural Gas Share is reduced to 20% of its original share, lower phase-out in Germany according to Energy Concept plan

District heat Share is increased the most in countries with low shares of district heating today. By 5 %-points in DK and LV. By 10%-point in NO and SE and by 15 %-points in Poland. In the remaining countries the share remains at the same level as today.

Direct electric heating Reduced to 1-2 % in all countries except Norway where 10 % of heating is still assumed to be direct electric heating in 2050.

Solar Supplies 5 % of the demand for heat in all countries.

Heat pumps (using electricity)

Become are very important source of heating supplying between 28 % (Germany) and 66 % (Norway) of total heat demand

Biomass boiler Supplies remaining demand for heat, from 3 % (Norway) to 19 % (Germany).

Table 32: Assumptions regarding energy sources for heating

The heat pumps are envisioned to have an average annual COP of 3.

Transport sectorPetroleum products make up almost all energy used in the transport sector today.

Towards 2050 a transition away from petroleum products is required to comply with the overall target of 80-95 % reduction. The alternatives are electrification of the transport sector (electric vehicles, hydrogen vehicles) or increased used of biofuels (ethanol, biodiesel, bio-methane etc.). Moreover, modal change – increasing the use of public transportation, cycling etc – can also be important measures.

Moreover, in a long transition period, using more efficient conventional transport means can be an important measure.


The projection is based on some simple assumptions. Gasoline and diesel is still used at a low level with their shares being reduced to 15 % in total compared to more than 80 % today. Jet fuel maintains the same share as it has today in the five richest economies, i.e. 15 %. Biofuels provide a simple solution because it can be used in conventional engines. However, its application is constrained by limited biomass resource even if the fuel produced some 2nd generation biofuel refineries. In the projection biofuels (in various shapes) are expected to comprise 15 % of the fuel demand in the transport sector. The remaining demand for transport – 55 % - is covered by electrified transportation means – most notably electric vehicles.

In order to match the projections of the Germany Energy Concept a lower degree of electrification is applied in Germany. Energy source (technology) Assumptions

Gasoline 5% (15 % in Germany)

Diesel oil 10% (20 % in Germany)

Jet fuel Share is fixed at 15 %

Biofuels Share is fixed at 15 %

Electricity (EV’s etc.) 55 % (35 % in Germany)

Table 33: Assumptions regarding energy sources (technologies) for transportation

The end use efficiencies of the electric vehicles are assumed to be 2.5 times higher than conventional engines (today this factor would probably be around 3.5, but the efficiency of the conventional engines is assumed to be improved considerable towards 2050).

ResultsTable 34 and Table 35 show final energy consumption in 2008 as well as the projected energy consumption for 2050.


Total final energy TOTAL Denmark Germany Lithuania Latvia Estonia Finland Sweden Poland Norway

Oil 5.724 297 3.538 68 61 40 316 414 712 278

Electricity 3.586 120 1.892 32 24 25 297 370 423 401

Bioenergy 1.234 48 380 21 39 22 185 355 158 26

Coal 1.113 9 428 9 4 7 38 76 513 29

Natural gas 2.940 70 2.381 22 21 10 34 60 331 10

District heat 1.239 98 437 36 22 21 176 170 267 11

Free heat 108 3 17 1 0 0 9 54 6 18

SUM 15.942 645 9.072 190 171 125 1.056 1.498 2.410 774

Table 34: Final energy consumption in 2008

Total final energy TOTAL Denmark Germany Lithuania Latvia Estonia Finland Sweden Poland Norway

Oil 1.537 67 936 22 17 13 69 110 236 67

Electricity 4.240 172 1.802 75 52 50 319 473 947 350

Bioenergy 2.538 75 1.104 43 38 37 220 322 654 45

Coal - - - - - - - - - -

Natural gas 1.262 22 1.003 15 11 7 18 15 167 5

District heat 1.481 85 326 59 24 26 184 175 563 38

Free heat 970 40 384 18 15 9 58 72 313 61

SUM 12.027 461 5.555 232 157 142 867 1.168 2.879 565

Table 35: Projected final energy consumption in 2050


Appendix B: Assessment of the potential for wind power in the BSR

In 2009 the European Environment Agency published the report “Europe’s onshore and offshore wind energy potential”. This report is used in the present project as the key source of information for the estimation of both onshore and off-shore wind power potentials in the Baltic Sea Region.

The EEA report calculates the “raw” wind resource potential and at the same time introduces and quantitatively analyses the environmental and social constraints on wind sector development. The assessment of the raw potential is primarily based on wind speed data.

The EEA project distinguishes between off-shore and onshore wind power. Moreover special assumptions are made regarding wind power potentials in mountainous areas.

Onshore wind potentialThe generation potential is divided into three cost categories: not competitive (average production cost higher than 6.7 €-cent/kWh), most likely competitive (5.5-6.7 €-cent/kWh and competitive (lower than 5.5 €-cent/kWh).

The analysis shows that the raw technical potential is very significant compared to the current electricity consumption and wind power generation in the region. This is also the case, when only the “competitive” potential is assessed. For example in the case of Denmark the competitive potential is 687 TWh compared to an annual consumption of 35 TWh and annual wind power generation in the order of 7 TWh. In Germany the competitive wind power potential is 258 TWh, which should be compared to an annual electricity consumption of approx. 630 TWh and annual wind power generation close to 50 TWh. If sites are included, which are most likely competitive, the wind power potential increases to 642 TWh.


Not competitive Most likely competitive Competitive Grand totalDenmark - - 751 751 Estonia - 75 597 672 Finland 7 1.052 3.359 4.418 Germany 344 1.206 2.467 4.017 Latvia - 260 593 853 Lithuania - 305 442 747 Norway 616 527 1.094 2.237 Poland 39 1.035 2.609 3.683 Sweden 487 2.021 2.539 5.047

Table 36: Onshore wind power potential in different cost classes in 2030 (TWh).

In practice the raw technical wind potential will be constrained by a number of factors including in particular social constraints such as the visual impact of the wind farms, noise and the protection of natural habitats.

Denmark and Germany are probably the two countries in the region, which have come closest to developing their full onshore wind power potential. In Denmark the onshore capacity is just above 2800 MW, but analyses by the Danish government and municipalities indicate that the constrained potential is probably 4000 MW producing approx. 12 TWh. The additional MWs will be made available through repowering of existing sites – replacing old turbines with new larger ones – and by identifying new sites.

In Germany the installed onshore wind power capacity is approx. 27.000 MW producing close to 50 TWh. The German Energy Concept includes a number of measures (improved spatial planning, repowering, enhance public acceptance, optimized licensing procedures) aimed at further increasing this number. According to the Energy Concept’s background report (Energieszenarien für ein Energikonzept der bundes regierung, 2010)i onshore wind power generation is assumed to increase to 36.000 MW in the long-term producing up to 79 TWh.

If one compares the raw wind power potential of the cost classes “most likely competitive” and “competitive” with the projections of the long-term potentials in Denmark and Germany respectively you would see that (only) 1,6 % of the Danish raw technical potential is exploited and in Germany 2,2%. The share is higher in Germany in spite of the fact that the population density is close to twice as high as in Denmark (230 vs. 125 per inhabitants per km2). It can be assumed, that the discrepancy reflects a difference in social acceptance of wind power in the two countries.


To assess the long-term potentials in the other countries in the region we use an (un-weighted) average level of acceptance Denmark and Germany – 1.9% of the raw potential - as a proxy for acceptance level in the other countries in the region.

Using this assumption we obtain the following potentials for the different countries in the region in 2030.

TWh Most likely competitive Competitive TotalEstonia 1 11 13Finland 20 63 83Germany 23 46 69Latvia 5 11 16Lithuania 6 8 14Norway 10 21 30Poland 19 49 68Sweden 38 48 85

Table 37:Onshore wind power potential in 2030 (TWh)

For the purpose of the modelling these potentials are converted to a number of MW wind power capacity within different electricity regions. For this exercise we made the following assumption about average full-load hours:

Competitive: 2200 FLHMost-likely competitive: 1700 FLHNot-competitive: 1200 FLH

Distribution on electricity regions is made according to the distribution of existing turbines.

MW Most likely competitive Competitive TotalEstonia 800 5.100 5.900 Finland 11.600 28.600 40.200 Latvia 2.900 5.100 8.000 Lithuania 3.400 3.800 7.200 Norway 5.800 9.300 15.100 NW Russia - - - Poland 11.400 22.200 33.600 Sweden 22.300 21.600 43.900

Table 38: Onshore wind power potential in 2030 (MW)


It appears from Table 38 that potential for wind power is very significant in all countries in the region compared to their current installed capacity. It should be stressed however that the above numbers are associated with a high-level of inherent uncertainty because they reflect a certain level of social acceptance.

Note: Special assumptions regarding wind in mountainous areasThe EEA study assumes a lower “power density” at sites above 600 m of sea level. Moreover, areas might be more isolated as the terrain is more complex for large wind farms . The power density of 8 MW/km2 applied to all types of land uses is set to 4 MW/km2 for mountainous areas. Moreover, it is assumed that wind farms should be sited below 2 000 m above sea level since access to roads and grid connections above 2 000 m is quite restricted.

Off-shore wind potentialOff-shore wind power potentials on a country per country level have been assessed using figures from the technical background report, “ETC/ACC Technical Paper 2008/6”20 which supports support the above-mentioned EEA report.

The appraisal of the off-shore potentials is based on the following overall assumptions:

- Depths above 50 m are excluded from the analysis- Sites with less than 2500 full-load-hours are excluded from the

analysis- The raw technical off-shore potential is constrained by a number of

factors including visual impacts, shipping routes, military platforms, oil and gas exploration, touristic zones etc. Constrained potential is assumed to be:

o 4 % of the area within 0-10 km from shoreo 10 % of area within 0-50 km from shoreo 20 % of area above km from shore

Based on these assumptions we obtain the following potentials in MW per country, grouped with three categories related to the distance to shore.

20 http://www.world-nuclear.org/info/inf102.html (2009-02-03).



MW Far offshore

(>50 km)

Offshore

(20-50 km)

Near offshore

(0-20 km)

SUM

Turkey 286 400 160 847

Spain 1.825 1.069 328 3.222

Romania - 389 181 570

Germany 55.713 16.623 2.557 74.893

France 22.029 22.730 8.551 53.311

Belgium 1.760 4.290 1.838 7.888

Norway 70.680 17.453 1.548 89.682

Netherlands 50.476 14.923 2.253 67.651

UK 114.063 39.479 7.778 161.319

Bulgaria - 2.206 1.030 3.236

Portugal - 1.921 896 2.817

Poland 5.040 4.356 1.562 10.958

Sweden 26.344 13.065 3.638 43.048

Italy - 6.510 3.038 9.547

Greece 4.533 4.407 1.633 10.573

Cyprus - 105 49 154

Lithuania 867 837 310 2.014

Finland 16.192 13.606 4.838 34.637

Ireland 33.227 16.317 4.514 54.058

Denmark 97.225 30.598 5.205 133.027

Latvia 3.767 6.363 2.618 12.748

Estonia 6.112 8.955 3.609 18.676

Table 39: Country specific off-shore wind potentials. Own calculations based on “ETC/ACC Technical Paper 2008/6.


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