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Estonian energy scenarios 2030 2050. Reference and single track scenarios in Balmorel. introduction. Process. 4 December 2012: Kick-off meeting 7+8 January 2013: Scenarios defined 26 February: Test results for two scenarios 8 April: Test results for all scenarios
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Estonian energy scenarios 2030 2050 Reference and single track scenarios in Balmorel
Process 4 December 2012: Kick-off meeting 7+8 January 2013: Scenarios defined 26 February: Test results for two scenarios 8 April: Test results for all scenarios 9 April: Defining combination scenario 23 April: Skype 7 May: Draft final report 14 May: Meeting 31 May: Final report
Purpose of this presentation • It is draft model runs – not final results • 30 time steps per year • Will be increased to 72 • Can be used to discuss assumptions • Input to discussion of combination scenario
Scenarios 110% Medium CO2 price Reference CO2 market collapse (0€) Carbon leakage (0€ in Russia) Renewable energy focus Liberal market CO2concern(100€) EE • Reference and single track scenarios has been simulated. Focus on electricity and district heating.
Input driven scenarios • Define input • Demand, fuel costs, CO2 price, technology costs, existing plants and transmissions lines • Use “fundamental model” • Optimal dispatch • Optimal investments (simplifyed) • Study results • Prices, costs • Emissions
Scenario table of key assumptions * Zero in Russia
Status • Model runs are successful • Socio-economic evaluation will be included before meeting! • Renewable energy focus-scenario needs to be reformulated! • Now: • Only investment in new capacity in Estonia in the form of RE • 100% national generation of electricity • Should be: • Only investment in new capacity in Estonia in the form of RE • Increasing requirement for RE generation in Estonia • Work with Stream model is in progress • Result send to demand group for comments
Introduction: Balmorel • Optimal dispatch (a given year, with given technology) • Input: • Electricity and heat demand • Fuel prices • Capacities (generators, transmission) and efficiencies • Output: • Generation per unit, flow between areas • Costs, emissions • Optimal investments • Input: • Cost of technologies • Interest rate, time horizon • Output: • New MW generation and transmission • Interpretation: • New capacity is build based on expected development (if profitable in year X) • Construction time = Horizon for expectation • No strategic behaviour
“Optimal dispatch and marginal price” Area 2 Generation: 0-100 MW Marginal price: 150 X/MWh Area 1: Generation: 0-100 MW Marginal price: 100 X/MWh 50 MW
Model set-up • Model area • Baltic states, Nordic countries, Poland, Germany, NW Russia and Belarus • Belarus modelled as transit country (No demand, no power plants) • 23 price areas
NO_S NO_N NO_M NO_O SE_N SE_M FI DK_W DK_E SE_S RU_KAL LV DE_NW DE_NE LT EE DE_CS PL BLR RU 2011
Updated assumptions • Base year set to 2012 and investments in new generation capacity from 2020 • Possibility for rebuilding selected oil shale plants to coal or biomass (Narva 8, 11 and Auvere ) • Updated Estonian electricity demand forecast and grid loss • Oil shale price: set to mining costs in 2012 and short term opportunity costs from 2020 and onwards (function of oil and CO2 price) • Estonian biogas price and resource • Investment option for oil shale power plants • Estonian wood chips price reduced by 1.75 EUR/GJ per year • Nuclear in Poland postponed to 2025 • CO2 leakage to Russia • SameCO2 price in Russia as within the EU (in most scenarios): Assuming no leakage • New Carbon leakage scenario • Data feedback from Lithuanian TSO
Levelised cost of energy Text will be added 2030 and 2050
Electricity generation – model area Wind Coal & lignite Coal CCS Natural gas Nuclear Hydro Note: Different step of X-axis, corresponding to simulated years
Electriricygeneraton by country Map will be added
Electricity generation – EstoniaReference and Liberal market Note: higher coal and natural gas generation in reference scenario due to 110 % capacity requirement
Electricity generation – EstoniaCO2 concern and CO2 market collapse Note: CO2 market collapse leads to heavy coal generation
Electricity generation – EstoniaRE focus and No CO2 price in Russia Note: RE scenario method leads to increased oil shale generation. Consider RE % methods instead of investment constraint.
Rebuilding of oil shale plants Text will be added Detailedresultsaboutrebuilding of plants
CO2 emission – model area Endogenous investments from 2020
Investment in elec. generation – model area Note 3: Wind and solar investments in 2040 and beyond are primarily reinvestments in Germany due to NREAP Note 1: Large investments in first year with endogenousinvestments indicate an unbalance in earlier years Note 2: Remember 5 years time step in the last part: More investments per time step
Electricity prices - Estonia • Note: • Investments from 2020 results in changes electricity price • Fuel prices are the same in all scenarios
Import balance Reference CO2 concern CO2 market collapse No CO2 price in Russia Note: Postivevaluesareexport – negative import.
Investments in transmission - MW Text will be added
Socio-economics Text will be added
Observations - Estonia Text will be added
Observations – surrounding system Text will be added
Next steps • Reformulate RE scenario • Increasing requirement for RE generation in Estonia: x% by 2030, X% by 2050 • Forecast for district heating demand and check of district heatingareas • Update Latvian data (if we receive feedback) • Increase time resolution
Reference scenario Reference scenario – Business as usual i.e. with a requirement of having inland production capacity equal to 110 % of the hourly peak demand, current trend in energy efficiency, an oil shale price is a function of the international oil price, and WEO 2012 forecast of CO2 prices in their new policy scenario i.e. 23-31-34 €/ton CO2 in 2020-2030-2035, respectively. The price in 2050 set to 45 €/ton CO2. The 110 % requirement is calculated as follows: 110 % of peak demand – 150 MW
Liberal market scenario Liberal market scenario – A scenario with reduced requirements for inland Estonian electricity capacity.In this scenario the impact of setting a lower capacity requirement is analysed. This scenario have no specific requirement for Estonian capacity.
Oil shale opportunity costsMethod The opportunity costs of oil shale seen from the existing power plants at Narva from 2011 to 2050. The model will then consider the efficiencies at existing Narva power plants and electricity prices etc. This substitution price could be estimated as either the short or long term marginal costs: • Short term costs: • fuel oil price x refinery efficiency - oil shale refinery OPEX – refinery CO2-costs • Long term costs: • fuel oil price x refinery efficiency - oil shale refinery CAPEX - oil shale refinery OPEX – refinery CO2-costs. We assume the refineries are already in operation and base our cost estimate on short term marginal costs.
Oil shale opportunity costsAssumptions Reference oil price set to fuel oil based on price forecast from IEA World Energy Outlook 2012. Mining fee (royalty) 2011-2014: 1,1 euro/tonnes. 2014-2050: 2,4 euro/tonnes Mining costs (ex transport and royalty): 2011: 10,5 euro/tonnes, 2030: (10,5+16)/2= 13,25 euro/tonnes. 2050: 16 euro/tonnes. For the years between these points I have made a linear projection. Transport costs to Narva: 3 euro/tonnes in all years OPEX of refinery: 21 euro/tonnes in all years. CAPEX of refinery: Average of Enefit and Petroter: 10 euro/tonnes per year with an interest rate of 10 % and 20 years pay back time. 1 tonnes of oil shale rock set to contain 2,33 MWh or 8,33 GJ energy - based on the report of the resource group. Refinery efficiency set to 70 % based on the report of the resource group. This is in line with the efficiency of the existing Petroter refinery. CO2 price forecast based on IEA World Energy Outlook New Policies scenario with an adjustment to the historic 2011 and 2012 CO2 price level. CO2 emission based on Enefit 280 data: 0,36 tonnes CO2/bbl shale oil. I have estimated the calorific value of 1 bbl oil shale to 5,52 GJ and used an refinery efficiency of 70 %.