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SLED scenario assessment for Albania

SLED scenario assessment for Albania. László Szabó, PhD – András Mezősi PhD Regional Centre for Energy Policy Research Tirana , Albania October 26 , 201 5. Outline of the presentation. 1. Modelling methodology 2. Scenario definitions Main assumptions on dem a nd supply and taxation

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SLED scenario assessment for Albania

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  1. SLED scenario assessment for Albania László Szabó, PhD – András Mezősi PhD Regional Centre for Energy Policy Research Tirana, Albania October 26, 2015

  2. Outline of the presentation 1. Modelling methodology 2. Scenario definitions • Main assumptions on demand supply and taxation • Input data 3. Model results • Prices • Generation mix • Carbon emissions • RES support costs • Investment costs

  3. Methodology The SLED analysis is based on assessing three scenarios: • Reference scenario (REF); • Currently Planned Policies (CPP); • Ambitious Climate Scenario (AMB). • Scenario assumptions were related to six dimensions: • carbon value; • energy/excise tax; • environmental standards; • deployment of renewable energy technologies; • deployment of conventional generation technologies; and • electricity demand (integrating assumptions on end-use energy efficiency improvement). • Main tools: Electricity Market Model and Network model

  4. 1. European Electricity Market Model and EKC networkmodel

  5. Introduction • Market impacts in the three analysed scenarios of SLED (REF, CPP, AMB) are modelled with REKK European Electricity Market Model (EEMM) • Highlights: • Electricity trade is modelled within the whole EU • Hydro generation is modelled under average rainfall conditions, but in the sensitivity assessment the impacts of dry years are also simulated • Fossil generation modelling is important even in Albania, as in the future two gas fired blocks are planned.

  6. Model functionality Comments: • The map shows the main results of the model: • Competitive market equilibrium prices by countries • Electricity flows and congestions on cross-border capacities • 36 countries are handled in the model. • Morocco, Tunisia, Turkey, Moldova, Russia and Belarus are considered as exogenous markets • In these markets the net export position are equal with the fact in 2013 (assumed a baseload flow) • The model is calculating the marginal cost of around 5000 power plant blocks and sets up the merit order country by country. • Taking into consideration the merit order and exports/import, the model calculates equilibrium prices. • Power flow is ensured by 85 interconnectors between countries. 6

  7. Basic economics in the model • Competitive behavior by power generators • „if someone is willing to pay more for my energy than what it costs me to produce it, then I will produce” • Prices equalize supply and demand • Efficient cross-border capacity auctions • „we export electricity to wherever it is more expensive and import from wherever it is cheaper” • Capacity limits • in production and cross-border trade • Large country prices around the region are exogenous to the model, the rest are determined by the model

  8. Economic description and main assumptions Main model assumptions Main inputs and outputs of the model • The applied model is a partial equilibriummicroeconomic model in which a homogeneous product is traded in several neighboringmarkets. • Production and trade are perfectly competitive, there is no capacity withholding by market players. • Production takes place in capacity-constrained plants with marginal costs and no fixed cost. • Electricity flows are modeled as bilateral commercial arrangements between marketswith a special spatial structure. • Power flows on an interconnector are limited by NTC values in each direction. • Fuel prices reflect power plant gate prices, transportation/ transmission costs are taken into consideration. • Only ETS countries buy CO2 allowances • The model calculates regional power supply – demand balance at certain capacity and import/export constraints • Demand evolution, power plant capacities, availability and cross border power flow defines market price • Fuel prices are estimated based on available information

  9. Model characteristics • In a year 90 reference hours are modelled, representing well the daily, weekly and seasonal variations • Power plant data comes from international database (PLATTS), but modelled country capacity data are coming from national sources of information • Future capacity expansion are from national strategic documents • Fossil fuel prices are based on international forecasts of EIA and IEA. • Natural gas price projections depend on the country: • TTF Spot price (Western Europe) • OIL index price • Mix of oil index and spot price

  10. Components of marginal cost

  11. Efficiency parameters, utilization rates • Taken from literature; dependent on the commission year and the type of the PP • Availability/utilization rates: • Hydro availabilities: dependent on country and season (based on historical utilization rates) • Wind an PV: taken from JRC

  12. Determining short-term marginal cost Short term marginal cost = Fuel cost + CO2 cost + Variable part of the OPEX + Energy tax

  13. Merit order curves - examples

  14. Modelled baseload prices in 2015 (€/MWh), and the yearly trade flows 14

  15. Modelled baseload prices in 2025 (€/MWh), and the yearly trade flows 15

  16. Model output • Equilibrium price in a demand period • Baseload and peakload prices • Electricity trade between countries • Price of cross border capacities • Production by plants • Gas consumption • CO2 emission

  17. Network modelling • EKC network model was used for the assessment • Representatives hours of years 2020 and 2025 were modelled, to assess the network impacts on the whole region • The following assessments were carried out: • Steady-state and contingency analyses • Evaluation of net transfer capacity • Transmission grid losses

  18. 2. Scenariodefinitions

  19. Outline • Main information sources • The consultation process • Scenario definitions • Main input datatothemodels

  20. Main information sources • Assessment of Energy Developments in Albania for the period 2012–2030 (USAID, January 2015) – providing demand and RES assumptions • Draft National Renewable Action Plan, June 15, 2015 (received from the Ministry of Energy in July 2015 as a document under revision) • National Renewable Action Plan of Albania (Ministry of Energy, 2014) • National Energy Efficiency Action Plan for the Republic of Albania 2010 • National Background Report on Energy for Albania, 2012 (WBC-Inco)

  21. The consultation process A two-phase feedback-loop was built in the SLED project: • 2014: consultation on main scenario assumptions • 2015: preliminary results were delivered - further alignment of assumptions and data – to reflect the INDC process of the country • Timeline:

  22. SLED Scenario definition- Reference

  23. SLED Scenario definition- CPP, AMB

  24. Harmonisation between USAID and SLED scenarios • Correspondence between USAID and SLED scenarios: • USAID scenarios were modified in two main fields: • Electricity consumption • Renewable capacity deployment

  25. Electricity consumption • Reference: consumption forecast of NREAP up till 2020, than 3.1% growth rate (average of the previous ten years) • CPP and AMB corresponds to the USAID energy efficiency and energy efficiency + renewables scenarios

  26. Renewable electricity assumptions Till 2020 we stick to the draft NREAP (2015) values in the various RES-E technologies • AMB scenario: USAID modelled growth rates between 2020-2030 • CPP scenario: 50% of the growth rate of AMB after 2020 • REF scenario: 25% of growth rate of AMB after 2020 In this way capacity development is deteremined, while production is forecasted by the model up till 2030 assuming country specific utilisation hour (solar and wind) and average rainfall for hydro

  27. RES-E capacities 1 REFERENCE scenario capacity values (MW)

  28. RES-E capacities 2 AMBITIOUS scenariocapacityvalues (MW)

  29. Present cross-border capacity HR HU 758 429 689 507 RS BA 403 488 162 250 BG 253 440 223 96 491 483 215 ME MK GR 329 151 0 223 400 IT 250 0 400 250 AL

  30. Planned cross-border capacities HR HU RO 800 800 RS BA 600 600 BG 500 600 1000 600 400 600 600 Under construction and approved categories are used in the model runs till 2030. While AL-MK is also built, IT-AL is not realised in the modelling period. ME MK GR 1000 600 1000 600 500 IT 500 500 AL

  31. Assumed capacities I. Present installed capacity New, planned non RES-E capacities • In Albania two CCGT plants are assumed in all scenarios according to official energy strategy: • CCGT Vlora I ( 200 MW) comes online in 2020 • CCGT Vlora II (160 MW) comes online in 2025

  32. 3. Scenario Assessment Results

  33. Outline • Wholesale price impacts • Generation mix, CO2 impacts • Impacts on system costs: • Investment costs, • RES support costs • Sensitivity assessment: Impacts of reduced rainfall • Network impacts • Contingencies • NTC valuations • Network loss impacts

  34. Modelling result – baseload electricity price, €/MWh in real term

  35. Modelling result – peakload price, €/MWh in real term

  36. Wholesale price evolution • Both baseload and peakload electricity wholesale prices have a significant drop between 2015-2020, followed by a slight increase in the later period. • The main factors influencing the wholesale price developments in Albania are the followings: • Generation expansion in the fossil based generation in the region is high. Over 7000 MW capacity (mainly lignite and coal) is built in the countries: AL; BA; BG; GR; HR; HU; ME; MK; RS; RO according to the national plans • New RES capacities above 12000 MW are also contributing to the price drop till 2020. • Higher interconnectedness in the region also allows trade of electricity (higher NTC) • These new capacity expansion is illustrated in the following slide for the region

  37. New PPs in the wider region* New coal-based power generation, MW New RES-E generation capacity, MW Region includes the following countries: AL; BA; BG; GR; HR;HU; ME; MK;RS;RO;

  38. Electricity mix

  39. Generation mix and CO2 emissions • Albania is characterised by expanding hydro capacities and significant net import share till 2030 in all scenarios to satisfy increasing demand for electricity • Although there is 360 MW of natural gas capacity installed in the CPP and AMB scenario is characterised by a close to zero (3 kt CO2) emissions by 2030. It is due to the fact that the plant is running at very low level according to the modelling results (<50h). • Other than hydro RES is only appear in the AMB scenario from 2020, however their contribution become significant only in 2030. • The results show, that the Albanian electricity system could cover electricity demand without installing both gas plants. The results question the economic rational for the investment in the planned gas capacities (360 MW), mainly the second phase, as the resulting utilisation rate the investment will have no chance to recover any cost. Gas is not competitive enough compared to import, at the assumed prices • In addition import is quite limited in the CPP and AMB scenarios

  40. Total investment cost of new PPs, m€, 2015-2030 Source of investment cost: Serbian Energy Strategy and Fraunhofer (2013) • There is a significant investment cost need in the various scenarios: • The Reference scenario has a 2.8 Billion € investment need over the following 15 years period, increasing over 6 Billion in the AMB scenario due to the higher RES expansion and to the gas plants. If this latter one is avoided. In this case investment cost would be below 6 Billion €. • The main contributing part is still hydro investments, but these are still the most economical RES options in the country.

  41. Calculation of the RES-E support budget • Support budget = (LCOEt-P)*Generated electricity • LCOEt: Levelized cost of electricity generation of technology t ~ average cost of electricity production • P: Modelled baseload electricity price (except PV, where peak load electricity prices are taken into account) • LCOE figures are based on literature data (Ecofys, 2014) • 55 €/MWh for hydro • 90 €/MWh for wind • 110 €/MWh for biomass • 105 €/MWh for PV • 80 €/MWh for geothermal • Baseload and peakload prices are the results of the modelling • RES fee = RES support budget/ electricity consumption

  42. Yearly RES-E support need, m€/year

  43. Unit RES-E support, €/MWh

  44. RES-E support • Present FIT support for new hydro is set around 70 €/MWh for capacities up to15 MW • LCOE values show that this level of support will be sufficient to cover Hydro based generation, but other types of RES-E would require higher rates. • The higher rates for the AMB scenarios shown in previous figure is due to the new RES capacities in biomass, PV and wind, so careful timing of these capacities should be planned. In PV and Wind high cost saving could still appear due to the technology learning effect.

  45. Sensitivity runs: dry years In order to check the impacts of a dry year sensitivity runs were carried out on all scenarios: • A severe drought is modelled (lowest precipitation of last 8 years) • Droughts assumed to take place in the whole region of South-East Europe • Capacity values are the same as in the original scenarios, but hydro availability reduced according to the reduced rainfall

  46. Impacts of reducing rainfall 1

  47. Impacts of reducing rainfall 2

  48. Impacts of reducing rainfall 3

  49. Network modelling results- contingencies • The increasing consumption level and new generation pattern causes problem in the network of Albania as shown in the table • In all scenarios, tripping of the line OHL 220 kV Fierza (AL) – Titan (AL) leads to overloading OHL 220 kV VauDejes (AL) – Komani (AL). New OHL 220 kV Komani-Titan solves this problem (70 km).

  50. NTC change with neighbours 2025 summer • Higher RES penetration has varying impact on NTC • The highest positive difference could be observed with RS, so trade opportunities increase in this directions, while in the other directions the impacts are mixed 2025 winter

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