Approach and key assumptions in our modelling exercise on RES in SEE countries. a regional approach to support renewable energies

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1 SEERMAP: Modelling results & conclusions Focus on Renewable Energies Gustav Resch, Lukas Liebmann, Albert Hiesl TU Wien, Energy Economics Group László Szabó, András Mezősi, Zsuzsanna Pató, Ágnes Kelemen REKK Foundation Vienna, Energy Community Secretariat 14 September

2 Content RES modelling in SEERMAP: Approach and key assumptions in our modelling exercise on RES in SEE countries a regional approach to support renewable energies Key results from the modelling of RES developments and related impacts 2

3 Models applied and interlinkages Input to Green-X: Electricity demand, wholesale price, market values of RES technologies Output: installed capacities of RES technologies, investments, support expenditures 3

4 Green-X model (TU Wien) Simulation model for assessing the impact of energy policy instruments in the European (renewable) energy market RES-E, RES-H, RES-T and CHP, conventional power Based on the concept of dynamic cost-resource curves Allowing to develop scenarios up to 2030(2050) on national / regional / European level Base input information Country selection Technology selection Power generation (Access Database) Electricity demand reduction (Access Database) Economic market and policy assessment potential, costs, offer prices Simulation of market interactions RES-E, CHP, DSM power market, EUAs Scenario Information Policy strategies selection Social behaviour Investor/consumer Externalities Framework Conditions (Access Database) Results Costs and Benefits on a yearly basis ( ( ) ) Reference clients: European Commission (DG RESEARCH, DG TREN, DG ENV, DG ENER), European Environmental Agency, Consultation to Ministries in Serbia, Luxembourg, Morocco, Ireland, Germany etc. 4

5 Green-X model (TU Wien) A brief repetition: Green-X approach at a glance costs band 1 band 2 band 3 potential Long-term (up to 2050) realisable potentials in year n & corresponding costs at country level by energy technology (subdivided into several bands) The Green-X approach: costs potential Potential Cost (efficiency) Technology diffusion ( S-curve ) (non-economic barriers by technology/country) Technological change ((global) learning curves by technology) Dynamic cost-resource curves Realisable yearly potentials in year n & P FIT costs potential Energy policy (energy prices, support schemes) a detailed energy policy representation Deployment in year n and corresponding costs & benefits 5

6 Potentials for RES in the Green-X database Energy generation Definition of the (additional) realisable long-term potential (up to 2020/2030/2050) Historical deployment Theoretical potential Technical potential Maximal time-path for penetration (Realisable Potential) Barriers (non-economic) Short-term potential (2020) Economic Potential (without additional support) Definition of potential terms Theoretical potential... based on the determination of the energy flow. Technical potential based on technical boundary conditions (i.e. efficiencies of conversion technologies, overall technical limitations as e.g. the available land area to install wind turbines) Long-term potential R&D Policy, Society Additional realisable long-term potential (up to 2050)

7 Wind energy potential: Approach used in Green-X Example: Wind onshore (1) Potential derived from GIS-based wind data (2) Merged with land use constraints (Corinne land use database, spatial planning consideration) (3) Merged with default simple power system constraints (max. wind capacity below estimated peak load 2050) Figure: GIS-based wind map Figure: Land use constraints for wind onshore (based on Corinne land use database) (incl. wind data) 7

8 Wind energy potential: Derived data for Green-X database Artifical surfaces 0% Arable land 25.0% Permanent crops 15.0% Pastures 20.0% Heterogeneous agricultural areas % Heterogeneous agricultural areas % Heterogeneous agricultural areas 3 (agro-forestry) 5.0% Forests 5.0% Natural grasslands, moors 22.5% Sclerophyllous vegetation & Transitional woodland-shrub 22.5% Beaches, dunes, sands 10.0% Bare rocks 0.0% Sparsely vegetated areas 30.0% Burnt areas & glaciers 0.0% Inland wetlands 5.0% Maritime wetlands 5.0% Inland waters 0% Marine waters 0% TOTAL POTENTIAL with land use restrictions, without power system constraints (Capacity potential) [MW] RESTRICTED POTENTIAL with land use restrictions, with default power system constraints (Capacity potential) [MW] Applied land use constraints: Suitability for wind power plants Long-term (2050) potentials for Wind Onshore in SEE countries GR BG RO AL BA KO ME MK SR Greece Bulgaria Romania Albania Bosnia H * Kosovo* Montenegro FYR of Macedonia * considering feedback from national experts to reflect country specifics (islands) Example: Wind onshore Serbia

9 Solar energy potential (for electricity sector): Approach used in Green-X Example: Photovoltaics (1) Potential derived from GISbased solar irradiation data (2) Merged with land use constraints for central PV ( free field ): max. 0.75% of agricultural land area, for decentral PV based on area per capita (IEA-PVPS approach) approximations (3) Merged with default simple power system constraints (max. solar capacity below estimated peak load 2050 (i.e. default: < 75% of peak load ) Figure: GIS-based solar map (relevant for PV) 9

10 Hydropower potential: Approach used in Green-X Data on potentials for small (< 10 MW) and large-scale hydropower plants based on a detailed literature analysis, refined over the last 15 years (considering latest information from European / regional / national studies and expert evaluations) E.g. for Western Balkans based on a detailed bottom-up assessment conducted by Joanneum Research done in the EU-IEE-project BETTER ( 10

11 Overview on RES potentials & costs in SEE countries Figure: Supply curves for selected renewable energy technologies in SEE region (using cost from today s perspective) Additional long-term (2050) potential Achieved potential % 400% AL BA KO MK ME SR RES-E - Electricity generation potential [TWh/yr.] 350% 300% 250% 200% Biogas (Solid) Biomass Biowaste Geothermal electricity Hydro large-scale Hydro small-scale Photovoltaics Solar thermal electricity Tide & Wave Wind onshore Wind offshore 150% 100% 50% 0% Figure: Overview on potentials for RES electricity in SEE countries (non-eu only) RES-E - Electricity generation potential [% of gross electricity demand (2005)] AL BA KO MK ME SR

12 RES in SEERMAP: Key inputs to the modelling exercise To ensure maximum consistency the key input parameters of the Green-X scenarios are (as default) based on REKK s EEMM modelling and the (updates of the) Green-X database. Based on EEMM Energy demand by sector Primary energy and carbon prices derived wholesale electricity prices and market values for variable RES Conventional supply portfolio and conversion efficiencies Defined for this study RES policy framework RES potential (Green-X database) RES cost & learning rates (Green-X database, incl. biomass) Technology diffusion (non-cost barriers) Financing conditions (WACC) Table: Main input sources for scenario parameters

13 RES in SEERMAP: Financing conditions (Weighted Average Cost of Capital) WACC assumptions and the impact of risk (policy, technology, country) WACC c,t,p = WACC default * f c * f t * f p Default assumptions concerning energy technologies, exemplified for Austria WACC (in Austria) default (real) ideal posttax (nominal) 6.5% 4.9% pretax (nominal) 8.7% 6.5% pretax (real) 7.4% 5.3% Note: Through complementary measures the investor risk can be reduced, from real to ideal (according to an assessment conducted in the DIA-CORE project) Source: Dia-Core project ( Policy risk: Instrument-specific risk factor (i.e. multiplier of default WACC) Technology-specific risk factor (i.e. multiplier of default WACC) RES-electricity Biogas Solid biomass 1.05 Biowaste 1.05 Geothermal electricity 1.1 Hydro large-scale 0.95 Hydro small-scale 0.95 Photovoltaics Solar thermal electricity 1.1 (1.0) Tide & wave 1.4 (1.2) Wind onshore 0.95 Wind offshore 1.4 (1.15) Note: Numbers in brackets refer to the period post FIT (feed-in tariff) 1.00 FIP (feed-in premium) 1.10 QUO (quota system with uniform tradable green certificates (TGC)) 1.20 ETS only (Emission Trading Scheme only - no dedicated RES support) 1.30 TEN (tenders for selected RES-E technologies) 1.15

14 RES in SEERMAP: Financing conditions (Weighted Average Cost of Capital) The impact of country-specific risk our alternative approach GR BG RO EU28 AL BA MK ME SR KO Alternative country risk setting DIA-CORE figures 182% 120% 145% 2016 data weighting factor Eurostat - long term government bond yields 10% RES deployment times risk ranking Default risk multiplication factor 738% 207% 282% 100% National Credit Rating 90% RES deployment times risk ranking Default risk multiplication factor 189% 151% 126% 100% 189% 189% 151% 189% 151% 151% Ease of getting credit 0% RES deployment times risk ranking Default risk multiplication factor 125% 89% 73% 100% Average risk rating 244% 157% 142% 100% 189% 189% 151% 189% 151% 151% Smootheining factor - low 75% 208% 143% 131% 100% 167% 167% 138% 167% 138% 138% Smootheining factor - medium (default) 50% 172% 128% 121% 100% 144% 144% 126% 144% 126% 126% Smootheining factor - high 25% 127% 111% 108% 100% 117% 117% 110% 117% 110% 110% Smootheining factor - very high 13% 118% 107% 105% 100% 111% 111% 106% 111% 106% 106% Sources: Alternative country risk setting Eurostat - long term government bond yields National Credit Rating Ease of getting credit

15 RES in SEERMAP: Financing conditions (Weighted Average Cost of Capital) The impact of country-specific risk an update/extension of the DIA-CORE study 10% 9% 8% Figure: Default WACC used in modelling (in the case of regionally harmonised policy approaches) 7% 6% 5% 4% 3% 2% 1% 0% Financing conditions align in the mid to long term differences diminish thanks to expected economic progress AT GR BG RO AL BA FYROM ME SR KO* How would a common (regional) policy influence financing conditions? A regional (or EU wide harmonised) policy would have an averaging effect: OUR approach 50% determined by default country risk, 50% by regional (average) risk

16 3 scenarios 16

17 The assumptions behind the scenarios KEY ASSUMPTIONS No Target Delayed Decarbon CO 2 target No target 94% reduction 94% reduction Fossil plants National plans: all PPs National plans: all PPs National plans: only PPs with FID SEERMAP RES target Phase out of support (for new installations) after 2020 Continuation of current policies until 2035, and afterwards a strong uptake More ambitious RES deployment from 2020 to reach the 2050 target Shared assumptions demand, CO 2 and fossil fuel prices, gas infrastructure, WACC, NTCs 17

18 RES in SEERMAP: Policy concept for RES The policy concept used in our default scenarios A regional policy approach post As default we assumed a regionally harmonised (i.e. for the whole SEERMAP region, including all 9 countries) policy concept for supporting new RES installations post 2020 with this one can take the full benefits of doing it regionally rather than purely nationally, increasing cost-effectiveness and allowing to use renewables there where most beneficial / attractive from a system perspective The required uptake of new RES (installed post 2020) across the SEERMAP region is shown below (as %share in gross electricity demand) No Target Delayed Decarbon

19 RES in SEERMAP: Policy concept for RES The policy concept used in our default scenarios A regional policy approach post 2020 Different policy instruments for supporting the uptake of RES-electricity are assessed: - Regionally harmonised quota scheme with tradable green certificates technology-neutral, uniform pricing - Feed-in premium scheme with technology-specific auctions for price determination (pay-as-bid) - National quota schemes with tradable green certificates technology-neutral, uniform pricing Sensitivity analysis on various key aspects (RES cooperation, carbon value, electricity demand, etc.)

20 MODELLING RESULTS FOR THE SEERMAP REGION 20

21 Installed capacity Gradual phase out of fossil capacities Role of natural gas is uncertain: bridging role in decarbonisation scenario, and more permanent in others Dynamic uptake of RES technologies, especially wind and solar including the no target scenario 21

22 Gross electricity mix Coal based generation disappears form electricity mix Gas consumption peaks in , and downward trend afterwards. Trade position of the region slightly deteriorates RES domination in the generation mix after

23 Gross electricity mix by country in 2050 Intermittent RES dominated countries: GR, RO Hydro domination in: AL, BA, ME, MK, RS RES shares above 100% in AL and ME Significant import in RS and BG; Exporting countries: AL, BA and ME 23

24 Fossil and RES investments All scenarios require dynamic investment uptake in the region. Investments in RES dominate the period post

25 Magnitude of wholesale price and RES support Increasing wholesale price level over the period, reaching above 80 /MWh by 2050 Low variable costs of RES reduces wholesale prices by 2050 compared to no target RES support expenditures decrease over time and become negligible by 2050 In delayed scenario sharp increase of support expenditures in the years post

26 A closer look at RES support Support expenditures, expressed as premium per MWh electricity demand [ /MWh DEMAND ] No target Delay Decarbon (Green Certificates) Decarbon (Auctions for sliding premia) In delayed scenario sharp increase of support expenditures in the years post 2040 the required strong market uptake requires to make use of not only the low hanging fruits which makes it expensive at the very end Auctions for sliding feed-in premiums appear beneficial compared to uniform (technologyneutral) green certificate trading schemes to support to required RES uptake 26

27 A closer look at RES support: sensitivity assessment Support expenditures, expressed as premium per MWh electricity demand [ /MWh DEMAND ] ,4 No target 6,4 Delay 3,8 Decarbon Sensitivity analysis 16,7 14,4 9,7 7,8 2,9 Low Demand High Demand Low CO2 Price National targets Constraint Potentials Low Demand: contributes to decarbonisation and lowers the cost for renewables (-24% compared to default (decarbon)) High Demand: vice versa makes decarbonisation a challenge and increases the cost for renewables (+155%) National targets / No RES cooperation: doubles the cost for renewables (+104%) Constraint potentials (for wind and hydro): decarbonisation becomes very costly (+337%) and probably a mission impossible Figure: Average (2016 to 2050) support expenditures, expressed as premium per MWh electricity demand 27

28 RES support vs ETS Auction Revenues Revenue from the auction of carbon allowances under the EU ETS as a potential source of financing for RES investment / support. Results show that in the region as a whole ETS auctioning revenues are generally* more than sufficient to cover the necessary RES support Country level results can however differ from that general trend *Exception: last decade in the delayed scenario 28

29 Conclusions The high penetration of RES in all scenarios suggests that a robust no-regret action for countries in the SEERMAP region is to focus on enabling RES integration. This involves: investing in transmission and distribution networks including cross border capacities, enabling demand side management and RES generation through a combination of technical solutions and appropriate regulatory practices, and promoting investment in storage solutions including those with regional relevance such as pumping hydro as well as small scale storage. Delayed action on renewables is feasible, but it has two disadvantages compared with a long term planned effort. stranded assets in fossil based generation, including power plants which are currently planned. the disproportionate effort needed towards the end of the modelled period leads to a significant increase in RES support will be required. 29

30 Conclusions Decarbonisation of the electricity sector does not drive up wholesale electricity prices compared to a scenario where no emission reduction target is set. For meeting decarbonisation targets there is a need for dedicated support of renewable energies also in the period post Expected increases in wholesale prices help to lower the financial gap that needs to be filled by dedicated RES support. At the level of the region on average auctioning revenues from the EU ETS are more than sufficient to cover RES support needs, and although country level results differ, in all countries a potentially significant share of the RES support needed for decarbonisation of the electricity sector can be covered from EU ETS revenues. The need for long term RES support highlights the need for long term evidence based policy planning to provide investors with the necessary stability to ensure that sufficient renewable investments will take place. 30

31 Thanks for your attention! In case of questions/remarks (or ) 31