Investigated scenarios & the accompanying key assumptions

Definition of scenarios

The aim of the scenario runs is to analyse the effects of different support schemes – both harmonised and non-harmonised policies among the EU 15 Member States – with respect to RES-E deployment, investment needs, generation costs and transfer costs for consumers.

The RES-E Directive (EC/77/01) sets a minimum framework for RES-E policy. However, in line with the Principle of Subsidiary, it allows each MS to choose the support scheme, which “corresponds best to its particular situation”. Taking account of the wide diversity of promotion schemes between Member States, the Directive states that it is too early to set a Community-wide framework regarding support schemes. By 10/27/2005, the Commission should present a report on the experience gained with the application and coexistence of different support schemes in the Member States. The report may be accompanied by a proposal for a Community framework for RES support schemes (art.4.2). However, it does not prejudge what the RES-E policy scheme should be used for in the future. Not even if a common RES-E promotion scheme should be implemented. The directive also stipulates that such a proposal for a harmonised support framework should allow a transition period of at least 7 years (thereafter) in order to maintain investors’ confidence and avoid stranded costs. Therefore, at least in the short/medium-term, national support schemes will continue to be used by MS to promote RES-E. In the future – at least ‑ some sort of combination of a community framework (harmonisation) and continuation of MS policies for new and existing capacity is possible.

The model runs try to consider the spread of possible RES-E policy deployment within the EU in the following way:

• No harmonisation, where currently implemented policies remains available (without any adaptation), i.e. business as usual (BAU) forecast (scenario B1)

• After a transition period of 7 years, a harmonisation of the support schemes takes places. To be able to analyse the effect of different (harmonised) policies compared to the status quo (BAU) it is assumed that the same RES-E target as under BAU conditions should be reached by 2020. The following currently must promising and favourable policies should be investigated under harmonised conditions:

  -    Feed-in tariff (B2)

  -    International TGC system (B3)

  -    National TGC system (B4)

• To investigate how the RES-E target influences the efficiency of different support schemes, a second more ambitious RES-E target should be reached in 2020. More precisely, it is assumed that 1000 TWh should come from RES-E technologies in 2020 assuming:

  -    Current policy (BAU) up to 2012 ‑ 7 year transition period ‑ and a harmonised system thereafter. Again the goal should be reached by applying the following support mechanisms:

  -    Feed-in tariff (H1)

  -    International TGC system (H2)

  -    Harmonisation should already take place in 2005 and the indicative RES-E target in 2010 should be reached. Therefore, the effects of “early actions” and a high interim target (2010 goal) can be shown (scenario H3 to H5).

Figure 1 gives an overview of the investigated scenario paths.

Figure 1. Investigated cases

General scenario assumptions

►    Gross electricity consumption

Electricity demand according to DG TREN Outlook 2030: European Energy and Transport Trends to 2030 Outlook (Mantzos et. al 2003) – Baseline forecast. This means that electricity demand rise – on average – by 1.8% p. a. up to 2010 and by 1.5 % p. a. thereafter. Of course, on country level different demand projections are used. For example while the demand forecast for France is 2.2% p.a. up to 2010, a projection of only 1.1% p.a. is assumed for Germany.

►    Primary energy prices for biomass products

Figure 2 gives an overview about the variations of biomass prices in EU 15 countries. The price level differs among the countries and biomass fractions. Current prices are based on an assessment conducted within the Green-X project and are expressed in €2002. Prices are lowest for biowaste, followed by forestry and agricultural residues, and they are high for both forestry and agricultural products. It is assumed that the costs for bioenergy products remain constant till 2010. In the period 2010-2015 a slight rise of 0.5% per annum and after 2015 a price increase of 1% is projected.

Figure 2. Variation of the prices for the different biomass products in EU 15

►    Electricity prices

For each EU 15 Member State the power price is derived endogenously within the Green-X model considering interconnection constraint among the countries. The calculations are based on

–    Primary energy projections from the WETO project .

–   Different CO₂-policy assumptions[1], namely no-CO₂ constraint, medium CO₂ constraint (assuming a tradable emission allowance price up to 10 €/t-CO₂) and high CO₂ constraint (assuming a tradable emission allowance price up to 20 €/t-CO₂)

–    Note, RES-E policy significantly influences the power market price.

►    Interest rate / weighted average cost of capital

The determination of the necessary rate of return is based on the weighted average cost of capital (WACC) methodology.[2] Two options are considered in the analysis, namely 6.5% and 8.6%. The different values are based on different risk assessment, one standard risk level and a higher risk level characterised by a higher expected market rate of return. The 6.5% value is used as the default value; the 8.6% is used for the sensitivity analysis and is applied in scenarios with lower stable planning conditions and support schemes cause a higher risk for the investors (TGC system). To analyse the effects of different strategies, for the simulation no technology-specific risk premiums (different WACC according to their maturity and risk characteristics) are used.[3]

►    Future cost projection – technological learning

Within the model Green-X the following dynamic developments of the electricity generation technologies are considered

–    Investment costs (experience curves or expert forecast)

–    Operation & Maintenance costs (expert forecast)

–    Improvement of the energy efficiency (expert forecast)

For most technologies the investment cost forecast is based on technological learning, see Table 1. As learning is taking place on the international level the deployment of a technology on the global level must be considered. For the model runs global deployment consists of the following components:

–    Deployment within the EU 15 Member States is endogenously determined, i.e. is derived within the model

–    For the new EU Member States (EU-10+) forecasts of the future development by RES-E categories are taken from the project ‘FORRES 2020’; for details see Ragwitz et. al. (2004).

–    Expected developments in the ‘Rest of the world’ are based on forecasts as presented in the IEA World Energy Outlook 2004 (IEA, 2004).

Dynamic assessment of investment costs for different RES-E technologies

RES-E category	Applied approach	Assumptions
Biogas	Experience curve (global)	LR (learning rate) = 5%
Biomass	Experience curve (global)	LR = 5%
Geothermal electricity	Experience curve (global)	LR = 5%
Hydropower	Expert forecast	No cost decrease in considered period
Photovoltaics	Experience curve (global)	LR = 15% up to 2010, 10% after 2010
Solar thermal electricity	Experience curve (global)	LR = 15% up to 2010, 10% after 2010
Tidal & Wave	Expert forecast	Cost decrease 5%/year up to 2010, 1%/year after 2010
Wind on- & offshore	Experience curve (global)	LR = 9%