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The deployment of electricity generation from renewable energies in Germany and Spain: A comparative analysis based on a simple model
Energy Policy 57 (2013) 552–562
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The deployment of electricity generation from renewable energies in Germany and Spain: A comparative analysis based on a simple model ˜ a Ortiz, Jorge Xiberta Bernat Pablo Ferna´ndez Ferna´ndez n, Eunice Villican Department of Energy, University of Oviedo, Oviedo, Spain
H I G H L I G H T S c c c c
Policies must be assessed according to the surcharge caused per unit generated. Surcharge evolution function fitted by an Erlang alike distribution. About two-third of the decade surcharge shall be devoted to units commissioned by 2010. Germany focused on technology development, while Spain on deployment.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 15 December 2011 Accepted 14 February 2013 Available online 16 March 2013
The fulfilment of the aims set by the European Union in the deployment of renewable energy sources for electricity generation (RES-E) has counted and must continue to count on public funding from the member states, which promote private investment in this type of facilities. This funding guarantees a cost-oriented remuneration which, being higher than the market price means an additional cost to the electricity system. With the aim of minimizing the economic impact as the weight of RES-E in the electricity mix increases, the generation costs of renewable units must approach those of the market, which are expected to increase according to the fossil fuel price forecasts. The present study analyzes both the RES-E development and deployment in Spain and Germany, two pioneering countries worldwide and with very similar electricity systems. Based on their national action plans and a simple model, this analysis approaches the RES-E surcharge, comparing and contrasting the results obtained in both countries. & 2013 Elsevier Ltd. All rights reserved.
Keywords: Electricity surcharge National renewable energy action plans (NREAP) Technology roadmaps
1. Introduction On 11 December 1997 most developed countries committed themselves in the city of Kyoto to implementing measures in order to cut by 2012 greenhouse gas emissions by 5% from 1990 levels. The European Union (EU), as a key player in this Protocol, pledged to further reduce emissions up to 8%, setting individual targets for each member state depending on economic and environmental variables. A reduction goal was therefore set in some countries like Germany at 21%, while enabling others like Spain to increase by 15%. The stronger economic growth in Spain from 1990 onwards caused a rise in emissions according to its higher energy demand, so that by 2007 Spain had already increased its emissions by 52.6%. Germany fulfilled that year for the first time the EU goal, with a reduction of 22.4% (EEA, 2009). Gas emissions have been reduced in Spain during the last 3 years n Correspondence to: ETS de Ingenieros de Minas. C/ Independencia, 13. 33004 Oviedo. Asturias. Spain. Tel.: þ 34 630406022; fax: þ 34 985104322. E-mail address: [email protected] (P. Ferna´ndez Ferna´ndez).
0301-4215/$ - see front matter & 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.enpol.2013.02.027
because of the economic crisis, posting in 2009 a growth of 26.8% compared to 1990. Germany maintained its path over these years, cutting its emissions slightly (EEA, 2011). Consumption control and energy efficiency, together with a major use of energy from renewable sources (RES), constitute the main measures taken by the EU to meet this challenge. The 1997 White Paper defined for the first time a European strategy and action plan to promote the market penetration of RES. It aimed at a reduction in the dependency on imports, increasing supply security and supporting national technical development and job creation, setting a global indicative target of doubling its overall use by 2010 from 6% in 1996 to 12%. In the Directive 2001/77/EC, Member States are required to advance in the regulatory framework regarding authorization procedures, reducing barriers to electricity production plants with RES, as well as to set national indicative targets. Finally, the Directive 2009/28/EC aimed for the first time at binding targets for each member state by the year 2020, and required them to adopt national renewable energy action plans (NREAP) in order to attain their goals. Those plans had to be notified to the Commission by 30 June 2010, according
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20%
Directive 2009/28/EC
White Paper 1997
18%
1996 1997
Table 1 Electricity generation share by source in Germany and Spain in 2010. Type
Source
Germany (%)
Spain (%)
Fossil sources
Natural gas Coal
61.0
13.4 42.2
43.5
23.8 8.8
Non-fossil sources
RES-E and hydro Nuclear
39.0
16.5 22.5
56.5
35.0 21.5
12% 8,5%
6%
553
5,8%
2005
2009 2010
2020
Fig. 1. RES European Acts and targets overview. Source: own illustration.
to a harmonized template established by the Commission Decision 2009/548/EC (EC, 2009). A binding RES share target of 20% in overall energy consumption by 2020 was set for the EU as a whole. This objective varies among the member states according to their initial level by 2005, as well as their potential. Germany must therefore increase their share from 5.8% in 2005 to 18% in 2020, whereas Spain’s target equals EU’s at 20%, compared with their initial 8.5% in 2005. Germany registered in 2010 an actual RES share of 10.9% in gross final consumption (BMU, 2011), while Spain of 13.2% (IDAE, 2011). Although these national targets referred to the overall use of RES, the national plans had to set out the breakdown by sector: electricity, heating and cooling and transport. The present study focused on analyzing the first sector, which amounted to 23.2% of the energy gross final consumption in Germany in 2010 and 24.6% in Spain. In Fig. 1, the RES European Acts and their targets are outlined in chronological order. In Germany, the 1991 Electricity Feed Act (Stromeinspeinsungsgesetz) enabled RES producers, mainly small companies, to access the grid owned by the big operators, guaranteeing them a special compensation. The 2000 Renewable Energy Sources Act (Erneubare Energien Gesetz) provided technology and site and resource specific tariffs in an attempt to double RES-E penetration. The recommendations identified by the successive progress reports on the EEG were implemented in the amending laws of 2004 and 2009 (EEG Novellierungen). Finally, in Germany the referred NREAP aimed to a RES share of 19.6% and a RES-E share of 38.6% (BMU, 2010). In Spain, the Electric Power Act 54/1997 liberalized the electricity market and established a special regime for RES-E producers, whose legal and economic framework was further consolidated in the Spanish Royal Decrees 436/2004 and 661/ 2007. Because of a strong growth in energy demand on those years and EU targets regarding RES deployment, the Spanish government approved in 2005 the Renewable Energy Plan, assuming a goal of 12.1% RES-E share by 2010 (MITyC, 2005). Finally, the Spanish NREAP aimed at an overall renewable share of 22.7%, 37.5%, when considering only the electricity sector (MITyC, 2010). In order to develop RES-E policies, both countries have decided on the same strategy, based on three milestones: removal of noneconomic barriers to RES-E development, generation costoriented compensation and its tendency towards electricity market price. That system, so-called feed-in tariff (FiT), guarantees a long-term grid access, ensuring the generated electricity to be taken and paid for at a fixed tariff, higher than the market price (ISI, 2005). All these similarities were behind the foundation of the joint project called Feed-In Cooperation by the governments of Germany and Spain in 2004, which carry out annually international workshops to promote the exchange of experiences regarding feed-in systems (IFIC, 2011).
Table 2 Total and RES-E generation and RES-E share in Germany and Spain. Country
Variable
2005
2010
2015
2020
Germany
RES-E generation (GW h) RES-E share (%) Total generation (GW h)
61.653 10.4 % 604.441
104.972 17.6 % 595.414
157.623 27.0 % 584.134
216.935 38.6 % 562.008
Spain
RES-E generation (GW h) RES-E share (%) Total generation (GW h)
53.773 18.0 % 293.731
84.034 28.1 % 299.345
110.988 32.7 % 339.931
150.030 37.5 % 400.420
On the other hand, both countries use the ‘‘merit order’’ principle in their electricity markets, which ranks energy sources in ascending order of production costs, so that those with the lowest costs are the first ones to meet demand. These are nuclear and large hydro power plants, most of them already amortized and with lower operating costs. RES-E also has the market access guaranteed, thanks to the FiT. Finally, fossil fuel power plants with the highest costs match the uncovered demand. In addition, the electricity mix in Germany and Spain is roughly balanced between fossil fuel plants (coal and natural gas) and non-fossil ones (nuclear and renewable). The generation breakdown by source in 2010 is shown in Table 1 (AGEB, 2011, REE, 2011). As shown in Table 2, according to their NREAPs both countries expected similar shares by 2020, around 38%, although Spanish deployment in 2010 was much higher than the German one, 28.1% and 17.6%, respectively. The distance is even bigger, when comparing the actual data from the year 2010 (16.8% in Germany and 29.2% in Spain) (BMU, 2011, IDAE, 2011), so that the German deployment effort over the next decade will be more demanding in terms of share. However, considering how overall generation is expected to develop over the next decade in both countries, i.e. a growth of 34% in Spain and a fall of 6% in Germany, their deployment efforts will level over the following decade.
2. Methodology The present study aims to assess the resulting support expenditures in Germany and Spain caused by the deployment for the selected RES-E technologies. Thus, a simple model is set up to calculate over the period 2010–2020 the additional cost required (RES-E surcharge) to achieve the expected deployment (RES-E share). These functions are determined by an own developed1 TMD model, given a set of variables and assumptions. This approach is shown in Fig. 2. 2.1. Technologies The scope of this study is limited to those technologies identified as relevant. The EU defined renewable sources as non-fossil sources, 1 TMD model is own developed simple model, which stands for its variables tariff, market (price) and deployment.
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554
Technologies
Variables
TMD Model
Results
Assumptions Fig. 2. Approach. Source: own illustration.
namely wind, solar, aerothermal, geothermal, hydrothermal and ocean energies, hydropower, biomass, landfill gas, sewage treatment plant gas and biogases (European Parliament, 2009). However, not all of these sources are supposed to play the same role in the RES-E deployment over the next decade. Hydropower has moved from being almost the only renewable energy developed some years ago, to nowadays meaning 20% out of the RES-E generation in Germany (5% out of the total) and 33% in Spain (10% out of the total). The vast majority of those plants are large-scale old units under the ordinary framework, which does not mean a surcharge to the system. Although some studies suggest that a large hydropower potential is still untouched, the development of such energy sources turns out to be rather complex because of their administrative procedures and environmental constraints. In order to overcome all these difficulties, both governments have focused on small-scale hydropower plants, integrating them into the special regimen for the production of electricity. Despite this support, those small sized facilities are less profitable. As few plants are planned to be commissioned over the next decade, hydropower yield is only a question of weather conditions. On the other hand, the minor contribution of other emerging technologies, such as geothermal or ocean energy, suggests that over the next decade, research, development and demonstration will still be real issues, instead of becoming relevant in the electricity mix. However, among all the RE sources, three of them, i.e. solar, wind and biomass, have proved to be the most relevant in terms of deployment over the last decade in both countries, and are set to remain so for the next one. How to increase renewable resource utilization has always been a key point in technical improvements: in terms of the conversion process in the case of solar energy (from photovoltaic to concentrating solar power), in terms of selection of the best locations in the case of wind energy (from on-shore to off-shore) or in terms of synergies with other sectors in the case of biomass (paper, landfill, manure, etc.). A brief description of the previous three technologies, as well as their actual state in Germany and Spain, is presented below.
2.1.1. Biomass The conversion of organic matter to energy is the largest single source of renewable energy today, and has the highest technical potential for expansion among renewable energy technologies due to its contribution to the three sectors. According to the conversion process, a distinction must be made between solid biomass (thermal process of combustion, gasification or co-combustion) and biogas (biological process of anaerobic digestion). Germany topped both solid biomass and biogas generation in Europe in 2009, and worldwide was only behind the USA in terms of solid biomass (EurObserver ER, 2010a, 2010b). On the other hand, Spain was the 11th and seventh country, far from its actual potential (MARM, 2010). Unlike the atomization of biomass generation in Germany, a small number of larger scale projects have been commissioned in Spain so far. For example, the number
of biogas plants in Germany by 2010 was estimated in 6.000, whose average size amounted to 380 kW (FBeV, 2010). However, not even 100 biogas plants were installed in Spain by 2010, with an average size of 2 MW (CNE, 2011). Such expansion in Germany has been a result of the spread of digesters run on slurry and manure by German farmers. On the contrary, 75% of the Spanish biogas generation comes from the conversion of organic matter in landfills (EurObserver ER, 2010b). 2.1.2. Wind energy The conversion of mechanical wind energy to electricity by means of a rotor connected to a generator has been the most expanded RES over the last few years thanks to the improvements made in terms of: capacity from advances in wind turbine technology, utilization from best wind resources location and flexibility from smart grid technology and output forecasting (IEA, 2010a). Less variable and faster wind available offshore has turned it into the field with a higher potential. On-shore wind energy has expanded remarkably both in Germany and Spain so that by 2010 they were ranked among the first four countries worldwide in terms of installed capacity, only behind China and the USA (WWEA, 2011). Due to difficulties in environmental procedures and to the deeper waters in the Spanish coast, off-shore wind turbines commissioning has been delayed in both countries compared to others like Denmark or the United Kingdom. On top of normally better wind conditions in Spain, 2.000 full load hours per year compared with 1.600 in Germany, the year 2010 was even a better wind year in Spain. As a result, wind energy amounted to 15% of the total generation, but still reaching peaks of 50%. Eventually, the Spanish network operator was forced to disconnect some of the units due to low electrical demand and high wind generation simultaneously. 2.1.3. Solar energy Solar radiation is by far the most abundant resource available on Earth. According to the conversion process, a distinction must be made between direct utilization because of the photovoltaic (PV) effect in a semiconductor and indirect by concentrating sunlight into a small area to heat a fluid which drives an engine called concentrated solar power (CSP). Both Germany, as the first positioned, and Spain, as the second one, are world leaders in the solar PV sector. Despite their worse solar conditions, with 900 peak sun hours annually compared to the 1.800 h in Spain, the powerful German industry has achieved not only a spectacular development of solar technologies but also an outstanding deployment all over the country. Regarding CSP, Spain leads the sector together with the USA. However, the contribution of solar energy sources in terms of share is still minor nowadays (1.9% in Germany and 2.3% in Spain) Spanish PV deployment by the year 2007 had been slower than expected. In order to reverse that situation, the compensation framework was remarkably improved, which together with cuts in investment costs and short project durations, caused a PV boom during the year 2008, multiplying by 10 in only 1 year the capacity so far. As a register on the in advance-allocation of the compensation came into force by 2009, PV deployment was again under control. Major solar potential for CSP is located in the North of America and Africa and in the Middle East, as well as in the Center and South of Spain, the latter with higher water availability for cooling and more proximity to consumers (IEA, 2010b). According to the subdivision above, the study is limited to six renewable technologies, called RES-E-6, namely solid biomass, biogas, wind on-shore, wind off-shore, solar PV and solar CSP. Further information about each technology is available in the comprehensive publications provided by a number of associations
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in the RES-E field. The figure in the appendix sets out a top-down approach by technology and geographical scope (world, European and national). 2.2. TMD model The deployment of RES-E must count on public funding, which promotes private investment in facilities, using the technologies considered above. This funding guarantees a long-term and costoriented tariff, which being higher than the market price, means an additional cost to the electricity system. In order to calculate this surcharge, a simple model, called TMD, is developed. It stands for the first letters of each considered variable, i.e. tariffs (T), market price (M) and deployment (D). The RES-E promotion strategy is based on the gradual tendency of tariffs and market price to parity as the RES-E deployment increases. An outline of their evolution from 1 year i 1 to the next i is shown in Fig. 3. The algorithm to obtain the function surcharge is determined below, formulating a set of assumptions. Considering that the generation of the plants will remain constant over the years, the deployment achieved during the year i for a given technology k, is Di,k–Di-1,k, called di,k. Moreover, assuming a discrete expansion, i.e. all the units commissioned come into force at the beginning of each year, their surcharge would start applying that year, and is obtained by multiplying the deployment and the difference between the tariff and the market price ( di,k UðT i,k M i Þ if T i,k 4 M i Si,k ¼ ð2:1Þ 0 if T i,k r M i If the tariff is lower than the market price, those plants will offer their electricity in the market, without any surcharge. The function surcharge can be obtained by considering all the installations come into force from the year 0 to the year i, as the following sum: Si,k ¼
i X
dj,k UðT j,k M i Þ
ð2:2Þ
j¼0
Those summands, where Tj,k oMi, mean that the facilities commissioned during the year j, offering their electricity in the market during the year i without any surcharge. Supposing that tariffs and market prices will change at fixed rates over the period, both expressions as geometric series are T i,k ¼ T 0,k Uð1t k Þi
ð2:3Þ
555
Mi ¼ M0 Uð1 þ mÞi
ð2:4Þ
where t and m are the common ratios for each progression. T0,k and M0,k are the tariffs and market prices by the year 0, which are used as iteration starting points. Finally, all the plants commissioned before that year are grouped together as D0,k, whose average tariff T0,k is expected to be higher than T0,k, according to the convergence path. The function surcharge can be expressed as the sum of a series from 0 to i 1: 8 Year 0 : d0,k UðT 0,k M i Þ ¼ d0,k UðT 0,k M 0 Uð1 þ mÞi Þ > > > > < Year 1 : d UðT M Þ ¼ d UðT Uð1t ÞM Uð1 þ mÞi Þ 0 i 1,k 1,k 1,k 0,k k Si,k ¼ > ^ > > > : Year i : d UðT M Þ ¼ d UðT Uð1t Þi M Uð1 þ mÞi Þ i1,k
i
i1,k
i,k
0,k
k
0
ð2:5Þ Assuming the same deployment dk every year over the considered period, the sum is Si,k ¼ dk U½T 0,k Uf1þ ð1t k Þ1 þ þ ð1t k Þi gði þ 1ÞUM0 Uð1 þ mÞi Þ ð2:6Þ Using the formula of the geometrical series sum ) " ( # 1ð1t k Þi þ 1 ði þ 1ÞUM 0 Uð1 þ mÞi Þ Si,k ¼ dk T 0,k U tk
ð2:7Þ
The overall surcharge for each year i over the time period can be obtained by adding the six technology surcharges 6 X
Si ¼
Si,k
ð2:8Þ
k¼1
In terms of additional cost per unit of energy generated, the RES-E-6 surcharge si can be finally calculated dividing the overall surcharge by the total generation si ¼
Si Gi
ðin ch=k WhÞ
ð2:9Þ
On the other hand, the RES-E-6 share amounts wi ¼
Di Gi
ðin %Þ
ð2:10Þ
Even though some assumptions have been taken, the function above is still too complex. Hence, the surcharge for the year i and the technology k can be calculated by replacing each variable in every single summand of the series (2.4). This model can be easily simulated to obtain the RES-E-6 surcharge si over the period 2010–2020, using a simple spreadsheet and the inputs shown in Table 3. Projections on both tariffs and market prices for the path 2010–2020 will be considered in real terms, referring to 2010 constant prices. As mentioned, state members were required by the EU in 2010 to submit national renewable energy action plans over the period of 2010–2020, which led them to achieve the RES-E share targets Table 3 Model inputs overview.
Fig. 3. TMD simple model outline. Source: own illustration.
Input
Symbol
Type
Total generation Deployment Average tariffs before 2010 Tariffs 2010 Electricity market price 2010 Tariffs annual ratio Elec. market price annual ratio
Gi di,k T0,k T0,k M0 tk m
Array Array Array Array Value Array Value
Year Technology, year Technology Technology Constant Technology Constant
10 6,10 6 6 1 6 1
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556
out of their total generation Gi. The deployments in Germany and Spain of the six assessed technologies di,k have been taken directly from the NREAPs for each single year up to 2020. German and Spanish 5-yearly plans were summarized in Table 2. The initial tariff for each technology T0,k is determined on average, dividing the total surcharge paid that year by the generation. In Germany, these tariffs can be accessed from the EEG-Umlage report (cost allocation according to the Renewable Energy Act) (Deutscher Bundestag, 2010), while in Spain, the special framework compensation report provides this information monthly (CNE, 2011). Tariff levels in 2010 for both countries by technology are summarized in Table 4. Since the tariffs are supposed to decrease over time, the average tariffs before 2010 T0,k should be higher than those of the year 2010 T0,k. However, except for solar PV, whose payments have been declining meaningfully over the last 2 years, tariffs paid to the plants commissioned before and during the year 2010, did not differ considerably. A PV solar tariff of 30 ch/K Wh is assumed in both countries, while the values shown in Table 4 apply for the other technologies. As a result of the growing crude oil and fossil fuels prices, the initial market price M0 has posted a steady growth over the year 2010. The Spanish market price M0 that year was on average 3.8 ch/K Wh (OMIE, 2011), while the Austrian–German market price amounted in 2010 to 4.45 ch/K Wh (EEX, 2011). In this study, the initial price M0 is rounded off to 4 ch/kW h in both countries. After identifying the variables in the model, and obtaining their actual values in 2010, the evolution from this year on must be forecasted. The deployment path is considered as given, according to the NREAPs, whereas tariffs and market prices are
approached, assuming fixed developments over the period 2010–2020. Obviously, the model is therefore simplified, and constraints are subsequently brought to the assessment, whose impact is considered in the sensitivity analysis below. In order to estimate these fixed rates, the factors driving the variables are identified. First, learning effect on technology will enable tariffs to be reduced. Second, the estimated price growth of fossil fuels will make the electricity market price increase. Finally, policies support RES-E deployment, covering the difference between tariffs and market price. These are the most relevant factors for each variable, but not the only ones. For example, market price is also driven by the RES-E share itself, or tariffs depend on climate conditions too. Variables and their driving factors are outlined in Fig. 4. However, changes on the drivers would modify the path of the variables. According to the merit-order principle, market electricity prices are fixed by the plants using fossil fuels (natural gas and coal). Although these fuels have decoupled their price from oil in recent years, market price predictions in both countries are still based on Brent index forecasts. This index is estimated to start experiencing a steadily growth from 2010 onwards, after some years of strong fluctuation. Two different scenarios are considered by both countries. First, with the Brent oil price at 100 $ and the market price at 9 ch per kW h in 2020, and second with 200 $ and 15 ch (BEE, 2009; MITyC, 2010) (both in 2010 constant prices). In the present study, the conservative scenario is chosen, assuming that 9 ch per kW h by 2020 will be met at fixed growth ratios. This annual ratio can be easily obtained through the following formula: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi M2020 10 m¼ 1 ð2:11Þ M2010
Table 4 Average tariffs by technology and country before 2010 T0,k.
where m amounts to 8.45%, considering as 4 ch and 9 ch per kW h as the starting and ending market prices, respectively. As the RES-E scheme in Germany and Spain is based on a costoriented compensation, tariffs must be progressively updated by costs’ evolution. This development follows market effects and technology improvements, and is modeled using experience curves and corresponding learning rates (Neij et al., 2003). Governments have not always succeeded in this task, so that tariffs have been occasionally registered both above and below their costs, and then, changes in compensation levels had to be applied. The IEA (International Energy Agency) has been developing, at the request of the G8 (Group of Eight), a series of global lowcarbon energy technology roadmaps, where both technical and economic issues are considered. The concept of LCOE (life cost of energy) is introduced as the total generation costs (h/MW h), including investment, operation, maintenance, or fuel costs, and an estimated throughput (full load hours). These roadmaps provide cost projections over the following decades by technology, and support the choice of tariff annual ratios tk.
ch/kW h
Germany
Spain
Biomass solid Biogas Solar FV Solar CSP Wind on-shore Wind off-shore
15.0 7.1 46.8 – 8.8 15.0
11.3 8.3 45.6 30.6 7.8 13.8
2.3. Biomass
Fig. 4. Factors. Source: own illustration.
Unlike wind and solar, biofuels comprise a wide range of sources and technology solutions, so that it is difficult to assess their LCOE future development as a whole. On the other hand, biomass plants require a feedstock supply, whose cost plays a major role in the cost allocation. Biogas tariffs differ in Germany from the expensive agricultural digestion to the cheaper waste-based options sewage and landfill gas (AEBIOM, 2009). Once the latter have been exploited to a large extent, forthcoming deployment will be identified for agricultural biogas. Thus, expected learning effect impact on tariff
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projections can be offset by the changing source share. Cost reductions in the primary forest fuel, the widest deployed biomass source, are expected to slow down, once a production experience has been achieved. Investment costs are lowered, but operative and maintenance, as well as fuel costs, balance the reduction [Junginger, 2007]. However, in the present study the tariffs paid to both solid biomass and biogas installations are supposed to remain constant over the period 2010–2020. This assumption is explained in further detail below.
Technologies - Biomass - Solar - Wind
Variables
TMD Model
Results
- Tariff - Market price - Deployment
Assumptions Predictions of the considered variables
2.4. Wind On-shore wind turbines investment costs are expected to decrease as a result of technology development, deployment and economies of scale (EWEA, 2009). These costs amounted in 2008 to 1.23 million euro per MW (considering a 2 MW wind turbine commissioned in Europe). According to the ETP Blue Map, LCOE cuts from levels in 2009 of 17% in 2030 and 23% in 2050 are expected (IEA, 2010a). For the present analysis, a baseline annual reduction of 1% is provided, which means an accumulated value of 9.6% over the period of 2010–2020. Regarding off-shore wind turbines, the higher LCOE (ranging from 7.5 to 9 ch/kW h) is balanced by 50% better wind conditions. Because of their lower deployment so far, a higher potential is forecasted with cost reductions of 27% by 2030 and 38% by 2050, according to ETP Blue Map (IEA, 2010a). For the present analysis, a baseline annual reduction of 1.5% is provided, which means an accumulated value of 16.1% over the period 2010–2020. 2.5. Solar Solar PV investment costs, ranging in 2009 from 2.9 to 4.3 million euro per MW installed depending on the sector (commercial or residential), are expected to decrease by 2020 to 1.3–1.9 million euro (IEA, 2010c). In terms of LCOE, this means an evolution from 21–49 ch/kW h by 2008, depending on the sector and solar radiation in the location chosen, to 9–22 ch/kW h by 2020. For the present analysis, a baseline annual reduction of 6.5% over their period 2010–2020 is provided, with an accumulated value of 50.0%. Regarding CSP, investment costs by 2009 ranged from 3 to 6 million euro per MW, and a LCOE from 14 to 21 ch/kW h, depending on solar radiation, storage capacity and installation size. A LCOE reduction by 2020 is forecasted to 7–10 ch/kW h (IEA, 2010b). For the present analysis, a baseline annual reduction of 6.5% over the period 2010–2020 is provided, with an accumulated value of 50.0%. Table 5 summarizes in percentage the tariff yearly regression over the period 2010–2020 by technology (tk), as well as the tariff projections in 2020 in both countries. Both countries differ in the way the surcharge is taken. While in Spain it is assumed via tariff deficit (the difference between Table 5 Annual reduction in tariffs per technology (tk). Regression (%)
Biomass solid Biogas Solar PV Solar CSP Wind on-shore Wind off-shore
557
0 0 6.5 6.5 1.0 1.5
Tariff projections in 2020 (ch/kW h) Germany
Spain
15.0 7.1 15.3 Does not apply 7.9 12.9
11.3 8.3 15.3 15.6 7.0 11.9
Fig. 5. Methodology. Source: own illustration.
actual generation costs and its income), in Germany, it is directly shifted to the consumers as a RES-E support. This surcharge is not equally paid, as high energy-consuming industries only pay for a small part of it, while the rest of the consumers pay for the biggest part. According to the EEG Umlage, a surcharge of 0.5 cth/kW h was set in 2010 for high energy consumers and 2.0 for the rest (Deutscher Bundestag, 2010). The methodology in the present study is based on the German concept. To conclude the point and reinforce the approach in the paper, the methodology is outlined in Fig. 5.
3. Results Based on the model and the assumptions defined previously, the reference scenario (REF) can be easily built. The RES-E-6 surcharge and share evolution over the period of 2010–2020 in Germany and Spain are shown in Graph 1. As a result of its further expansion, Spain recorded in 2010 a higher RES-E-6 surcharge than Germany. However, since German RES-E-6 share will increase faster from that year on, their surcharge will post a stronger growth too. Anyway, the growths in both countries will get gradually slower until reaching a maximum at a certain point, after which surcharges will start to decline. That point will be registered in Spain by 2013 and in Germany by 2016. Their maximum surcharges will be 1.87 ch/ kW h and 2.05 ch/kW h, respectively. Therefore, the aim of converging with market prices will be fulfilled during the following decade. The total surcharge over the period of 2010–2020 will amount in Spain to 62.979 million Euros to produce 910.507 GW h renewably (1.66 ch/kW h on average), and 118.421 million Euros to produce 1.505.750 GW h in Germany (1.85 ch/kW h on average), which means a difference of 0.19 ch/kW h between both countries. However, after leveling by 2013, that distance will start growing until leveling off by 2017 at nearly 0.5 ch/kW h. Because of the long-term compensation framework, a remarkable part of the entire expenditure over the following decade is already committed to the RES-E plants commissioned before 2011. Such a commitment, called fixed surcharge, only depends on the electricity market price, shares in REF 65.1% in Spain and 62.6% in Germany out of their whole expenditures. That means in terms of surcharge 1.08 ch/kW h in Spain and 1.16 ch/kW h in Germany. When considering only solar PV installations, the shares are 44.1% and 35.8%, respectively. Governments can only act on the remaining part of the compensation, called variable surcharge, by introducing quotas or changing the tariffs for plants commissioned after 2010. Different compensations for each renewable technology make surcharge and share breakdown differ. As shown in Graph 2, solar
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Graph 1. RES-E-6 share and surcharge evolutions in the reference scenario (REF).
Graph 2. RES-E-6 share and surcharge breakdown by technologies.
energy is by far the most supported technology, amounting to 62.8% in Germany and 79.7% in Spain out of the whole expenditure. However, their contribution only totals to 19.0% and 22.0%, respectively. On the contrary, wind turbines on-shore contribute the most, ranging from 44% in Germany to 68% in Spain, but they collect less than 15% from their government compensations. Solid biomass surcharges are about the average, although their share in Germany is much stronger. Finally, biogas is on the upswing in Germany, with 12.3% contribution to the electricity mix and only 1.6% to the total surcharge. Biogas figures in Spain are far more modest, with 1.8% share and a surcharge below average too. On the other hand, expected parity to the market will only be achieved during the period of 2010–2020 by two technologies, wind on-shore and biogas, though at different moments on each country. This will be in Germany by 2019 and 2017, respectively, and in Spain by 2018 and 2019. At that moment, the
compensation shall level market price so that plant operators will prefer to offer their electricity on the market instead of asking for fixed tariffs, so that further deployment will not be acted by RES-E policies but by the electricity market itself. A minimum surcharge could be easily achieved orienting the RES-E support to those cheaper technologies. For example, if both countries could cover their RES-E targets promoting only on-shore wind turbines, the surcharge would be reduced to 1.13 ch/kW h in Spain and 1.28 ch/kW h in Germany. However, apart from the insufficient availability of right emplacements to keep deploying the already expanded technology, higher shares of wind energy have been proved to be unfeasible, unless storage and interconnection capacity were strongly increased. Once the results of the reference scenario have been stated, the surcharge function is approached, in order to put the results into perspective. Although this function was too complex to be
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559
Fig. 6. Surcharge function approach. Source: own illustration.
if T i,k 4 M i ) Si,k ¼ 0 ) lim sðiÞ ¼ 0
S(i) + D
analyzed directly as an expression, the best-fit function to the actual point cloud (surcharge, year i) in Graph 1 can be approached, considering the following conditions Condition1
Smax
ð3:1Þ
i-1
-
The function surcharge has the horizontal asymptote s ¼0 when i tends to þN. Once each renewable tariff reaches the market price, they cannot continue decreasing and become negative Condition 2 If m4 t ) s00 ðiÞ o 0
ð3:2Þ
Substituting the particular case (m¼ 0, t ¼0) in the formula (2.4), the surcharge expression turns into the linear function below, whose first derivative is positive and second one equals zero. If the market price ratio is higher than the tariff one, then the negative part of the sum series becomes higher than the positive, so that the function moves under the linear Si,k ¼ D0 UðT 0 M0 Þ þ dUðT 0 M 0 ÞUði1Þ ) s0 ðiÞ 4 0, s00 ðiÞ ¼ 0
ð3:3Þ
Since renewable costs are supposed to develop better than market price, the tariff slope must be lower than the market price one, so that the function surcharge is concave. Adding both conditions, a surcharge function pattern is obtained in Fig. 6. Three different stages can be distinguished over time: growth until peaking at Smax (s0 40 and s00 o0) (1), rapid decline until inflecting at Si.p (2) and slower decline tending to zero (s0 o0 and s00 40) (3). Among the particular distributions, the cubic polynomial is the most accurate trendline to the surcharge distribution. Both functions below produce outstanding R-squared values2 of 0.993 for the given range, even though neither of the conditions is ensured beyond that range Germany : sðiÞ ¼ 0:000i3 0:016i2 þ 0:216i þ 1:245
ð3:4Þ
Spain : sðiÞ ¼ 0:002i3 0:048i2 þ0:286i þ1:359
ð3:5Þ
3
The truncated Gaussian distribution best represents the surcharge expression conditions. The parameter a is the curve maximum height (Smax), b is the position of this extreme (imax), and c drives the width of the curve, which represents how fast the convergence to market price occurs. Unlike the Gaussian the truncated Erlang distribution4 has no symmetrical axis, and also fits the surcharge behavior. 2 R-squared is a number from 0 to 1, which reveals how closely the estimated values for the trendline correspond to the actual data. A trendline is most reliable when its R-squared value is at or near 1. 2 2 3 Gaussian distribution: sðiÞ ¼ aUeððibÞ =2c Þ . 4 Erlang distribution: sðiÞ ¼ lUelUi UððlUiÞk1 =GðkÞÞ.
i = Year
Fig. 7. Surcharge function trendlines. Source: own illustration.
Depending on the parameters assigned to the variables, the surcharge function moves from its initial line. The tariffs (T) and the market price (M) scale up or down the line according to the expression T–M. In case of a growth, the function spreads, and both the maximum surcharge and its corresponding year increase. Otherwise, the function contracts and both values decrease, as shown in Fig. 7. On the other hand, the deployment (D) only drives the surcharge, remaining the function span. According to the methodology, the RES-E deployment path is considered as given, while market price and tariffs are assumed to develop at fixed rates, and driven respectively by the fossil fuel prices and the learning effect on RES-E technology. Since these drivers could change, constraints are brought to the assessment. Arguing the variations on fixed rates, a sensitivity analysis is carried out below to determine the impact on surcharge. Firstly, a conservative electricity market price of 9 ch/kW h in 2020 is assumed, based on the projections for fossil fuel prices. However, governments could decide on charging electricity price with further environmental costs. Or, on the contrary, an increasing RES-E share would support more competence among the power plants using fossil sources, and would subsequently reduce the market price, according to the merit order principle. In this study, two alternative scenarios from the baseline are considered, providing that market price remains constant at 4.5 ch, and another one reaching 12 ch/kW h by 2020. The average surcharge would increase about 30% in the first case and decrease 12% in the second one. On the other hand, provided that RES-E LCOEs could not continue to be cut down accordingly, because of no more learning effects on RES-E industry, compensations would perform in the same way. On the contrary, thanks to an increasing worldwide market penetration of RES-E, technologies could become cheaper and compensation regression could develop faster. In the study, a new scenario from the baseline considers that any learning effect
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Graph 3. Sensitivity analysis of the factors: market price and tariffs.
would not apply. The average surcharge would increase about 13%, in case of tariffs remaining constant. The surcharge paths in Spain for the three scenarios above are shown in Graph 3. The assumption of fixed rates for the considered variables refers to a long-term trendline, not necessarily fulfilled by every single year from 2010 onwards. Thus, despite the projections by 2020, electricity market price could in between vary its development or even eventually fall. On the other hand, LCOE reductions by learning effect on technologies are developed in phases, and regression of tariffs could slow down or even stop. Finally, depending on the financial and economical situation, RES-E deployment could develop differently. However, such situations are out of scope in this study. In addition, taking into account the feedback on the variables, projections will be updated with the actual data, once information will be available. First conclusions could already be drawn, like the rapid growth of PV in Germany, or the stagnation of RES-E deployment in Spain in 2010. Even though the tariff for every single technology falls, total surcharge could rise depending on the average tariff of the deployed technologies. The same way, the tariff for a certain technology could increase, although the tariff for every single source within this technology would decrease. For example, after some years deploying cheap sources of biogas in Germany, further deployment it is now time to deploy other more expensive options. After reaching a steady mix, tariffs are expected to decrease accordingly. In order to solve this model constraint, tariffs and deployment for these technologies should be broken down further, so that the tariff decreasing principle remains.
4. Conclusions The assessment of the renewable energy policies for electricity generation must consider as performance indicator, not only the RES-E share achieved, but also the additional cost required, as governments and consumers must complement the market price until covering the generation costs of the renewable units. From their national plans of 2010–2020 and a simple model, the surcharge annual evolution borne to promote clean energies in Germany and Spain can be easily calculated. Its trendline function
is approached by an Erlang alike distribution, whose horizontal asymptote at surcharge¼0 ensures the convergence path. Since the Spanish mix of renewable energies is cheaper and the tariffs are lower, the German surcharge is higher. Although the RES-E shares in 2020 will be similar in both countries, about 38%, the greater weight of large hydropower stations in Spain, which belong to the ordinary framework, must be balanced in Germany by a higher deployment of other more expensive technologies. Therefore, the share reached by the six technologies analyzed in this article is 5% higher in Germany. On the other hand, a greater yield of renewable sources allowed the tariffs paid in Spain until 2010 to be lower than in Germany for each technology, except for the biogas plants. The evolution of these tariffs is driven by technological factors regardless of the country, so that they are expected to develop the same way in the future. In spite of the German RES-E share differential next decade being 5% higher, the 3% average annual growth of total electricity generation in Spain balances the deployment effort on deploying new renewable plants over the next decade. Hence, 45% of the renewable generation expected in Spain in 2020 had been already registered by 2010, slightly lower in Germany with 43%. The uncertain future evolution of technological factors and fuel price on tariffs and market prices, respectively, will therefore drive the RES-E surcharge the same way in both countries. Since both countries had started promoting RES-E units before the year 2010, and their Acts provided a long-term compensation framework, an important part of the whole expenditure over the next 10 years is already committed to the plants commissioned before that year. This fixed surcharge, which only depends on market prices, will be borne during the whole period. While the surcharge caused by the plants coming into force after 2010 also depends on technological development, and will have a gradual effect over this period. Hence, the former will receive approximately 65% out of the total expenditures. In an attempt to cut down on these expenditures, the Spanish Government has recently decided on limiting the maximum number of hours subsidized in solar PV systems, compensating for the differences in 2014. Based on the costs of each renewable source, governments could move from their initial plans to a surcharge-oriented strategy, in order to optimize the yield of their promotion schemes. Moreover, two technologies (on-shore wind turbines and biogas plants) shall reach parity with market prices during
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Fig. 8. RES-E related organisms. Source: own illustration.
the period of 2010–2020, offering their electricity in the spot market out of the renewable energies Acts scope. Since the generation from wind, solar and hydropower power plants strongly depends on climate conditions, high fluctuations are expected, whose negative effects on the grid will increase as the RES-E weight grows. From a dynamic simulation model, conclusions and future constraints can be drawn for a better integration of renewable sources (IWES, 2009). The present study is based on making some calculations on the expenditures caused by the RES-E deployment, considering the share itself as the only yield. However, there are other positive impacts which, although not being assessed here, must be taken into account as an additional return from RES-E promotion to evaluate its net overall effect. According to the merit-order effect, the reduction of electricity demand from fossil sources will lead to lower prices. This contraction could even balance the support payments (Sensfuss et al., 2008). On the other hand, apart from this saving, which is partially shifted from generation companies to consumers, there are other effects on the economy, such as the induced investment in power generation technologies or the entailed employment promotion (Lehr et al., 2007). The contribution of RES-E promotion is not only limited to an economic field but also has environmental outcomes, based on the greenhouse gas emissions avoided through the use of renewable energies. It turns out to be a paradox that Germany, probably the most concerned country worldwide in developing renewable sources over the last decades, has one of the lowest RES-E shares in Europe. The higher electricity consumption and the lower renewable source potential are considered to be the main reasons (Hennicke and Fischedick, 2007). Although both countries are seen to be leaders in RES-E sector, so far Germany has focused on technology development, while Spain has done it on deployment.
Appendix A RES-E related organisms by technologies and geographical scope (Fig. 8). References AEBIOM, 2009. European Biomass Association. A Biogas Road Map for Europe. AGEB, 2011. AG Energiebilanzen e.V. Bruttostromerzeugung in Deutschland von ¨ 1990 bis 2010 nach Energietragern. BEE, 2009. German Renewable Energy Federation. National Renewable Energy Industry Roadmap 2009. BMU, 2010. Federal Ministry for the Environment. National Renewable Energy Action Plan 2011–2020. BMU, 2011. Federal Ministry for the Environment. Development of Renewable Energy Sources in Germany 2010. ˜ a. Informacio´n estadı´stica sobre las CNE, 2011. Comisio´n Nacional de Energı´a Espan ventas de Energı´a del Re´gimen Especial (updated December 2010). Deutscher Bundestag, 2010. EEG–Umlage 2010 nach Erneubare Energien Gesetz. EEA, 2009. European Environment Agency. Annual European Community Greenhouse Gas Inventory 1990–2007 and Inventory Report 2009. EEA, 2011. European Environment Agency. Annual European Community Greenhouse Gas Inventory 1990–2009 and Inventory Report 2011. EEX, 2011. European Energy Exchange. Phelix Index 2010 (Yearly Average of Daily Trading). EurObserver E.R., 2010a. Solid Biomass Barometer, November 2010. EurObserver E.R., 2010b. Biogas Barometer, November 2010. EC, 2009. European Commission. Decision 2009/548/EC Establishing a Template for National Energy Action Plans Under Directive 2009/28/EC. EWEA, 2009. European Wind Energy Association. The Economics of Wind Energy. FBeV, 2010. Fachverband Biogas e.V. Biogas Branchenzahlen 2010. Hennicke, P., Fischedick, M., 2007. Erneubare Energien. IDAE, 2011. Instituto para la diversificacio´n y ahorro de la energı´a. Plan de energı´as renovables 2011–20. Estado Julio de 2011. IEA, 2010a. International Energy Agency. Technology Roadmap Wind Energy. IEA, 2010b. International Energy Agency. Technology Roadmap Concentrating Solar Power. IEA, 2010c. International Energy Agency. Technology Roadmap Solar Photovoltaic Energy.
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IFIC, 2011. International Feed-In Cooperation. /http://www.feed-in-cooperation. orgS. ISI, 2005. Fraunhofer Institute Systems and Innovation Research. Feed-in Systems in Germany and Spain and a Comparison. IWES, 2009. Fraunhofer Institute for Wind Energy and Energy Systems Technology. Dynamische Simulation der Stromversorgung in Deutschland nach dem Ausbauszenario der Erneuerbaren-Energien-Branche. Junginger, 2007. Learning in Biomass Energy Technology Development. Martin Junginger. International Workshop on Technology Learning and Deployment. Lehr, U., Nitsch, J., Kratzat, M., Lutz, C., Edler, D., 2007. Renewable Energy and Employment in Germany. MARM, 2010. Ministerio de Medio Ambiente, y Medio Rural y Marino. El sector del ˜ a. Mesa de Bioga´s. bioga´s agroindustrial en Espan MITyC, 2005. Ministerio de Industria, Turismo y Comercio. Plan de energı´as renovables 2005–2010.
MITyC, 2010. Ministerio de Industria, Turismo y Comercio. Plan de energı´as renovables 2011–2020. Neij, L., Dannemand Andersen, P., Durstewitz, M., Helby, P., Hopper-Kilpper, M., Morthorst, P.E., 2003. Experience Curves: A Tool for Energy Policy Programmes Assessment, Final Report Project ENG1-CT2000-00116, The European Commission within the Fifth Framework: Energy, Environment and Sustainable Development. ˜ a). Precios medios de la casacio´n OMIE, 2011. Operador del mercado Ibe´rico (Espan ˜ a en 2010. del Mercado Diario en Espan ˜ ola. El sistema ele´ctrico espan ˜ ol en 2010. REE, 2011. Red Ele´ctrica Espan Sensfuss, F., Ragwitz, M., Genoese, M., 2008. The Merit-Order Effect: A Detailed Analysis of the Price Effect of Renewable Electricity Generation on Spot Market Prices in Germany. WWEA, 2011. World Wind Energy Association. World Wind Energy Report 2010.
Contents lists available at SciVerse ScienceDirect
Energy Policy journal homepage: www.elsevier.com/locate/enpol
The deployment of electricity generation from renewable energies in Germany and Spain: A comparative analysis based on a simple model ˜ a Ortiz, Jorge Xiberta Bernat Pablo Ferna´ndez Ferna´ndez n, Eunice Villican Department of Energy, University of Oviedo, Oviedo, Spain
H I G H L I G H T S c c c c
Policies must be assessed according to the surcharge caused per unit generated. Surcharge evolution function fitted by an Erlang alike distribution. About two-third of the decade surcharge shall be devoted to units commissioned by 2010. Germany focused on technology development, while Spain on deployment.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 15 December 2011 Accepted 14 February 2013 Available online 16 March 2013
The fulfilment of the aims set by the European Union in the deployment of renewable energy sources for electricity generation (RES-E) has counted and must continue to count on public funding from the member states, which promote private investment in this type of facilities. This funding guarantees a cost-oriented remuneration which, being higher than the market price means an additional cost to the electricity system. With the aim of minimizing the economic impact as the weight of RES-E in the electricity mix increases, the generation costs of renewable units must approach those of the market, which are expected to increase according to the fossil fuel price forecasts. The present study analyzes both the RES-E development and deployment in Spain and Germany, two pioneering countries worldwide and with very similar electricity systems. Based on their national action plans and a simple model, this analysis approaches the RES-E surcharge, comparing and contrasting the results obtained in both countries. & 2013 Elsevier Ltd. All rights reserved.
Keywords: Electricity surcharge National renewable energy action plans (NREAP) Technology roadmaps
1. Introduction On 11 December 1997 most developed countries committed themselves in the city of Kyoto to implementing measures in order to cut by 2012 greenhouse gas emissions by 5% from 1990 levels. The European Union (EU), as a key player in this Protocol, pledged to further reduce emissions up to 8%, setting individual targets for each member state depending on economic and environmental variables. A reduction goal was therefore set in some countries like Germany at 21%, while enabling others like Spain to increase by 15%. The stronger economic growth in Spain from 1990 onwards caused a rise in emissions according to its higher energy demand, so that by 2007 Spain had already increased its emissions by 52.6%. Germany fulfilled that year for the first time the EU goal, with a reduction of 22.4% (EEA, 2009). Gas emissions have been reduced in Spain during the last 3 years n Correspondence to: ETS de Ingenieros de Minas. C/ Independencia, 13. 33004 Oviedo. Asturias. Spain. Tel.: þ 34 630406022; fax: þ 34 985104322. E-mail address: [email protected] (P. Ferna´ndez Ferna´ndez).
0301-4215/$ - see front matter & 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.enpol.2013.02.027
because of the economic crisis, posting in 2009 a growth of 26.8% compared to 1990. Germany maintained its path over these years, cutting its emissions slightly (EEA, 2011). Consumption control and energy efficiency, together with a major use of energy from renewable sources (RES), constitute the main measures taken by the EU to meet this challenge. The 1997 White Paper defined for the first time a European strategy and action plan to promote the market penetration of RES. It aimed at a reduction in the dependency on imports, increasing supply security and supporting national technical development and job creation, setting a global indicative target of doubling its overall use by 2010 from 6% in 1996 to 12%. In the Directive 2001/77/EC, Member States are required to advance in the regulatory framework regarding authorization procedures, reducing barriers to electricity production plants with RES, as well as to set national indicative targets. Finally, the Directive 2009/28/EC aimed for the first time at binding targets for each member state by the year 2020, and required them to adopt national renewable energy action plans (NREAP) in order to attain their goals. Those plans had to be notified to the Commission by 30 June 2010, according
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20%
Directive 2009/28/EC
White Paper 1997
18%
1996 1997
Table 1 Electricity generation share by source in Germany and Spain in 2010. Type
Source
Germany (%)
Spain (%)
Fossil sources
Natural gas Coal
61.0
13.4 42.2
43.5
23.8 8.8
Non-fossil sources
RES-E and hydro Nuclear
39.0
16.5 22.5
56.5
35.0 21.5
12% 8,5%
6%
553
5,8%
2005
2009 2010
2020
Fig. 1. RES European Acts and targets overview. Source: own illustration.
to a harmonized template established by the Commission Decision 2009/548/EC (EC, 2009). A binding RES share target of 20% in overall energy consumption by 2020 was set for the EU as a whole. This objective varies among the member states according to their initial level by 2005, as well as their potential. Germany must therefore increase their share from 5.8% in 2005 to 18% in 2020, whereas Spain’s target equals EU’s at 20%, compared with their initial 8.5% in 2005. Germany registered in 2010 an actual RES share of 10.9% in gross final consumption (BMU, 2011), while Spain of 13.2% (IDAE, 2011). Although these national targets referred to the overall use of RES, the national plans had to set out the breakdown by sector: electricity, heating and cooling and transport. The present study focused on analyzing the first sector, which amounted to 23.2% of the energy gross final consumption in Germany in 2010 and 24.6% in Spain. In Fig. 1, the RES European Acts and their targets are outlined in chronological order. In Germany, the 1991 Electricity Feed Act (Stromeinspeinsungsgesetz) enabled RES producers, mainly small companies, to access the grid owned by the big operators, guaranteeing them a special compensation. The 2000 Renewable Energy Sources Act (Erneubare Energien Gesetz) provided technology and site and resource specific tariffs in an attempt to double RES-E penetration. The recommendations identified by the successive progress reports on the EEG were implemented in the amending laws of 2004 and 2009 (EEG Novellierungen). Finally, in Germany the referred NREAP aimed to a RES share of 19.6% and a RES-E share of 38.6% (BMU, 2010). In Spain, the Electric Power Act 54/1997 liberalized the electricity market and established a special regime for RES-E producers, whose legal and economic framework was further consolidated in the Spanish Royal Decrees 436/2004 and 661/ 2007. Because of a strong growth in energy demand on those years and EU targets regarding RES deployment, the Spanish government approved in 2005 the Renewable Energy Plan, assuming a goal of 12.1% RES-E share by 2010 (MITyC, 2005). Finally, the Spanish NREAP aimed at an overall renewable share of 22.7%, 37.5%, when considering only the electricity sector (MITyC, 2010). In order to develop RES-E policies, both countries have decided on the same strategy, based on three milestones: removal of noneconomic barriers to RES-E development, generation costoriented compensation and its tendency towards electricity market price. That system, so-called feed-in tariff (FiT), guarantees a long-term grid access, ensuring the generated electricity to be taken and paid for at a fixed tariff, higher than the market price (ISI, 2005). All these similarities were behind the foundation of the joint project called Feed-In Cooperation by the governments of Germany and Spain in 2004, which carry out annually international workshops to promote the exchange of experiences regarding feed-in systems (IFIC, 2011).
Table 2 Total and RES-E generation and RES-E share in Germany and Spain. Country
Variable
2005
2010
2015
2020
Germany
RES-E generation (GW h) RES-E share (%) Total generation (GW h)
61.653 10.4 % 604.441
104.972 17.6 % 595.414
157.623 27.0 % 584.134
216.935 38.6 % 562.008
Spain
RES-E generation (GW h) RES-E share (%) Total generation (GW h)
53.773 18.0 % 293.731
84.034 28.1 % 299.345
110.988 32.7 % 339.931
150.030 37.5 % 400.420
On the other hand, both countries use the ‘‘merit order’’ principle in their electricity markets, which ranks energy sources in ascending order of production costs, so that those with the lowest costs are the first ones to meet demand. These are nuclear and large hydro power plants, most of them already amortized and with lower operating costs. RES-E also has the market access guaranteed, thanks to the FiT. Finally, fossil fuel power plants with the highest costs match the uncovered demand. In addition, the electricity mix in Germany and Spain is roughly balanced between fossil fuel plants (coal and natural gas) and non-fossil ones (nuclear and renewable). The generation breakdown by source in 2010 is shown in Table 1 (AGEB, 2011, REE, 2011). As shown in Table 2, according to their NREAPs both countries expected similar shares by 2020, around 38%, although Spanish deployment in 2010 was much higher than the German one, 28.1% and 17.6%, respectively. The distance is even bigger, when comparing the actual data from the year 2010 (16.8% in Germany and 29.2% in Spain) (BMU, 2011, IDAE, 2011), so that the German deployment effort over the next decade will be more demanding in terms of share. However, considering how overall generation is expected to develop over the next decade in both countries, i.e. a growth of 34% in Spain and a fall of 6% in Germany, their deployment efforts will level over the following decade.
2. Methodology The present study aims to assess the resulting support expenditures in Germany and Spain caused by the deployment for the selected RES-E technologies. Thus, a simple model is set up to calculate over the period 2010–2020 the additional cost required (RES-E surcharge) to achieve the expected deployment (RES-E share). These functions are determined by an own developed1 TMD model, given a set of variables and assumptions. This approach is shown in Fig. 2. 2.1. Technologies The scope of this study is limited to those technologies identified as relevant. The EU defined renewable sources as non-fossil sources, 1 TMD model is own developed simple model, which stands for its variables tariff, market (price) and deployment.
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554
Technologies
Variables
TMD Model
Results
Assumptions Fig. 2. Approach. Source: own illustration.
namely wind, solar, aerothermal, geothermal, hydrothermal and ocean energies, hydropower, biomass, landfill gas, sewage treatment plant gas and biogases (European Parliament, 2009). However, not all of these sources are supposed to play the same role in the RES-E deployment over the next decade. Hydropower has moved from being almost the only renewable energy developed some years ago, to nowadays meaning 20% out of the RES-E generation in Germany (5% out of the total) and 33% in Spain (10% out of the total). The vast majority of those plants are large-scale old units under the ordinary framework, which does not mean a surcharge to the system. Although some studies suggest that a large hydropower potential is still untouched, the development of such energy sources turns out to be rather complex because of their administrative procedures and environmental constraints. In order to overcome all these difficulties, both governments have focused on small-scale hydropower plants, integrating them into the special regimen for the production of electricity. Despite this support, those small sized facilities are less profitable. As few plants are planned to be commissioned over the next decade, hydropower yield is only a question of weather conditions. On the other hand, the minor contribution of other emerging technologies, such as geothermal or ocean energy, suggests that over the next decade, research, development and demonstration will still be real issues, instead of becoming relevant in the electricity mix. However, among all the RE sources, three of them, i.e. solar, wind and biomass, have proved to be the most relevant in terms of deployment over the last decade in both countries, and are set to remain so for the next one. How to increase renewable resource utilization has always been a key point in technical improvements: in terms of the conversion process in the case of solar energy (from photovoltaic to concentrating solar power), in terms of selection of the best locations in the case of wind energy (from on-shore to off-shore) or in terms of synergies with other sectors in the case of biomass (paper, landfill, manure, etc.). A brief description of the previous three technologies, as well as their actual state in Germany and Spain, is presented below.
2.1.1. Biomass The conversion of organic matter to energy is the largest single source of renewable energy today, and has the highest technical potential for expansion among renewable energy technologies due to its contribution to the three sectors. According to the conversion process, a distinction must be made between solid biomass (thermal process of combustion, gasification or co-combustion) and biogas (biological process of anaerobic digestion). Germany topped both solid biomass and biogas generation in Europe in 2009, and worldwide was only behind the USA in terms of solid biomass (EurObserver ER, 2010a, 2010b). On the other hand, Spain was the 11th and seventh country, far from its actual potential (MARM, 2010). Unlike the atomization of biomass generation in Germany, a small number of larger scale projects have been commissioned in Spain so far. For example, the number
of biogas plants in Germany by 2010 was estimated in 6.000, whose average size amounted to 380 kW (FBeV, 2010). However, not even 100 biogas plants were installed in Spain by 2010, with an average size of 2 MW (CNE, 2011). Such expansion in Germany has been a result of the spread of digesters run on slurry and manure by German farmers. On the contrary, 75% of the Spanish biogas generation comes from the conversion of organic matter in landfills (EurObserver ER, 2010b). 2.1.2. Wind energy The conversion of mechanical wind energy to electricity by means of a rotor connected to a generator has been the most expanded RES over the last few years thanks to the improvements made in terms of: capacity from advances in wind turbine technology, utilization from best wind resources location and flexibility from smart grid technology and output forecasting (IEA, 2010a). Less variable and faster wind available offshore has turned it into the field with a higher potential. On-shore wind energy has expanded remarkably both in Germany and Spain so that by 2010 they were ranked among the first four countries worldwide in terms of installed capacity, only behind China and the USA (WWEA, 2011). Due to difficulties in environmental procedures and to the deeper waters in the Spanish coast, off-shore wind turbines commissioning has been delayed in both countries compared to others like Denmark or the United Kingdom. On top of normally better wind conditions in Spain, 2.000 full load hours per year compared with 1.600 in Germany, the year 2010 was even a better wind year in Spain. As a result, wind energy amounted to 15% of the total generation, but still reaching peaks of 50%. Eventually, the Spanish network operator was forced to disconnect some of the units due to low electrical demand and high wind generation simultaneously. 2.1.3. Solar energy Solar radiation is by far the most abundant resource available on Earth. According to the conversion process, a distinction must be made between direct utilization because of the photovoltaic (PV) effect in a semiconductor and indirect by concentrating sunlight into a small area to heat a fluid which drives an engine called concentrated solar power (CSP). Both Germany, as the first positioned, and Spain, as the second one, are world leaders in the solar PV sector. Despite their worse solar conditions, with 900 peak sun hours annually compared to the 1.800 h in Spain, the powerful German industry has achieved not only a spectacular development of solar technologies but also an outstanding deployment all over the country. Regarding CSP, Spain leads the sector together with the USA. However, the contribution of solar energy sources in terms of share is still minor nowadays (1.9% in Germany and 2.3% in Spain) Spanish PV deployment by the year 2007 had been slower than expected. In order to reverse that situation, the compensation framework was remarkably improved, which together with cuts in investment costs and short project durations, caused a PV boom during the year 2008, multiplying by 10 in only 1 year the capacity so far. As a register on the in advance-allocation of the compensation came into force by 2009, PV deployment was again under control. Major solar potential for CSP is located in the North of America and Africa and in the Middle East, as well as in the Center and South of Spain, the latter with higher water availability for cooling and more proximity to consumers (IEA, 2010b). According to the subdivision above, the study is limited to six renewable technologies, called RES-E-6, namely solid biomass, biogas, wind on-shore, wind off-shore, solar PV and solar CSP. Further information about each technology is available in the comprehensive publications provided by a number of associations
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in the RES-E field. The figure in the appendix sets out a top-down approach by technology and geographical scope (world, European and national). 2.2. TMD model The deployment of RES-E must count on public funding, which promotes private investment in facilities, using the technologies considered above. This funding guarantees a long-term and costoriented tariff, which being higher than the market price, means an additional cost to the electricity system. In order to calculate this surcharge, a simple model, called TMD, is developed. It stands for the first letters of each considered variable, i.e. tariffs (T), market price (M) and deployment (D). The RES-E promotion strategy is based on the gradual tendency of tariffs and market price to parity as the RES-E deployment increases. An outline of their evolution from 1 year i 1 to the next i is shown in Fig. 3. The algorithm to obtain the function surcharge is determined below, formulating a set of assumptions. Considering that the generation of the plants will remain constant over the years, the deployment achieved during the year i for a given technology k, is Di,k–Di-1,k, called di,k. Moreover, assuming a discrete expansion, i.e. all the units commissioned come into force at the beginning of each year, their surcharge would start applying that year, and is obtained by multiplying the deployment and the difference between the tariff and the market price ( di,k UðT i,k M i Þ if T i,k 4 M i Si,k ¼ ð2:1Þ 0 if T i,k r M i If the tariff is lower than the market price, those plants will offer their electricity in the market, without any surcharge. The function surcharge can be obtained by considering all the installations come into force from the year 0 to the year i, as the following sum: Si,k ¼
i X
dj,k UðT j,k M i Þ
ð2:2Þ
j¼0
Those summands, where Tj,k oMi, mean that the facilities commissioned during the year j, offering their electricity in the market during the year i without any surcharge. Supposing that tariffs and market prices will change at fixed rates over the period, both expressions as geometric series are T i,k ¼ T 0,k Uð1t k Þi
ð2:3Þ
555
Mi ¼ M0 Uð1 þ mÞi
ð2:4Þ
where t and m are the common ratios for each progression. T0,k and M0,k are the tariffs and market prices by the year 0, which are used as iteration starting points. Finally, all the plants commissioned before that year are grouped together as D0,k, whose average tariff T0,k is expected to be higher than T0,k, according to the convergence path. The function surcharge can be expressed as the sum of a series from 0 to i 1: 8 Year 0 : d0,k UðT 0,k M i Þ ¼ d0,k UðT 0,k M 0 Uð1 þ mÞi Þ > > > > < Year 1 : d UðT M Þ ¼ d UðT Uð1t ÞM Uð1 þ mÞi Þ 0 i 1,k 1,k 1,k 0,k k Si,k ¼ > ^ > > > : Year i : d UðT M Þ ¼ d UðT Uð1t Þi M Uð1 þ mÞi Þ i1,k
i
i1,k
i,k
0,k
k
0
ð2:5Þ Assuming the same deployment dk every year over the considered period, the sum is Si,k ¼ dk U½T 0,k Uf1þ ð1t k Þ1 þ þ ð1t k Þi gði þ 1ÞUM0 Uð1 þ mÞi Þ ð2:6Þ Using the formula of the geometrical series sum ) " ( # 1ð1t k Þi þ 1 ði þ 1ÞUM 0 Uð1 þ mÞi Þ Si,k ¼ dk T 0,k U tk
ð2:7Þ
The overall surcharge for each year i over the time period can be obtained by adding the six technology surcharges 6 X
Si ¼
Si,k
ð2:8Þ
k¼1
In terms of additional cost per unit of energy generated, the RES-E-6 surcharge si can be finally calculated dividing the overall surcharge by the total generation si ¼
Si Gi
ðin ch=k WhÞ
ð2:9Þ
On the other hand, the RES-E-6 share amounts wi ¼
Di Gi
ðin %Þ
ð2:10Þ
Even though some assumptions have been taken, the function above is still too complex. Hence, the surcharge for the year i and the technology k can be calculated by replacing each variable in every single summand of the series (2.4). This model can be easily simulated to obtain the RES-E-6 surcharge si over the period 2010–2020, using a simple spreadsheet and the inputs shown in Table 3. Projections on both tariffs and market prices for the path 2010–2020 will be considered in real terms, referring to 2010 constant prices. As mentioned, state members were required by the EU in 2010 to submit national renewable energy action plans over the period of 2010–2020, which led them to achieve the RES-E share targets Table 3 Model inputs overview.
Fig. 3. TMD simple model outline. Source: own illustration.
Input
Symbol
Type
Total generation Deployment Average tariffs before 2010 Tariffs 2010 Electricity market price 2010 Tariffs annual ratio Elec. market price annual ratio
Gi di,k T0,k T0,k M0 tk m
Array Array Array Array Value Array Value
Year Technology, year Technology Technology Constant Technology Constant
10 6,10 6 6 1 6 1
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556
out of their total generation Gi. The deployments in Germany and Spain of the six assessed technologies di,k have been taken directly from the NREAPs for each single year up to 2020. German and Spanish 5-yearly plans were summarized in Table 2. The initial tariff for each technology T0,k is determined on average, dividing the total surcharge paid that year by the generation. In Germany, these tariffs can be accessed from the EEG-Umlage report (cost allocation according to the Renewable Energy Act) (Deutscher Bundestag, 2010), while in Spain, the special framework compensation report provides this information monthly (CNE, 2011). Tariff levels in 2010 for both countries by technology are summarized in Table 4. Since the tariffs are supposed to decrease over time, the average tariffs before 2010 T0,k should be higher than those of the year 2010 T0,k. However, except for solar PV, whose payments have been declining meaningfully over the last 2 years, tariffs paid to the plants commissioned before and during the year 2010, did not differ considerably. A PV solar tariff of 30 ch/K Wh is assumed in both countries, while the values shown in Table 4 apply for the other technologies. As a result of the growing crude oil and fossil fuels prices, the initial market price M0 has posted a steady growth over the year 2010. The Spanish market price M0 that year was on average 3.8 ch/K Wh (OMIE, 2011), while the Austrian–German market price amounted in 2010 to 4.45 ch/K Wh (EEX, 2011). In this study, the initial price M0 is rounded off to 4 ch/kW h in both countries. After identifying the variables in the model, and obtaining their actual values in 2010, the evolution from this year on must be forecasted. The deployment path is considered as given, according to the NREAPs, whereas tariffs and market prices are
approached, assuming fixed developments over the period 2010–2020. Obviously, the model is therefore simplified, and constraints are subsequently brought to the assessment, whose impact is considered in the sensitivity analysis below. In order to estimate these fixed rates, the factors driving the variables are identified. First, learning effect on technology will enable tariffs to be reduced. Second, the estimated price growth of fossil fuels will make the electricity market price increase. Finally, policies support RES-E deployment, covering the difference between tariffs and market price. These are the most relevant factors for each variable, but not the only ones. For example, market price is also driven by the RES-E share itself, or tariffs depend on climate conditions too. Variables and their driving factors are outlined in Fig. 4. However, changes on the drivers would modify the path of the variables. According to the merit-order principle, market electricity prices are fixed by the plants using fossil fuels (natural gas and coal). Although these fuels have decoupled their price from oil in recent years, market price predictions in both countries are still based on Brent index forecasts. This index is estimated to start experiencing a steadily growth from 2010 onwards, after some years of strong fluctuation. Two different scenarios are considered by both countries. First, with the Brent oil price at 100 $ and the market price at 9 ch per kW h in 2020, and second with 200 $ and 15 ch (BEE, 2009; MITyC, 2010) (both in 2010 constant prices). In the present study, the conservative scenario is chosen, assuming that 9 ch per kW h by 2020 will be met at fixed growth ratios. This annual ratio can be easily obtained through the following formula: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi M2020 10 m¼ 1 ð2:11Þ M2010
Table 4 Average tariffs by technology and country before 2010 T0,k.
where m amounts to 8.45%, considering as 4 ch and 9 ch per kW h as the starting and ending market prices, respectively. As the RES-E scheme in Germany and Spain is based on a costoriented compensation, tariffs must be progressively updated by costs’ evolution. This development follows market effects and technology improvements, and is modeled using experience curves and corresponding learning rates (Neij et al., 2003). Governments have not always succeeded in this task, so that tariffs have been occasionally registered both above and below their costs, and then, changes in compensation levels had to be applied. The IEA (International Energy Agency) has been developing, at the request of the G8 (Group of Eight), a series of global lowcarbon energy technology roadmaps, where both technical and economic issues are considered. The concept of LCOE (life cost of energy) is introduced as the total generation costs (h/MW h), including investment, operation, maintenance, or fuel costs, and an estimated throughput (full load hours). These roadmaps provide cost projections over the following decades by technology, and support the choice of tariff annual ratios tk.
ch/kW h
Germany
Spain
Biomass solid Biogas Solar FV Solar CSP Wind on-shore Wind off-shore
15.0 7.1 46.8 – 8.8 15.0
11.3 8.3 45.6 30.6 7.8 13.8
2.3. Biomass
Fig. 4. Factors. Source: own illustration.
Unlike wind and solar, biofuels comprise a wide range of sources and technology solutions, so that it is difficult to assess their LCOE future development as a whole. On the other hand, biomass plants require a feedstock supply, whose cost plays a major role in the cost allocation. Biogas tariffs differ in Germany from the expensive agricultural digestion to the cheaper waste-based options sewage and landfill gas (AEBIOM, 2009). Once the latter have been exploited to a large extent, forthcoming deployment will be identified for agricultural biogas. Thus, expected learning effect impact on tariff
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projections can be offset by the changing source share. Cost reductions in the primary forest fuel, the widest deployed biomass source, are expected to slow down, once a production experience has been achieved. Investment costs are lowered, but operative and maintenance, as well as fuel costs, balance the reduction [Junginger, 2007]. However, in the present study the tariffs paid to both solid biomass and biogas installations are supposed to remain constant over the period 2010–2020. This assumption is explained in further detail below.
Technologies - Biomass - Solar - Wind
Variables
TMD Model
Results
- Tariff - Market price - Deployment
Assumptions Predictions of the considered variables
2.4. Wind On-shore wind turbines investment costs are expected to decrease as a result of technology development, deployment and economies of scale (EWEA, 2009). These costs amounted in 2008 to 1.23 million euro per MW (considering a 2 MW wind turbine commissioned in Europe). According to the ETP Blue Map, LCOE cuts from levels in 2009 of 17% in 2030 and 23% in 2050 are expected (IEA, 2010a). For the present analysis, a baseline annual reduction of 1% is provided, which means an accumulated value of 9.6% over the period of 2010–2020. Regarding off-shore wind turbines, the higher LCOE (ranging from 7.5 to 9 ch/kW h) is balanced by 50% better wind conditions. Because of their lower deployment so far, a higher potential is forecasted with cost reductions of 27% by 2030 and 38% by 2050, according to ETP Blue Map (IEA, 2010a). For the present analysis, a baseline annual reduction of 1.5% is provided, which means an accumulated value of 16.1% over the period 2010–2020. 2.5. Solar Solar PV investment costs, ranging in 2009 from 2.9 to 4.3 million euro per MW installed depending on the sector (commercial or residential), are expected to decrease by 2020 to 1.3–1.9 million euro (IEA, 2010c). In terms of LCOE, this means an evolution from 21–49 ch/kW h by 2008, depending on the sector and solar radiation in the location chosen, to 9–22 ch/kW h by 2020. For the present analysis, a baseline annual reduction of 6.5% over their period 2010–2020 is provided, with an accumulated value of 50.0%. Regarding CSP, investment costs by 2009 ranged from 3 to 6 million euro per MW, and a LCOE from 14 to 21 ch/kW h, depending on solar radiation, storage capacity and installation size. A LCOE reduction by 2020 is forecasted to 7–10 ch/kW h (IEA, 2010b). For the present analysis, a baseline annual reduction of 6.5% over the period 2010–2020 is provided, with an accumulated value of 50.0%. Table 5 summarizes in percentage the tariff yearly regression over the period 2010–2020 by technology (tk), as well as the tariff projections in 2020 in both countries. Both countries differ in the way the surcharge is taken. While in Spain it is assumed via tariff deficit (the difference between Table 5 Annual reduction in tariffs per technology (tk). Regression (%)
Biomass solid Biogas Solar PV Solar CSP Wind on-shore Wind off-shore
557
0 0 6.5 6.5 1.0 1.5
Tariff projections in 2020 (ch/kW h) Germany
Spain
15.0 7.1 15.3 Does not apply 7.9 12.9
11.3 8.3 15.3 15.6 7.0 11.9
Fig. 5. Methodology. Source: own illustration.
actual generation costs and its income), in Germany, it is directly shifted to the consumers as a RES-E support. This surcharge is not equally paid, as high energy-consuming industries only pay for a small part of it, while the rest of the consumers pay for the biggest part. According to the EEG Umlage, a surcharge of 0.5 cth/kW h was set in 2010 for high energy consumers and 2.0 for the rest (Deutscher Bundestag, 2010). The methodology in the present study is based on the German concept. To conclude the point and reinforce the approach in the paper, the methodology is outlined in Fig. 5.
3. Results Based on the model and the assumptions defined previously, the reference scenario (REF) can be easily built. The RES-E-6 surcharge and share evolution over the period of 2010–2020 in Germany and Spain are shown in Graph 1. As a result of its further expansion, Spain recorded in 2010 a higher RES-E-6 surcharge than Germany. However, since German RES-E-6 share will increase faster from that year on, their surcharge will post a stronger growth too. Anyway, the growths in both countries will get gradually slower until reaching a maximum at a certain point, after which surcharges will start to decline. That point will be registered in Spain by 2013 and in Germany by 2016. Their maximum surcharges will be 1.87 ch/ kW h and 2.05 ch/kW h, respectively. Therefore, the aim of converging with market prices will be fulfilled during the following decade. The total surcharge over the period of 2010–2020 will amount in Spain to 62.979 million Euros to produce 910.507 GW h renewably (1.66 ch/kW h on average), and 118.421 million Euros to produce 1.505.750 GW h in Germany (1.85 ch/kW h on average), which means a difference of 0.19 ch/kW h between both countries. However, after leveling by 2013, that distance will start growing until leveling off by 2017 at nearly 0.5 ch/kW h. Because of the long-term compensation framework, a remarkable part of the entire expenditure over the following decade is already committed to the RES-E plants commissioned before 2011. Such a commitment, called fixed surcharge, only depends on the electricity market price, shares in REF 65.1% in Spain and 62.6% in Germany out of their whole expenditures. That means in terms of surcharge 1.08 ch/kW h in Spain and 1.16 ch/kW h in Germany. When considering only solar PV installations, the shares are 44.1% and 35.8%, respectively. Governments can only act on the remaining part of the compensation, called variable surcharge, by introducing quotas or changing the tariffs for plants commissioned after 2010. Different compensations for each renewable technology make surcharge and share breakdown differ. As shown in Graph 2, solar
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Graph 1. RES-E-6 share and surcharge evolutions in the reference scenario (REF).
Graph 2. RES-E-6 share and surcharge breakdown by technologies.
energy is by far the most supported technology, amounting to 62.8% in Germany and 79.7% in Spain out of the whole expenditure. However, their contribution only totals to 19.0% and 22.0%, respectively. On the contrary, wind turbines on-shore contribute the most, ranging from 44% in Germany to 68% in Spain, but they collect less than 15% from their government compensations. Solid biomass surcharges are about the average, although their share in Germany is much stronger. Finally, biogas is on the upswing in Germany, with 12.3% contribution to the electricity mix and only 1.6% to the total surcharge. Biogas figures in Spain are far more modest, with 1.8% share and a surcharge below average too. On the other hand, expected parity to the market will only be achieved during the period of 2010–2020 by two technologies, wind on-shore and biogas, though at different moments on each country. This will be in Germany by 2019 and 2017, respectively, and in Spain by 2018 and 2019. At that moment, the
compensation shall level market price so that plant operators will prefer to offer their electricity on the market instead of asking for fixed tariffs, so that further deployment will not be acted by RES-E policies but by the electricity market itself. A minimum surcharge could be easily achieved orienting the RES-E support to those cheaper technologies. For example, if both countries could cover their RES-E targets promoting only on-shore wind turbines, the surcharge would be reduced to 1.13 ch/kW h in Spain and 1.28 ch/kW h in Germany. However, apart from the insufficient availability of right emplacements to keep deploying the already expanded technology, higher shares of wind energy have been proved to be unfeasible, unless storage and interconnection capacity were strongly increased. Once the results of the reference scenario have been stated, the surcharge function is approached, in order to put the results into perspective. Although this function was too complex to be
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559
Fig. 6. Surcharge function approach. Source: own illustration.
if T i,k 4 M i ) Si,k ¼ 0 ) lim sðiÞ ¼ 0
S(i) + D
analyzed directly as an expression, the best-fit function to the actual point cloud (surcharge, year i) in Graph 1 can be approached, considering the following conditions Condition1
Smax
ð3:1Þ
i-1
-
The function surcharge has the horizontal asymptote s ¼0 when i tends to þN. Once each renewable tariff reaches the market price, they cannot continue decreasing and become negative Condition 2 If m4 t ) s00 ðiÞ o 0
ð3:2Þ
Substituting the particular case (m¼ 0, t ¼0) in the formula (2.4), the surcharge expression turns into the linear function below, whose first derivative is positive and second one equals zero. If the market price ratio is higher than the tariff one, then the negative part of the sum series becomes higher than the positive, so that the function moves under the linear Si,k ¼ D0 UðT 0 M0 Þ þ dUðT 0 M 0 ÞUði1Þ ) s0 ðiÞ 4 0, s00 ðiÞ ¼ 0
ð3:3Þ
Since renewable costs are supposed to develop better than market price, the tariff slope must be lower than the market price one, so that the function surcharge is concave. Adding both conditions, a surcharge function pattern is obtained in Fig. 6. Three different stages can be distinguished over time: growth until peaking at Smax (s0 40 and s00 o0) (1), rapid decline until inflecting at Si.p (2) and slower decline tending to zero (s0 o0 and s00 40) (3). Among the particular distributions, the cubic polynomial is the most accurate trendline to the surcharge distribution. Both functions below produce outstanding R-squared values2 of 0.993 for the given range, even though neither of the conditions is ensured beyond that range Germany : sðiÞ ¼ 0:000i3 0:016i2 þ 0:216i þ 1:245
ð3:4Þ
Spain : sðiÞ ¼ 0:002i3 0:048i2 þ0:286i þ1:359
ð3:5Þ
3
The truncated Gaussian distribution best represents the surcharge expression conditions. The parameter a is the curve maximum height (Smax), b is the position of this extreme (imax), and c drives the width of the curve, which represents how fast the convergence to market price occurs. Unlike the Gaussian the truncated Erlang distribution4 has no symmetrical axis, and also fits the surcharge behavior. 2 R-squared is a number from 0 to 1, which reveals how closely the estimated values for the trendline correspond to the actual data. A trendline is most reliable when its R-squared value is at or near 1. 2 2 3 Gaussian distribution: sðiÞ ¼ aUeððibÞ =2c Þ . 4 Erlang distribution: sðiÞ ¼ lUelUi UððlUiÞk1 =GðkÞÞ.
i = Year
Fig. 7. Surcharge function trendlines. Source: own illustration.
Depending on the parameters assigned to the variables, the surcharge function moves from its initial line. The tariffs (T) and the market price (M) scale up or down the line according to the expression T–M. In case of a growth, the function spreads, and both the maximum surcharge and its corresponding year increase. Otherwise, the function contracts and both values decrease, as shown in Fig. 7. On the other hand, the deployment (D) only drives the surcharge, remaining the function span. According to the methodology, the RES-E deployment path is considered as given, while market price and tariffs are assumed to develop at fixed rates, and driven respectively by the fossil fuel prices and the learning effect on RES-E technology. Since these drivers could change, constraints are brought to the assessment. Arguing the variations on fixed rates, a sensitivity analysis is carried out below to determine the impact on surcharge. Firstly, a conservative electricity market price of 9 ch/kW h in 2020 is assumed, based on the projections for fossil fuel prices. However, governments could decide on charging electricity price with further environmental costs. Or, on the contrary, an increasing RES-E share would support more competence among the power plants using fossil sources, and would subsequently reduce the market price, according to the merit order principle. In this study, two alternative scenarios from the baseline are considered, providing that market price remains constant at 4.5 ch, and another one reaching 12 ch/kW h by 2020. The average surcharge would increase about 30% in the first case and decrease 12% in the second one. On the other hand, provided that RES-E LCOEs could not continue to be cut down accordingly, because of no more learning effects on RES-E industry, compensations would perform in the same way. On the contrary, thanks to an increasing worldwide market penetration of RES-E, technologies could become cheaper and compensation regression could develop faster. In the study, a new scenario from the baseline considers that any learning effect
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Graph 3. Sensitivity analysis of the factors: market price and tariffs.
would not apply. The average surcharge would increase about 13%, in case of tariffs remaining constant. The surcharge paths in Spain for the three scenarios above are shown in Graph 3. The assumption of fixed rates for the considered variables refers to a long-term trendline, not necessarily fulfilled by every single year from 2010 onwards. Thus, despite the projections by 2020, electricity market price could in between vary its development or even eventually fall. On the other hand, LCOE reductions by learning effect on technologies are developed in phases, and regression of tariffs could slow down or even stop. Finally, depending on the financial and economical situation, RES-E deployment could develop differently. However, such situations are out of scope in this study. In addition, taking into account the feedback on the variables, projections will be updated with the actual data, once information will be available. First conclusions could already be drawn, like the rapid growth of PV in Germany, or the stagnation of RES-E deployment in Spain in 2010. Even though the tariff for every single technology falls, total surcharge could rise depending on the average tariff of the deployed technologies. The same way, the tariff for a certain technology could increase, although the tariff for every single source within this technology would decrease. For example, after some years deploying cheap sources of biogas in Germany, further deployment it is now time to deploy other more expensive options. After reaching a steady mix, tariffs are expected to decrease accordingly. In order to solve this model constraint, tariffs and deployment for these technologies should be broken down further, so that the tariff decreasing principle remains.
4. Conclusions The assessment of the renewable energy policies for electricity generation must consider as performance indicator, not only the RES-E share achieved, but also the additional cost required, as governments and consumers must complement the market price until covering the generation costs of the renewable units. From their national plans of 2010–2020 and a simple model, the surcharge annual evolution borne to promote clean energies in Germany and Spain can be easily calculated. Its trendline function
is approached by an Erlang alike distribution, whose horizontal asymptote at surcharge¼0 ensures the convergence path. Since the Spanish mix of renewable energies is cheaper and the tariffs are lower, the German surcharge is higher. Although the RES-E shares in 2020 will be similar in both countries, about 38%, the greater weight of large hydropower stations in Spain, which belong to the ordinary framework, must be balanced in Germany by a higher deployment of other more expensive technologies. Therefore, the share reached by the six technologies analyzed in this article is 5% higher in Germany. On the other hand, a greater yield of renewable sources allowed the tariffs paid in Spain until 2010 to be lower than in Germany for each technology, except for the biogas plants. The evolution of these tariffs is driven by technological factors regardless of the country, so that they are expected to develop the same way in the future. In spite of the German RES-E share differential next decade being 5% higher, the 3% average annual growth of total electricity generation in Spain balances the deployment effort on deploying new renewable plants over the next decade. Hence, 45% of the renewable generation expected in Spain in 2020 had been already registered by 2010, slightly lower in Germany with 43%. The uncertain future evolution of technological factors and fuel price on tariffs and market prices, respectively, will therefore drive the RES-E surcharge the same way in both countries. Since both countries had started promoting RES-E units before the year 2010, and their Acts provided a long-term compensation framework, an important part of the whole expenditure over the next 10 years is already committed to the plants commissioned before that year. This fixed surcharge, which only depends on market prices, will be borne during the whole period. While the surcharge caused by the plants coming into force after 2010 also depends on technological development, and will have a gradual effect over this period. Hence, the former will receive approximately 65% out of the total expenditures. In an attempt to cut down on these expenditures, the Spanish Government has recently decided on limiting the maximum number of hours subsidized in solar PV systems, compensating for the differences in 2014. Based on the costs of each renewable source, governments could move from their initial plans to a surcharge-oriented strategy, in order to optimize the yield of their promotion schemes. Moreover, two technologies (on-shore wind turbines and biogas plants) shall reach parity with market prices during
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Fig. 8. RES-E related organisms. Source: own illustration.
the period of 2010–2020, offering their electricity in the spot market out of the renewable energies Acts scope. Since the generation from wind, solar and hydropower power plants strongly depends on climate conditions, high fluctuations are expected, whose negative effects on the grid will increase as the RES-E weight grows. From a dynamic simulation model, conclusions and future constraints can be drawn for a better integration of renewable sources (IWES, 2009). The present study is based on making some calculations on the expenditures caused by the RES-E deployment, considering the share itself as the only yield. However, there are other positive impacts which, although not being assessed here, must be taken into account as an additional return from RES-E promotion to evaluate its net overall effect. According to the merit-order effect, the reduction of electricity demand from fossil sources will lead to lower prices. This contraction could even balance the support payments (Sensfuss et al., 2008). On the other hand, apart from this saving, which is partially shifted from generation companies to consumers, there are other effects on the economy, such as the induced investment in power generation technologies or the entailed employment promotion (Lehr et al., 2007). The contribution of RES-E promotion is not only limited to an economic field but also has environmental outcomes, based on the greenhouse gas emissions avoided through the use of renewable energies. It turns out to be a paradox that Germany, probably the most concerned country worldwide in developing renewable sources over the last decades, has one of the lowest RES-E shares in Europe. The higher electricity consumption and the lower renewable source potential are considered to be the main reasons (Hennicke and Fischedick, 2007). Although both countries are seen to be leaders in RES-E sector, so far Germany has focused on technology development, while Spain has done it on deployment.
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