Consortium. Imprint. Financial Support

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2 Consortium Imprint Vienna University of Technology Energy Economics Group (EEG) Gusshausstrasse 25-29/373-2 A 1040 Vienna Austria Printed in Austria May 2007 Graphic Design: Moosmoar Financial Support Legal Notice: This project is supported within the Intelligent Energy - Europe Programme of the European Commission (Contract No. EIE/04/049/S ). The sole responsibility for the content of this report lies with the authors. It does not represent the opinion of the Community. The European Commission is not responsible for any use that may be made of the information contained therein. I

3 Coordination Dr. Hans Auer Vienna University of Technology Energy Economics Group (EEG) Gusshausstrasse 25-29/373-2 A 1040 Vienna Austria Tel: Fax: auer@eeg.tuwien.ac.at Website: ii

4 Preface In recent years the share of renewable electricity generation has been increasing significantly, especially in Europe. The major reason of this development on European level has been the implementation of national promotion policies having been triggered by Directive 2001/77/EC. Looking ahead, an ambitious binding target has been defined by the governors of the EU Member States in March 2007 in Brussels: 20% to be met by renewable energies on overall energy consumption in the European Union in Large-scale integration of renewable electricity generation into the European grids, however, has a variety of dimensions. Neither in practise nor in research many open questions have been addressed and/or solved. Moreover, the necessity of a convergence of different policies (e.g. renewable promotion policy, grid regulation policy, unbundling implementation policy) seems not to be obvious at present. In the research community on European level, for example, there exist a variety of projects and analyses tools addressing specific aspects in the field of future deployment of renewable electricity generation and distributed generation (e.g. designing renewable promotion policies and instruments, modelling large-scale intermittent wind generation, analysing technological and economical aspects of distributed generation). The GreenNet-Europe project tries to combine several dimensions of large-scale grid integration of renewable electricity generation technologies mentioned above. Moreover, a new aspect in GreenNet- Europe is to comprehensively address also the grid operator s point-of-view in this context. Since the currently ongoing implementation process of new grid regulation models is accompanied by benchmarking of eligible costs for grid operation and grid infrastructure planning, the grid operator naturally has to be confident that several extra costs caused by large-scale renewable grid integration are eligible in this context. At present, this is not necessarily the case in the EU Member States. Therefore, the willingness of grid operators to absorb large amounts of exogenously saddled renewable electricity generation is limited. GreenNet-Europe, therefore, critically discusses the relevance of transparent unbundling and cost allocation in the context of large-scale grid integration of renewable electricity generation technologies and clearly defines the role of the renewable power plant, the grid infrastructure and overall system operation. Moreover, the discussion and consideration of basic unbundling principles (still violated in almost all EU Member States) and correct cost allocation and cost remuneration (e.g. renewable promotion instruments versus grid tariffs versus balancing/wholesale markets) of different cost components of the total costs of a renewable project are important cornerstones of the modelling approach in GreenNet-Europe. Finally, in GreenNet-Europe policy recommendations are derived on how to reach a maximum of renewable electricity generation in the European grids with minimal costs for society. Dr. Hans Auer (Coordinator) iii

5 Table of Contents 1 Introduction Motivation Outline of the action plan Unbundling and Large-Scale RES-E Grid Integration The role of the grid infrastructure Challenges for RES-E developers Challenges for grid operators Deep versus shallow versus super-shallow RES-E integration System operation and intermittent RES-E generation Grid-Related and System-Related Costs of RES-E Grid Integration Grid infrastructure costs Grid connection costs Grid reinforcement/upgrading costs System operation costs Short-term system balancing costs Long-term system adequacy costs Costs for electricity storage technologies and demand response Resume Determination of Potentials and Costs of RES-E Generation in Europe The Simulation Software Tool GreenNet-Europe Modelling approach of GreenNet-Europe Scenario selection in GreenNet-Europe Screenshots of GreenNet-Europe Results on Least Cost RES-E Integration Scenarios based on GreenNet-Europe RES-E deployment in the reference scenario Grid-related and system-related costs of wind integration in the reference scenario RES-E deployment for different cost allocation policies of grid-related and system-related costs Correct remuneration of grid-related and system related-costs according to basic unbundling principles Policy Recommendations...23 References...24 iv

6 1 Introduction 1.1 Motivation The EC-Directive 2003/54/EC (repealing EC-Directive 96/92/EC) on liberalization of electricity markets requires the electricity supply industry to be competitive, yet realizes that many elements of the electricity supply chain are still natural monopolies (EC (2004)). Consequently, it is considered best for different segments of the electricity system to be separated into clearly defined and separately accounted entities, as there are electricity generation, high-voltage transmission, low-voltage distribution and customer supply. This is called unbundling, which is one of the cornerstones of the liberalized electricity market. Separation of the competitive segments electricity generation and customer supply from the grid infrastructure is seen as a precondition for nondiscriminatory grid access of third parties (e.g. RES-E generators) as well as for transparent grid regulation procedures and grid tariff determination. But legislation and definition of RES-E policy goals on national as well as EU level still face a variety of lacks and inconsistencies (see e.g. Auer (2006)): mixing up boundaries between RES-E generation units, the grid infrastructure (new grid connection lines, reinforcement/upgrading of the existing grid) and overall system operation, neglecting disaggregated cost allocation of RES-E grid integration and, subsequently, mixing up different instruments for remuneration of the different disaggregated cost components of RES-E integration (i.e. RES-E promotion instruments versus wholesale/balancing markets versus grid tariffs). The intermittent nature of RES-E generation technologies like wind, furthermore, expects additional measures for overall system operation. Moreover, the consideration of different time scales is important for managing generation and load on system level in general, and with large amounts of intermittent wind generation in particular. These time scales vary from seconds to minutes to days and longer (see e.g. van Werven et al (2005)): In the short-term (times scales below seconds to several hours) a variety of balancing (ancillary) services are necessary for maintaining stable system operation. The driver for short-term system balancing requirements is the magnitude of random power fluctuations, caused by unpredictable changes in both generation and load. In the long-term, in competitive electricity markets the market itself shall be responsible for providing adequate generation capacities. This is also true for systems with large amounts of intermittent wind generation (example: Danish electricity system). Large-scale RES-E grid integration, furthermore, also has to be analysed from the grid operator s point-of-view. At present, in many EU Member States new grid regulation models are implemented. 1 These new models shall, on the one hand, provide incentives for grid operators for efficient grid operation and grid infrastructure planning and, on the other hand, define transparent procedures for grid tariff determination. Since the grid regulation process is accompanied by benchmarking of eligible costs for grid operation and grid infrastructure planning, the grid operator naturally has to be confident that several extra costs of large-scale RES-E grid integration are eligible in this context. This is, at present, not necessarily the case. Therefore, the willingness of grid operators to absorb large amounts of exogenously saddled RES-E generation technologies is limited. The discussion above indicates that large-scale RES-E grid integration has a variety of dimensions. Neither in practise nor in literature many open questions in this context have been addressed and/or solved. Moreover, the necessity of a convergence of different policies (e.g. RES-E promotion policy and grid regulation policy) seems not to be obvious at present. On the contrary, analytical models as well as literature on critical reviews of unbundling in the context of large-scale RES-E grid integration are still scarce. Up to now the different decision makers, practitioners and research communities did not realise the significant interdependencies between the different segments of the unbundled electricity supply chain. 1 Sophisticated grid regulation models are already implemented in European countries like Norway, UK, The Netherlands, and Austria. In other countries like Germany the implementation of new grid regulation models and grid tariff determination procedures has been delayed for years (sector-specific energy regulator has been installed later). 1

7 In the research community on European level, for example, there exist a variety of analyses tools modelling specific aspects in the field of future RES-E deployment and distributed generation. In the following, selected examples are briefly summarised: RES-E Policy Design: In recent years a variety of projects have been conducted focusing on ex-ante design, optimization of the implementation/harmonization process and also ex-post evaluation of RES-E policy instruments (e.g. FORRES2020, OPTRES, PROGRESS, FUTURES-E). In these projects, furthermore, also a variety of dynamic, socio-economic and macro-economic aspects of future RES-E deployment have been taken into consideration. The most important analyses tool in several of these projects has been the software tool Green-X, having been continuously extended. Wind Integration Models: There also exist sophisticated system-based wind integration analyses tools having been developed in different European projects (e.g. WILMAR, SUPWIND) for modelling large-scale wind integration in different European system configurations (e.g. Nordel-, UCTE-, UK system). These kinds of analyses provide insights into short-term system operation characteristics and long-term grid infrastructure planning requirements caused by large-scale intermittent wind generation, on the one hand, and also address the interactions with the unique properties of the individual electricity markets, on the other hand. Distributed Generation: Last but not least, in the European research community there exists a strong cluster in the field of distributed generation. A variety of currently ongoing (e.g. EU-DEEP, DER-LAB) as well as finished projects (e.g. DISPOWER, SUSTELNET) in the field of distributed generation are bundled in the IRED- Cluster. The major objectives in this cluster are industry-driven technological and economical analyses in the field of large-scale distributed generation (incl. RES-E generation). The major objective of this report and the GreenNet-Europe project in general is to establish a common understanding on least cost RES-E grid integration. A precondition, therefore, is to critically discuss the relevance of unbundling in the context of large-scale RES-E integration and to define the role of the grid infrastructure and overall system operation. The modelling approach of the simulation software tool GreenNet-Europe finally tries to combine several dimensions and interdependences of large-scale RES-E integration mentioned above: (i) RES-E policies, (ii) large amounts of intermittent wind generation and (iii) integration of spatially distributed RES-E potentials into the electricity systems. Moreover, based on comprehensive GreenNet-Europe simulation runs discussing different cost allocation policies of RES-E integration recommendations for decision makers are derived on how to reach a maximum of RES-E deployment in Europe with lowest cost for society. 1.2 Outline of the action plan The action plan is organised as follows: Section 2 addresses the relevance of unbundling for large-scale RES-E grid integration. In this context the grid infrastructure plays a core role. Besides the challenges faced by the RES-E developer and the grid operator, stable system operation with large amounts of intermittent RES-E generation is another major issue of practical relevance. In section 3 analytical methods and empirical data on costs caused by large-scale RES-E integration are presented for grid infrastructure connection and grid reinforcement/upgrading, on the one hand, and extra system operation costs due to intermittent RES-E (mainly wind) generation, on the other hand. Section 4 describes the determination of the potentials and costs of RES-E generation in the EU Member States up to the year 2020 and its integration into the empirical data base of the simulation model Green- Net-Europe. In section 5 the modelling approach, the major features and the empirical data set of the simulation software tool GreenNet-Europe are briefly summarized. The most important simulation results on RES-E deployment, grid infrastructure costs and system operation costs for different cost allocation policies of RES-E integration are presented in section 6. Furthermore, sensitivity analyses are conducted in order to provide insights into the bandwidth of RES-E deployment under different constraints. Finally, section 7 derives policy recommendations and action plans for decision makers and stakeholders on least-cost RES-E integration into the European electricity grids. 2

8 2 Unbundling and Large-Scale RES-E Grid Integration 2.1 The role of the grid infrastructure Challenges for RES-E developers Grid integration often is a significant economic barrier for RES-E generation technologies in dispersed locations. If the new RES-E developer has to pay all the costs of grid connection up-front, then a compromise between the best generation sites and acceptable grid conditions has to be made, as is often the case for wind and smallhydro power. On contrary, grid connection for biomass or biogas in general is no crucial barrier as the particular location of the plant is even more independent from resource conditions. To pay for the connection, the RES-E developer includes the costs into the long-run marginal generation costs. However, if the grid connection costs are covered by the grid operator and the corresponding costs are socialized in the grid tariffs, then the initial burden does not fall on the RES-E developer. Besides new grid connection lines (regardless of the distance and/or voltage level of connection) also grid reinforcement/upgrading measures may be necessary elsewhere in the existing network due to large-scale RES-E integration. But the allocation of the corresponding grid reinforcement/upgrading costs to the RES-E developer is ambiguous. The core problem is that any changes in an intermeshed grid infrastructure will change the load flows in the system. The status quo as well as changes of load flows, however, have a variety of dimensions, as there are e.g. changes in generation and load centres, bottleneck situations, power trading activities, etc. Therefore, the allocation of load flow changes to one single event (e.g. grid reinforcement/upgrading caused by a new RES-E generator) is not necessarily correct Challenges for grid operators Neither in literature nor in practice large-scale RES-E integration has been analysed from the grid operator s point-of-view so far. Moreover, the challenges faced by grid operators in bearing their additional RES-E grid integration costs have to be addressed not least due to the following two currently ongoing developments (being not linked together at present): rapidly increasing shares of RES-E grid integration in the European transmission and distribution grids, and implementation of new grid regulation and grid tariff determination models by national regulators accompanied by benchmarking of eligible costs for grid infrastructure planning and grid operation. Moreover, electricity grids are capital-intensive infrastructures characterized as natural monopolies over a defined geographic and/or voltage region. The grid assets life-times can be up to years and once investments are made they are effectively sunk. Therefore, grid assets are vulnerable to changes in regulatory conditions which could prevent or hinder cost recovery. In particular, RES-E promotion policies (like feed-in tariffs, TGCs, etc.) not directly taking into account effects on grid operations can impose costs on transmission and distribution grids and give rise to the question of cost recovery of grid operators. At present, from the grid operators point-of-view these uncertainties are significant economic disincentives to absorb large-scale RES-E generation technologies into their grids. In order to overcome these existing inadequacies and also to satisfy the grid operators future expectations on investment cost recovery in the context of RES-E grid integration a convergence in the design of both RES-E 2 Moreover, the grid-connection and grid reinforcement/upgrading discussion in the context of large-scale RES-E integration is ambiguous having in mind the natural monopoly character of capital-intensive electricity grids. Natural monopolies being characterised by: (i) subadditivity of the cost function, (ii) sunk costs of investments and (iii) average costs below marginal costs are regulated. A precondition in the grid regulation process is the exact definition of the boundary between the grid infrastructure and electricity generators as well as end-users. Although any marginal changes of generation and/or load centres also change overall load flow patterns in an intermeshed electricity network, a single event (e.g. feeding in of a new generator, disconnection of a consumer) can t be charged individually for extra measures managing these changes in an intermeshed network. On the contrary, an intermeshed network has to be treated separately within the electricity supply chain since it reacts endogenously on any marginal changes of generation and/or load. 3

9 promotion policies and grid regulation policies 3 is supposed to be indispensable. A precondition for harmonised policies is serious and clear unbundling, i.e. to rethink the definition of the boundary between the RES-E power plant (often characterised by local availability in remote areas) and the grid infrastructure Deep versus shallow versus super-shallow RES-E integration A satisfactory solution for grid operators could be an explicit ex-ante mechanism in the grid regulation process identifying and remunerating any asset stranding or new investment requirements in the electricity grids caused by policies promoting RES-E generation technologies. This is the so-called super-shallow RES-E grid integration approach (see Figure 2.1). In this case, neither the RES-E developer (causing increased investment requirements) nor the grid operators (facing these challenges) bear the costs directly. The knock on effects are directly allocated to the grid tariffs and finally borne by the end-users. On contrary, in the so-called deep RES-E grid integration approach, the RES-E developer bears several extra grid-infrastructure related RES-E integration costs (see also Figure 2.1). In the deep grid integration approach the RES-E developer is encouraged to make the locational decision on the RES-E power plant site having the least negative knock on effects on grid operators. This approach does not cause significant disincentives for the grid operator. The deep RES-E grid integration cost approach will, however, discourage investments into RES-E generation technologies relative to the scenario where RES-E developers do not have to take into account corresponding knock on effects (i.e. supershallow or shallow RES-E grid integration approach). Figure 2.1: Grid integration of new RES-E generation technologies: deep versus shallow versus super-shallow cost allocation approach. Previous paragraphs obviously show the dilemma for policy making: If the RES-E policy aim is to maximise the amount of RES-E generation technologies in the system by a target date, then taking into account also the extra costs imposed on electricity grids by making the RES-E developers pay for them may, hence, not favour the first best resource availabilities ( deep integration cost approach). If the grid regulation policy aim is to overcome the grid operators economic disincentives of asset stranding caused by large-scale RES-E grid integration and, subsequently, to maximise the amount of RES-E generation technologies in the system by a target date, then the extra RES-E related grid integration costs have to be socialised in the corresponding grid tariffs (i.e. correct and strict unbundling) being finally paid by the end-users ( super-shallow integration cost approach). 3 Different grid regulation models describe the different approaches to identify the eligible costs for grid infrastructure planning and grid operation and to translate these costs into grid tariffs. For details on different grid regulation models see e.g. Jamasb/Pollitt (2001). 4

10 2.2 System operation and intermittent RES-E generation Large-scale intermittent RES-E integration into the existing electricity systems expects besides grid infrastructure related measures also a variety of additional measures in overall system operation. Moreover, the management of the intermittent nature of wind generation is one of the major challenges. At present, large electricity systems operate mainly without advanced energy storage technologies (except those systems with large capacities of pumped hydro-storage plants). Therefore, at any instant, output from several electricity generators has to be controlled to equal total load. For these reasons, system operators have to forecast generation and load on timescales from seconds to years and have methods to control the balance continuously. Robust approaches are necessary to estimate the additional system-related requirements and costs for stable system operation. Moreover, there are still many open questions, as there are e.g. where to allocate the corresponding system operation costs, whether or not the corresponding balancing/wholesale markets send out the right price signals or which mechanisms and procedures prevent competition in system operation. In the short-term (times scales below seconds to several hours) a variety of balancing (ancillary) services are necessary for maintaining stable system operation. The driver for short-term system balancing requirements is the magnitude of random power fluctuations, caused by unpredictable changes in both generation and load. System frequency is the parameter used to indicate the balance between generation and load and must be maintained continuously within narrow statutory limits around 50 Hz (see e.g. Norheim (2005)). With no change in generation, system frequency decreases when load is higher than generation and increases when generation is higher than load. In order to manage frequency effectively, system operators utilise a range of balancing (ancillary) services operating according to different time horizons and predominantly involve changes in generation rather than load (see e.g. van Werven et al (2005)). In the long-term, in competitive electricity markets the market itself shall be responsible for providing adequate generation capacities being able to meet peak demand in the system. This is also true for systems with large amounts of intermittent RES-E (wind) generation (see e.g. Danish electricity system). Nevertheless, long-term analyses estimate the capacity contribution of intermittent RES-E (wind) generation on system level. Although wind generation throughout a national network makes some contribution to assured capacity, this contribution is significantly less than for equivalent conventional generation or non-intermittent RES-E generation. The relevant parameter in estimating the system capacity requirement caused by intermittent wind generation is the capacity credit (see e.g. Giebel (2006)). More precisely, the capacity credit is the amount of capacity of conventional or non-intermittent RES-E generation that can be displaced by intermittent wind capacity whilst maintaining the same degree of system security. Roughly the capacity credit is equal to the average capacity factor of wind generation at low wind penetrations, but decreases with increasing wind penetration in a system. In the last decades a number of studies have been carried out aiming to quantify the capacity credit of wind generation in different electricity system configurations. When analyzing the results from different studies it is important to note that due to the different modelling approaches and assumptions it is not permitted to directly compare the published numbers. In Ensslin et al (2006) a structured description of the different approaches is presented and the impact of parameter variations on the capacity credit of wind generation is analyzed. The results clearly indicate that variations of the major input parameters like LOLP (Loss of Load Probability), spatial distribution of wind sites and the wind year considerably influence the resulting capacity credit in the different electricity systems. Last but not least, for robust estimates of the capacity credit of wind generation data quality is essential. Therefore, mainly studies from recent years shall be used since wind data quality has been improved considerably. 5

11 3 Grid-Related and System-Related Costs of RES-E Grid Integration 3.1 Grid infrastructure costs Grid connection costs In section 2.1 the role of the grid infrastructure in the context of RES-E integration is described. Due to the natural monopoly character the electricity grid has to be regulated (i.e. from the welfare-economics perspective a duplication of the grid infrastructure by competing grid operators is suboptimal). Therefore, it is important to clearly define the boundaries of the grid infrastructure towards both ends: electricity generators and end-users. In practise, however, the boundary definitions and responsibilities on both ends of the grid infrastructure are inconsistent: Whereas the responsibility of grid operator (and cost allocation) towards the end-user starts directly at the end-user, on the generation side this is only the case in the super-shallow integration approach (see Figure 2.1). This scenario describes perfect and strict unbundling. But in practise on the generation side in almost all EU Member States still the deep or shallow integration approach is implemented. Therefore, in the GreenNet-Europe simulation model the specific grid connection costs for several RES-E generation technologies are demarcated explicitly in the data base in order to be able to study the economics of RES-E grid integration for several different cost allocation policies (i.e. different unbundling scenarios). Based on the analyses of a variety of case studies on already existing RES-E projects (see Swider et al (2006)) the following specific cost components for grid connection have been derived and implemented into the Green- Net-Europe simulation model: For wind offshore the grid connection costs are clustered depending on the distance to shore. The following specific costs are derived (% of total project investment costs): Zone 0 (near shore): 10%; Zone 1 (< 30 km): 15%; Zone 2 (30-50 km): 20%; Zone 3 (> 50 km): 25%. For wind onshore the grid connection costs are on average 8% of total project investment costs. For remaining RES-E generation technologies they are depending on the specific onsite conditions between 0-5 % of total project investment costs. A variety of GreenNet-Europe model runs (see section 6) are conducted against the background that in the future the re-allocation of the grid connection costs of RES-E generation technologies (and the corresponding socialisation by the grid tariff) is supposed to be an upcoming discussion in policy making Grid reinforcement/upgrading costs Different country-specific studies quantify the grid reinforcement/upgrading requirements and corresponding costs in the next years caused by a variety of different drivers. These drivers include demand increase or power trading activities, in general, and/or extra measures and costs caused by large-scale wind integration, in particular. The summary of wind-related extra grid reinforcement/upgrading costs shown in Figure 3.1 are based on detailed load flow analyses of the corresponding national transmission and distribution grids (in selected European countries) considering different wind penetration scenarios of the most favourable sites each. Figure 3.1 Extra grid reinforcement/upgrading costs depending on wind penetration 6

12 3.2 System operation costs Short-term system balancing costs In electricity systems, the driver for short-term system balancing requirements is the magnitude of random power fluctuations, caused by unpredictable changes in both generation and load. Although intermittent RES-E (wind) generation contributes significantly to random power fluctuations, it is not the only source. Currently, in different electricity systems a variety of different schemes exist for the allocation of short-term extra balancing costs caused by large-scale intermittent wind generation. Figure 3.2 presents a summary of the results of available international studies having been analysed based on a consistent methodological approach. 3.5 Extra Balancing Costs depending on Wind Penetration (Comparison of international studies (except Germany)) Extra Balancing Costs [ /MWh] P Wind,inst / P L,max [%] US-Studien Studies DK Risoe UK ILEX Published German Balancing Cost (E.ON) are far way of the trend line. Therefore, they are not shown here. Figure 3.2 Extra system balancing costs depending on wind penetration Long-term system adequacy costs Long-term analyses estimate the capacity contribution of intermittent wind generation on system level. Although wind power throughout a network makes some contribution to assured capacity, this contribution is significantly less than that for equivalent conventional generation or non-intermittent RES-E generation. The challenge, therefore, is to determine the contribution of intermittent wind generation to system security, i.e. to determine the amount of conventional capacity that can be displaced by intermittent wind capacity whilst maintaining the same degree of system security (without affecting the so-called Loss of Load Probability (LOLP)). For increasing wind penetration in a system, it becomes increasingly less reliable for displacing capacity of conventional power plants since now system reliability is increasingly dominated by intermittent wind generation. Therefore, the capacity credit begins to tail off, see Figure 3.3. Remaining parameters determining the capacity credit of wind are summarised in section 2.2 (details see also in Ensslin et al (2006)). Capacity credit / Capacity factor of wind power 1.6 SCAR-Report 1.4 Dany/Haubrich DENA (Winter) 1.2 DENA 2003: Fig 12-7 High Scenario in GreenNet-Europe Low Scenario in GreenNet-Europe % 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Wind penetration PWind / PL,max Figure 3.3 Capacity credit of wind generation depending on wind penetration 7

13 Based on the determination of the capacity credit of intermittent wind generation extra costs for the residual conventional power plant mix (e.g. due to part-loaded operation of flexible conventional power plants) in a particular electricity system can be estimated in order to maintain system adequacy. The calculation of the extra system adequacy costs caused by intermittent wind generation is based on the thermal equivalent approach according to ILEX (2002) from which the following paragraph (in parts) is cited: The annual wind generation is calculated from the installed capacity in megawatts and the annual full load hours. Then the equivalent amount of conventional capacity required to produce the same annual amount of electricity is determined, assuming a CCGT (combined cycle gas turbine) at an average load factor. However, conventional capacity can be viewed as delivering two services, energy and capacity. Assuming, first, that wind provides no contribution to capacity margin, then to be equivalent to conventional generation wind would require back-up from equivalent conventional capacity. This capacity could come from a number of sources, including old conventional and pumped-hydro generation, new CCGTs or new open cycle gas turbines (OCGTs). For the cost calculation Table 3.1 Example on the calculation of the extra system adequacy costs Example - Calculation of extra system adequacy costs Installed wind capacity 15 GW Average annual full load hours 2600 h/y Total annual wind generation GWh Case I: Without capacity credit CCGT load factor 80 % CCGT full load hours 7008 h/y Thermal capacity equivalent 5.57 GW Capacity credit wind 0 % Capacity contribution wind 0 GW Required thermal capacity 5.57 GW Specific cost of thermal equivalent 55 /kw/y Capacity cost 306 million Extra system adequacy cost per MWh Wind 7.85 /MWh wind Case II: With capacity credit CCGT load factor 80 % CCGT full load hours 7008 h/y Thermal capacity equivalent 5.57 GW Capacity credit wind 27 % Capacity contribution wind 4.05 GW Required thermal capacity 1.52 GW Specific cost of thermal equivalent 55 /kw/y Capacity cost 83 million Extra system adequacy cost per MWh Wind 2.14 /MWh wind the capacity requirements are allocated to new but not leading-edge OCGTs, suitable for peaking operation, based on the consideration that at the margin only OCGTs will be used as any economically feasible existing generation would already be utilised on the system. The annualised capital costs are finally determined depending on annual wind generation. In the second case, if it is considered that wind does contribute to system security, albeit at a smaller rate than conventional capacity, the above capacity requirement is reduced by the level of that contribution. Again, the annualised capacity costs are then derived depending on annual wind generation. Both cases described above are illustrated with an empirical example in Table 3.1 (referring to a realistic UK wind penetration scenario in 2020) Costs for electricity storage technologies and demand response In the GreenNet-Europe model also the role, the corresponding potentials and the economics of electricity storage technologies as well as demand responsive options are considered for supporting system adequacy in case of large-scale intermittent RES-E (wind) generation. Whereas the role of electricity storage technologies already has been comprehensively addressed in different projects in the past, demand response represents a rather new but promising alternative to incorporate the demand side for managing overall system operation: With the exception of already existing flexible pumped hydro storage plants the core problem of several remaining electricity storage technologies (e.g. Battery Systems, Compressed Air Energy Systems, Hydrogen Fuel Cell Storage Systems, Super-Capacitors, Flywheels, etc.) is that they can not be economically implemented into the market up to the year 2020, see e.g. Mariyappan et al (2004). Therefore, the corresponding parameter selection screen is blinded out in the public version of the software tool GreenNet-Europe. The innovative aspect of demand responsive options is that existing capacities are reduced (rather than new capacities installed) in critical time periods of system operation. In detail, an option fee is considered for remunerating the reduction of elastic loads. The advantage of elastic loads is that they can be activated without any delays and this option is fully competitive in constraint markets. In the software tool GreenNet- Europe empirical default values are derived from existing demand responsive markets (e.g. Norway) Resume Summarizing the results on both short-term system balancing costs and long-term system adequacy costs it can be concluded that based on the analyses of a variety of empirical studies (see also Auer (2005)) total extra system operation costs caused by intermittent wind generation are in the range of: below 5% (i.e. around 2-4 Euro/MWh wind ) of the long-run marginal costs of wind generation for small wind penetrations in a system (<10% installed wind capacity), up to 10% (i.e. around 5-6 Euro/MWh wind ) of the long-run marginal costs of wind generation for high wind penetrations in a system (>20% installed wind capacity). 8

14 4 Determination of Potentials and Costs of RES-E Generation in Europe The starting point for deriving dynamic RES-E potentials for electricity generation in a particular country in year n is the determination of the additional mid-term potential up to the year The additional mid-term potential is the maximal additional achievable potential assuming that several existing barriers can be overcome and all driving forces are active. The so-called dynamic potential is the maximal achievable potential in year n triggered by different RES-E promotion instruments and strategies (see Figure 4.1). Theoretical potential Electricity generation Historical deployment Technical potential Dynamic potential year n Policy, Society Economic Potential R&D Mid-term potential 2020 additional realisable potential up to 2020 achieved potential Figure 4.1 Methodology for the definition of different RES-E potentials The methodology for the derivation of the additional mid-term potential varies significantly from one RES-E generation technology to another: 4 In most cases a top-down approach is used (e.g. for wind energy, photovoltaic). In a first step, the technical potential for a single technology in a country for the year 2020 is derived by determining the total useable energy flow of that technology. Then, the mid-term potential for the year 2020 is determined by taking into consideration the technical feasibility, social acceptance, planning aspects, growth rate of industry and market distortions. The additional mid-term potential is given by the mid-term potential minus existing penetration plus decommissioning of already existing plants. 5 For a few RES-E generation technologies, a bottom-up approach is applied (e.g. for geothermal electricity) by investigating every single site (or band) where energy production seems to be possible and by considering various barriers. The accumulated value of the single bands represents the additional mid-term potential for a technology in a country. In this context, one specific problem occurs with respect to biomass allocated to electricity generation. The total primary energy potential for biomass is restricted. The individual shares among the different options - pure electricity generation, CHP generation, heat generation or biofuels - depend on the net economic condition. For the determination of the economics of the different options, furthermore, the corresponding support schemes have to be taken into consideration. Therefore, the different options for the allocation of biomass are linked in the data base in order to be able to model several interdependences. Figure 4.2 summarises the achieved (up to 2004) and additional mid-term potential (up to 2020) for the RES-E generation technologies in the old EU-15 and new EU-12 Member States (incl. Croatia, Norway and Switzerland) on country-level (left) as well as by RES-E generation technology (right). 4 For a comprehensive description of the individual resource-specific approaches of the assessment of future RES-E potentials see Resch et al (2006) or Resch (2005). 5 In the data base of the software tool GreenNet-Europe the additional mid-term potentials on technology level (per country) are further disaggregated into different bands (details see in section 5). 9

15 RES-E - Electricity generation potential [TWh/year] _ AT BE DK FI FR DE GR Additional potential 2020 Achieved potential 2004 IE IT LU NL PT ES SE UK Biogas (Solid) Biomass Biowaste Geothermal electricity Hydro large-scale Hydro small-scale Photovoltaics Solar thermal electricity Tide & Wave Wind onshore Wind offshore RES-E - Electricity generation potential [TWh/year] _ CY Additional potential 2020 Achieved potential 2004 CZ EE HU LV LT MT PL SK SI BG RO HR CH NO Biogas (Solid) Biomass Biowaste Geothermal electricity Hydro large-scale Hydro small-scale Photovoltaics Solar thermal electricity Tide & Wave Wind onshore Wind offshore Figure 4.2 Achieved and additional mid-term potential for RES-E generation technologies in Europe Figure 4.3 below presents a detailed breakdown of the additional mid-term potential for RES-E generation technologies (up to 2020) on country-level: The largest additional RES-E mid-term potential in the EU-15 Member States is available for wind (43%), followed by solid biomass (23%) and biogas (8%). There also exist promising future potentials for tidal and wave energy (11%) and solar thermal energy (3%). The largest additional RES-E mid-term potential in the EU-12 Member States (incl. Croatia, Norway and Switzerland) exists for solid biomass (52%) and wind (19%), followed by biogas (13%). Unlike to the EU-15 Member States, refurbishment and construction of large hydro plants also shows significant shares (6%). Share of additional RES-E _ generation potential % 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% AT BE DK FI FR DE GR IE IT LU NL PT ES SE UK Biogas Biowaste Hydro large-scale Photovoltaics Tide & Wave Wind offshore 24% EU-15 total Breakdown of additonal RES-E 19% generation potential up to 2020 (Solid) Biomass Geothermal electricity Hydro small-scale Solar thermal electricity Wind onshore Figure 4.3a Breakdown of additional mid-term potential for RES-E generation technologies by country 11% 8% 3% 23% 2% 4% 2% 3% 10

16 Share of additioanl RES-E _ generation potential % 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% CY CZ EE HU LA LT MT PL SK SI BG RO HR CH NO Biogas Biowaste Hydro large-scale Photovoltaics Tide & Wave Wind offshore 3% 1% 2% EU-10 total Breakdown of 6% additional RES-E generation potential 3% up to % (Solid) Biomass Geothermal electricity Hydro small-scale Solar thermal electricity Wind onshore 3% 13% 52% Figure 4.3b Breakdown of additional mid-term potential for RES-E generation technologies by country Finally, the long-run marginal generation costs (year 2005) of several RES-E generation technologies in the EU- 27 Member States (incl. Croatia, Norway and Switzerland) are presented in Figure 4.4. Two different depreciation periods (15 years versus lifetime) are compared. 6 High investment costs of almost all RES-E generation technologies have been a barrier for large-scale market penetration so far. However, in recent years investment costs decreased substantially for many RES-E generation technologies. The main drivers for cost reductions have been research and development as well as economies of scale. Also interest rates have been decreasing in the past two decades. The broad range of long-run marginal generation costs for several RES-E generation technologies shown in Figure 4.4 also represents resource-specific conditions in different regions (countries). Long-run marginal generation costs also depend on the availability of technological options (e.g. co-firing, small-scale CHP plants for biomass, etc.). Wind offshore Wind onshore Tide & Wave Solar thermal electricity Photovoltaics Current market price_ cost range (LRMC) PV: 430 to 1640 /MWh Wind offshore Wind onshore Tide & Wave Solar thermal electricity Photovoltaics Current market price_ cost range (LRMC) PV: 340 to 1260 /MWh Hydro small-scale Hydro small-scale Hydro large-scale Hydro large-scale Geothermal electricity Geothermal electricity Biowaste Biowaste (Solid) Biomass (Solid) Biomass (Solid) Biomass co-firing (Solid) Biomass co-firing Biogas Biogas Costs of electricity (LRMC - Payback time: 15 years) [ /MWh] Costs of electricity (LRMC - Payback time: Lifetime) [ /MWh] Figure 4.4 Long-run marginal generation costs (year 2005) of several RES-E generation technologies 6 The long-run marginal generation costs are finally the benchmark (in combination with the corresponding financial promotion schemes) whether or not to build a new RES-E power plant and to enter a particular electricity market. 11

17 5 The Simulation Software Tool GreenNet-Europe 5.1 Modelling approach of GreenNet-Europe The simulation software tool GreenNet-Europe conducts a comparative and quantitative analysis on least-cost RES-E grid integration strategies in the liberalised European electricity market. The analysis can be conducted on aggregated (EU-27 Member States ) level or for individual Member States on an annual basis for the period 2005 to 2020 (2004 is the initial year). The major purpose of the software tool GreenNet-Europe is to investigate RES-E deployment for different cost allocation policies of RES-E grid integration ( deep versus shallow versus super-shallow approach) based on the currently implemented RES-E promotion instruments in the different EU Member States (see e.g. Resch et al (2007)). Scenario selection on a yearly basis ( ) Policy strategies Social behaviour Investor/consumer Externalities General framework conditions (primary energy prices,..) Determination of costresources curve year n supply-side per technology Economic assessment supply-side per technology potential, costs, offer prices Economic assessment demand-side per sub-sector demand elasticity, switch prices Determination of costresources curves year n demand-side per sub-sector RES-E CHP conv. power Trade-offs link of different technologies and markets (supply and demand) RES-E, CHP, DSM, power market, TEA Industry Household Tertiary Feedback year n+1 Results Costs and Benefits Feedback year n+1 on a yearly basis ( ) Figure 5.1 Overview on the least-cost modelling approach in the software tool GreenNet-Europe The general, the modelling approach in GreenNet-Europe is to describe both RES-Electricity generation technologies (supply curve) and energy efficiency measures (demand curve) 7 by deriving corresponding dynamic cost-resource curves. The costs as well as the potentials of these dynamic cost-resource curves can change year by year. These changes are given endogenously in the GreenNet-Europe model depending on the outcome of the previous year (n-1) and the policy framework conditions set for the simulation year (n). Figure 5.2 below describes the derivation of the dynamic cost-resource curve (supply-side) of a particular RES-E generation technology in detail. The static additional mid-term potential for the year 2020 of a particular RES-E generation technology (see also section 4) is described by different bands being characterised by different potentials and long-run marginal generation costs for the integration into the existing European electricity systems (Figure 5.2, left). The different bands describe the economic conditions of different RES-E generation sites (e.g. in case of wind-onshore the annual full load hours, etc.). However, due to a variety of barriers and constraints (industrial, technical, market, administrative, societal) not the entire potential within a particular band can be utilized in one year. Therefore, the achievable potential of each year has to be scaled down accordingly (Figure 5.2, right). 7 In the GreenNet-Europe simulation software tool dynamic demand curves are also derived in the different sectors on the demand side (household, industry, tertiary) and, subsequently, for different end-uses in each sector. The implemented methodology determines on an annual basis both: (i) prices for switching to a more efficient demand side technology and (ii) corresponding potentials of electricity savings. The price level is given exogenously in the model (as costs), the potentials are determined endogenously on an annual basis. In general, the willingness to invest into more efficient demand side energy saving technologies is modelled as the economic trade-off between a new technology and the existing one. The GreenNet-Europe simulation software tool, furthermore, also models known policy promotion instruments on the demand side. For reasons of lucidity the GreenNet-Europe simulation results on energy efficiency measures are not presented in this action plan (focusing on RES-E deployment). They are presented in the corresponding documents being available for download on 12

18 Figure 5.2 Cost-resource curve assessment of a particular RES-E generation technology: Additional midterm potential in year 2020 (left) and derivation of achievable potential in year n (right) The absolute level of the long-run marginal generation costs per band, furthermore, is given also endogenously in the GreenNet-Europe model. This means in particular, that in case of technological learning the generation cost level per band decreases in year (n) depending on the already implemented potential per band in the previous year (n-1). Based on the derivation of the dynamic cost-resource curves an economic assessment takes place in the Green- Net-Europe model considering scenario specific settings like RES-E policy selection, socio-economic parameters (consumer/investor behaviour) as well as wholesale electricity price and demand forecasts. Wholesale electricity price projections on the conventional power market are implemented exogenously in the public available version of GreenNet-Europe. Different wholesale price scenarios (e.g. for different fuel prices, CO 2 certificate prices, etc.) are calculated based on the optimisation tool E2M2 s (having been developed together with GreenNet-Europe in the same EC-contract). A comprehensive model description of E2M2 s can be found in Swider/Weber (2005). 8 Then, in the economic assessment extra costs for system operation (system balancing and system adequacy), grid connection and grid reinforcement are modelled and in case of selection allocated to the marginal generation costs of the corresponding RES-E technology. Figure 5.3 below shows the consideration of these extra cost elements in the supply curve of the GreenNet-Europe model. Figure 5.3 Implementation of extra system operation costs, grid connection costs and grid reinforcement/upgrading costs into the supply curve of the GreenNet-Europe model 8 The format of result presentation in E2M2 s is compatible with the GreenNet-Europe model. An iterative approach is used in modelling the interactions between the conventional power market (E2M2 s ) and RES-E generation (GreenNet-Europe). 13

19 The overall economic assessment, finally, includes a transformation from electricity generation costs and saving costs to bids, offers and switch prices. Promotion instruments for RES-E generation technologies include the most important price-driven strategies (feed-in tariffs, tax incentives, investment subsidies, subsidies on fuel input) and demand-driven strategies (quota obligations based on tradable green certificates, tendering schemes). As GreenNet-Europe is a dynamic simulation tool, the user can change RES-E policies and parameter settings within a simulation run on a yearly basis. Furthermore, several instruments can be set for each country individually. The results are derived on a yearly basis by determining the equilibrium level of generation and demand within each market segment and EU Member State (or any combination of EU Member States up to the EU-27 region) considered. For a comprehensive description of the GreenNet-Europe modelling approach it is referred to Huber et al (2004). Moreover, an even more detailed description of the derivation of the dynamic cost-resource curves as well as the comprehensive GreenNet-Europe data base is conducted in Resch (2005). 5.2 Scenario selection in GreenNet-Europe Several simulation runs in GreenNet-Europe are based on the assumption that currently implemented RES-E promotion instruments remain unchanged up to the year 2020 (Business as Usual (BAU) RES-E policy). Sensitivity analyses are conducted for different cost allocation policies of grid-related and system-related costs of RES-E grid integration. Concerning the allocation of grid-related costs (grid connection, grid reinforcement/upgrading) this means that several RES-E grid integration scenarios ( deep versus shallow versus super-shallow ) see section 2.1 for details can be modelled separately. Extra system operation costs mainly caused by intermittent wind generation are usually allocated to balancing markets (in mature/advanced electricity markets) or to transmission system operators (in electricity markets being still in transition). Finally, the end-user has to cover these cost components either in the energy price (the balancing market price is linked to the wholesale electricity market price and there exist interdependences between these two markets) or in the latter case in the grid tariff. Alternatively, there also exists a policy setting option in the GreenNet-Europe model where extra system operation costs can be allocated to the RES-E generator. An overview on the bandwidth of possible cost allocation scenario settings for RES-E grid integration in the simulation model GreenNet-Europe is given in Figure 5.4. Figure 5.4 Bandwidth of cost allocation scenario settings for RES-E grid integration in GreenNet-Europe 14

20 5.3 Screenshots of GreenNet-Europe Figure 5.5 below presents a small selection of screenshots of the simulation software tool GreenNet-Europe. (1) Simulation software tool GreenNet-Europe (2) Initial settings: RES technology selection (3) Input window: System operation costs (4) Result chart: Share of RES-E generation in 2020 (5) Result table: Cross-section results in 2020 (6) Result chart: Annual grid connection costs of new wind Figure 5.5 Selection of screenshots of the simulation software tool GreenNet-Europe 15

21 6 Results on Least Cost RES-E Integration Scenarios based on GreenNet-Europe 6.1 RES-E deployment in the reference scenario In the RES-E policy reference scenario total RES-E generation within the EU-27 Member States increases from around 500 TWh/yr in 2005 to 1050 TWh/yr in While electricity generation from established RES-E technologies like hydro power (small-scale and large-scale) 9 as well as biowaste remains almost stable, generation from wind (onshore and offshore), biomass and biogas will increase considerable up to 2020, see Figure ,200,000 Total RES-E generation in the EU27 [GWh/year] 1,000, , , , , Biogas Biowaste Hydro small-scale Photovoltaics Tide & wave Wind offshore Solid biomass Geothermal electricity Hydro large-scale Solar thermal electricity Wind onshore Figure 6.1 Reference scenario - Total RES-E generation in the EU-27 Member States up to 2020 Based on Figure 6.1 the corresponding shares of total RES-E generation in the EU-27 Member States are presented in Figure 6.2 for 2005, 2010 and In 2020 wind generation (onshore and offshore) is the most dominant RES-E generation technology (40%), followed by hydro power (37%) and biomass (12%). Shares of RES-E generation technologies [%] 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Wind offshore Wind onshore Tide & wave Solar thermal electricity Photovoltaics Hydro large-scale Hydro small-scale Geothermal electricity Biowaste Solid biomass Biogas Figure 6.2 Reference scenario - Shares of RES-E generation technologies in 2005, 2010 and Taking into account the upcoming negative effects on electricity generation caused by the Water Framework Directive (EC (2000)), total electricity generation from large-scale hydro power may even be lower in 2020 compared to the status quo. 16

22 The corresponding annual capital expenditures for different RES-E generation technologies over time are indicated in Figure 6.3. These capital expenditures significantly vary over time. While annual investments into windonshore and biogas power plants remain almost constant up to 2020, investments into solid biomass and biowaste power plants are mainly expected between 2005 and Beyond 2015 the competitiveness of these RES-E generation technologies is getting increasingly worse. Significant wind-offshore investments are expected beginning in Wind-offshore, finally, is the dominant RES-E generation technology in Annual capital expenditures for RES-E technologies [M /year] 25,000 20,000 15,000 10,000 5,000 0 Biogas Biowaste Hydro small-scale Photovoltaics Tide & wave Wind offshore Solid biomass Geothermal electricity Hydro large-scale Solar thermal electricity Wind onshore Figure 6.3 Reference scenario - Annual capital expenditures for RES-E generation technologies 6.2 Grid-related and system-related costs of wind integration in the reference scenario Based on wind deployment in the policy reference scenario in Figures the development of the specific grid-related costs (grid connection, grid reinforcement/upgrading) and system-related costs (system balancing, system adequacy) of wind integration are presented in Figure 6.4 up to Whereas specific grid connection costs are the most dominant cost component ( /MWh wind ), specific grid reinforcement/upgrading costs are rather low. Cumulated extra system operation costs (i.e. system balancing and system adequacy costs) caused by intermittent wind generation are in the range of /MWh wind in the period from Specific grid-related and system-related costs of wind integration [ /MWh wind ] Grid connection costs System balancing costs Grid reinforcement costs System adequacy costs Figure 6.4 Grid-related and system-related costs of wind integration in the reference scenario Assumptions: Average cost scenario in GreenNet-Europe. Grid connection and grid reinforcement/upgrading costs allocated to new plants (2005 onwards) only. System balancing and system adequacy costs allocated to new and existing plants. 17

23 Derived from the average cost scenario settings in the reference scenario in Figure 6.4, in Figure 6.5 sensitivity analyses are conducted for the extra system operation costs (i.e. system balancing and system adequacy costs) on EU-27 Member States level and also compared with the costs of demand response options. 10 More precisely, based on the average capacity credit settings of wind generation in Figure 6.4 (determining the average extra system operation costs) the bandwidth of extra system operation costs is investigated in Figure 6.5 for variations of the capacity credit settings in the simulation software GreenNet-Europe. The following major conclusions can be derived from these sensitivity analyses (see Figure 6.5): Compared to the average capacity credit scenario, extra system operation costs are doubled when assuming that wind provides no contribution to the capacity margin in the system. For pessimistic settings of the capacity credit of wind, extra system operation costs are 50% higher compared to the average cost scenario; for optimistic settings around 50% lower. If the system capacity margin is met by elastic demand responsive loads, extra system operation costs caused by large-scale intermittent wind generation are just around 25% compared to the average cost scenario. This is mainly due to the fact, that the average annual specific costs determining the option fee (10 /kw/yr) are significantly below the annualized specific investment costs of the thermal equivalent (see calculation example in section 3.2.2). But note, the overall potential of elastic demand responsive loads is considerably limited in a particular electricity system. These restrictions, however, are not implemented on EU Member State s level in the existing version of the software tool GreenNet-Europe. 12 System operation costs for different capacity credit scenarios of wind [ /MWh wind ] Without Capacity Credit Low Capacity Credit Average Capacity Credit High Capacity Credit Demand Response Figure 6.5 System operation costs due to intermittent wind generation in the EU-27 Member States up to 2020 for different capacity credit scenarios in GreenNet-Europe. 6.3 RES-E deployment for different cost allocation policies of grid-related and system-related costs One of the main features of the software tool GreenNet-Europe is the capability to simulate several cost allocation policies of RES-E grid integration: deep, shallow and super-shallow approach. Whereas in the deep integration approach the RES-E developer has to pay several extra grid-related and system-related costs upfront (i.e. cover these costs in the long-run marginal RES-E generation costs), the super-shallow approach represents the other extreme (end-user pays corresponding disaggregated costs components via grid tariffs and electricity prices). The shallow approach represents a hybrid solution between the two extremes, where the grid connection costs are usually allocated to RES-E developer (details see in section 2.1 and 5.2)). 10 Demand responsive loads are an alternative option to mitigate the challenges faced by intermittent wind generation on system level (details see in section 3.2). 18

24 The cost allocation philosophy of disaggregated cost components of RES-E integration influences the dynamics of RES-E deployment. The bandwidth of the development of total RES-E generation for the two extreme scenarios as well as the reference scenario is shown in Figure 6.6. In relative terms, total RES-E generation is around 4% higher for the super-shallow integration scenario compared to the reference scenario in On contrary, the deep integration approach results in a decrease of around 4% of total RES-E generation in ,100,000 1,000, , , , , , Total RES-E generation in the EU27 for different cost allocation policies [GWh/yr] Reference scenario Super-shallow scenario Deep scenario Figure 6.6 Deployment of total RES-E generation in the EU-27 Member States for different cost allocation policies of disaggregated cost components up to Due to the fact that deviations of RES-E deployment in the different cost allocation scenarios are mainly influenced by the wind technology, in Figure 6.7 total wind generation is analyzed in detail. In this context the allocation of the grid connection costs of wind-offshore plays a core role; i.e. whether or not the super-shallow approach is implemented in corresponding legislation of wind integration in the different countries with significant wind-offshore potentials. This is the case in countries like Denmark and Germany (see reference scenario in Figure 6.7). Assuming a super-shallow approach for both wind-onshore and wind-offshore in several EU-27 Member States higher wind generation can be observed up to Assuming the opposite (in practice rather unrealistic) extreme of wind integration in several EU-27 Member States (the deep integration approach) significantly less total wind generation can be observed compared to the reference scenario, see Figure ,000 Total wind generation in the EU27 for different cost allocation policies [GWh/yr] 400, , , , , , ,000 50,000 Reference scenario Super-shallow scenario Deep scenario Figure 6.7 Deployment of total wind generation (onshore & offshore) in the EU-27 Member States for different cost allocation policies of disaggregated cost components up to

25 Derived from previous Figures , in Figures the changes of the RES-E generation portfolio for the super-shallow (Figure 6.8) and deep (Figure 6.9) integration approach are presented compared to the reference scenario in year Due to the dominance of the grid connection costs of the wind technology (within the different disaggregated cost components of RES-E integration), wind-onshore and wind-offshore generation is affected most in the different extreme scenarios: significantly higher generation ( super-shallow approach), significantly less generation ( deep approach; especially for wind-offshore; see Figure 6.9). 10% Super-Shallow: Changes of RES-E generation portfolio compared to the reference scenario in 2020 [%] 8% 6% 4% 2% 0% -2% Biogas Solid biomass Biowaste Geothermal electricity Hydro largescale Hydro smallscale Photovoltaics Solar thermal electricity Tide & wave Wind onshore Wind offshore Figure 6.8 Changes of the RES-E generation portfolio in the super-shallow integration approach compared to the reference scenario in year Deep Approach: Changes of RES-E generation portfolio compared to the reference scenario in 2020 [%] 0% -5% -10% -15% -20% -25% -30% Biogas Solid biomass Biowaste Geothermal electricity Hydro largescale Hydro smallscale Photovoltaics Solar thermal electricity Tide & wave Wind onshore Wind offshore Figure 6.9 Changes of the RES-E generation portfolio in the deep integration approach compared to the reference scenario in year

26 6.4 Correct remuneration of grid-related and system relatedcosts according to basic unbundling principles A precondition for correct remuneration of disaggregated cost components of RES-E integration is correct unbundling and, subsequently, corrected cost allocation to the three different options: RES-E promotion instruments versus grid tariffs versus wholesale/balancing markets. At present, there exist a variety of different cost allocation approaches in the different EU Member States still violating basic unbundling principles. Figure 6.10 indicated the different cost allocation approaches of grid connection costs ( super-shallow versus shallow / deep ) of wind integration in the EU-27 Member States in the reference scenario up to More precisely, in the super-shallow approach (implemented for wind-offshore integration in DK and DE) the corresponding grid connection costs are allocated to the grid infrastructure and, therefore, remunerated in the grid tariffs. In remaining EU-27 Member States (incl. wind-onshore in DE) still the shallow or deep integration approach is implemented where grid connection costs are assumed to be part of the total project costs and, therefore, remunerated in the corresponding RES-E promotion instruments (feed-in tariffs, TGCs, etc.). The latter case is ambiguous twofold: (i) basic unbundling principles are violated, (ii) a comparison of electricity generation costs of RES-E technologies with conventional power plants is systematically wrong since RES-E generation costs also include grid infrastructure components (on contrary to conventional power plants). 11 Therefore, exclusively the super-shallow approach shall be implemented for RES-E (wind) grid connection. 2,500 Grid connection costs caused by wind [M /yr] 2,000 1,500 1, Allocated to End-user Allocated to RES-E generator GC costs for wind-offshore in DK & DE (super-shallow integration approach) GC costs in remaining EU27 Member States (shallow or deep integration approach) Figure 6.10 Development of grid connection costs of wind integration in the EU-27 Member States in the reference scenario up to 2020 (incl. indication of cost allocation schemes). Assumptions: Average cost scenario in GreenNet-Europe. Grid connection costs allocated to new plants (2005 onwards) only. Grid connection costs for wind-offshore are 10-25%, for wind-onshore 8% of total project costs. In Figure 6.11 the development of grid reinforcement/upgrading costs caused by wind integration in the EU-27 Member States are presented in the reference scenario up to 2020 (incl. the indication of the corresponding cost allocation approach). The deep integration approach still being implemented in the majority of EU Member States again violates basic unbundling principles and correct cost allocation schemes. Grid reinforcement/upgrading costs caused by RES-E integration exclusively have to be remunerated via grid tariffs. 11 E.g., there doesn t exist a grid integration discussion of the nuclear power plant Olkiluoto currently being built in Finland (or in general for any conventional power plant having been built up to now). This nuclear power plant also causes besides grid connection significant grid reinforcement/upgrading measures and costs in the Nordic electricity system. Implementation of correct unbundling, however, allocates and remunerates the different cost components correctly (e.g. grid infrastructure cost components are not incorporated into the calculation methodology of the electricity generation costs of the nuclear power plant; they are incorporated into the corresponding grid tariffs directly). 21

27 Grid reinforcement costs caused by wind [M /yr] Allocated to End-user Allocated to RES-E generator GR costs in DE, BE, DK, NL (shallow integration approach) GR costs in remaining EU27 Member States Figure 6.11 Development of grid reinforcement costs of wind integration in the EU-27 Member States in the reference scenario up to 2020 (incl. indication of cost allocation schemes). Assumptions: Average cost scenario in GreenNet-Europe. Grid reinforcement/upgrading costs of the transmission grid allocated to new plants (2005 onwards) only. Figure 6.12 finally presents the development of extra system balancing costs caused by intermittent wind generation on EU-27 Member States level in the reference scenario up to These extra system balancing costs indicate the costs for mitigating the short-term risk of imbalance in the system. 12 At present, on EU-27 Member States level the different remuneration schemes of these extra system balancing costs are equally shared between RES-E generators and end-users. These country-specific approaches, however, are still very heterogeneous and described by individual characteristics each. In the future, therefore, one of the major challenges is to implement homogeneous and transparent market mechanisms sending out the right price signals to several market participants being interested and qualified to contribute to balancing markets. 900 System balancing costs caused by wind [M /yr] Allocated to End-user Allocated to RES-E generator Risk of imbalance allocated to and borne by RES-E generator Risk of imbalance socialised and borne by end-user Figure 6.12 Development of extra system balancing costs of wind integration in the EU-27 Member States in the reference scenario up to 2020 (incl. indication of cost allocation schemes). Assumptions: Average cost scenario in GreenNet-Europe. System balancing costs allocated to new and existing plants. 12 In the model GreenNet-Europe, long-term system adequacy is described by system adequacy costs (corresponding modelling results are not presented in this section). In functioning electricity markets the price signals sent out by wholesale markets shall guarantee adequate system capacities (incl. corresponding margins) at any instant. Therefore, system adequacy costs caused by large-scale intermittent wind generation are rather investment opportunities (than costs) in the generation sector. 22

28 7 Policy Recommendations In the previous sections, several aspects of least-cost RES-E integration have been comprehensively analysed and modelled based on the simulation software tool GreenNet-Europe. The major lessons learned can be summarized as follows: There exist the following different disaggregated cost components describing total integration costs of intermittent RES-E generation technologies: RES-E power plant, grid connection, grid reinforcement/up-grading, short-term system balancing, long-term system adequacy. At present, basic unbundling and market principles are still often violated in the context of RES-E integration in almost all EU Member States. Cost allocation policies of disaggregated cost components of RES-E integration are very heterogeneous in different EU Member States: - Grid infrastructure: Deep versus shallow versus super-shallow cost allocation approach - System operation: Intermittency risk either socialized to end-user or fully covered by RES-E generator Cost allocation policies significantly influence overall economics and competitiveness (electricity generation costs) of RES-E technologies and, subsequently, future RES-E deployment. Amendments of cost allocation policies of RES-E integration also expect readjustments of RES-E promotion instruments in order to achieve comparable future RES-E deployment. The important role, responsibilities and also claims of grid operators in the context of large-scale RES-E integration have not been realized so far. Large-scale RES-E integration might take place on the expense of grid operators if RES-E grid integration costs are not eligible in the grid regulation process. Based on these lessons learned, finally, the following action plans for stakeholders and policy recommendations for decision makers are drawn to enable large-scale intermittent RES-E grid integration into the European electricity systems: 1. Implement correct unbundling! - A clear definition of the boundary between (and separation of) the RES-E power plant, the grid infrastructure and overall system operation is indispensable. - Super-shallow or at least shallow RES-E integration (incl. transparent locational signals to RES-E developers) is preferable compared to the deep integration approach. - Grid infrastructure cost components of RES-E integration shall be incorporated into the corresponding grid tariffs, extra system operation costs shall be remunerated in corresponding markets. 2. Establish markets in system operation to mitigate intermittency risk of RES-E generation! - Implement transparent markets in system operation (balancing markets for short-term system balancing; wholesale markets for long-term system adequacy) sending out the right price signals to interested and qualified market participants (with an extended geographical coverage compared to the status quo). - Improve and amend the market rules of already existing markets in this context (e.g. shorter gate closure, continuous day-ahead markets, etc.) and further support technological development to improve wind forecasting accuracy. 3. Don t neglect grid operators point-of-view! - Large-scale RES-E grid integration can t take place on the expense of grid operators. Currently existing regulatory uncertainties of cost recovery (in the context of RES-E grid integration in particular) won t trigger any investments into the capital intensive grid infrastructure since investments into the grid are effectively sunk and, therefore, vulnerable to changes of regulatory conditions. - Therefore, explicit ex-ante mechanisms for cost recovery have to be implemented in the grid regulation process providing incentives for grid operators to invest. - Moreover, grid regulation policies also explicitly have to take into consideration future responsibilities and corresponding investment needs of grid operators (e.g. for the transformation of existing, passive distribution grids into actively managed, intelligent distribution networks absorbing large-scale distributed and intermittent RES-E generation). 4. Finally, start RES-E promotion policy discussion based on these changed circumstances again! 23

29 References Auer H.: The Relevance of Unbundling for Large-Scale RES-E Grid Integration in Europe, Energy & Environment, Vol. 17, No. 6, p , ISSN X, Auer H.: Economics of Intermittent Wind Generation Value and Additional Costs of Large-Scale Wind Integration, Windtech International, Vol. 1, No. 7, p , ISSN , Auer H., T. Faber, C. Huber, G. Resch: Action Plan for an Enhanced Least-Cost Integration of RES-E into the European Grid, GreenNet Project Report (WP10), Ensslin C., A. Badelin, Y.-M. Saint-Drenan: The Influence of Modelling Accuracy on the Determination of Wind Power Capacity Effects, Proceedings, European Wind Energy Conference (EWEC), Athens, European Commission: Directive 2003/54/EC of June 26th 2003 concerning common rules for the internal market in electricity and repealing Directive 96/92/EC, European Commission: Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy, Giebel G.: Wind Power has a Capacity Credit A Catalogue of 50+ Supporting Documents, Wind Engineering, UK ISSN X, Volume 30, Huber C., T. Faber, G. Resch, H. Auer: The Integrated Dynamic Formal Framework of GreenNet, GreenNet Project Report (WP8), ILEX Energy Consulting & Manchester Centre for Electrical Energy (UMIST): Quantifying the system costs of additional renewables in 2020, Report for the Department of Trade and Industry (DTI), London, Jamasb T., M. Pollitt: Benchmarking and Regulation: International Electricity Experience, Utilities Policy, 9, p , Mariyappan J., M. Black, G. Strbac, K. Hemmi: Cost and Technical Opportunities for Electricity Storage Technologies, GreenNet Project Report (WP3), Norheim I., O. Mogstad, P. Sørensen, C. Jauch, D. Pudjianto, O. Anaya-Lara: Case Studies on System Stability with Increased RES-E Grid Integration, GreenNet-Europe Project Report (WP4), Obersteiner C., T. Faber, G. Resch, H. Auer, W. Prüggler: Modelling Least-Cost RES-E Grid Integration under different Regulatory Conditions based on the Simulation Software GreenNet-Europe, GreenNet-Europe Project Report (WP3), Resch G., T. Faber, R. Haas, C. Huber, M. Ragwitz, A. Held, P.-E. Morthorst, S.-G. Jensen, R. Coenraads, M. Voogt, G. Reece, I. Konstantinaviciute, B. Heyder: Recommendations for implementing effective and efficient renewable electricity policies, OPTRES-Project Report (Action Plan), Resch G., C. Obersteiner, H. Auer, W. Prüggler, G. Ruggieri, F. Forfori: Database on Potentials and Costs of RES-E and Energy Efficiency for the EU-27 Member States (incl. HR, CH, NO), GreenNet-Europe Project Report (WP1), Resch G.: Dynamic cost-resource curves for electricity from renewable energy sources and their application in energy policy assessment, PhD Thesis, Vienna University of Technology, Vienna, Swider D. (ed.): Case Studies on conditions and costs for RES-E grid integration, GreenNet-Europe Project Report (WP5), Swider D.J., C. Weber: Scenarios on the conventional European electricity market, GreenNet Project Report (WP6), Van Werven, M.J.N., L.W.M. Beurskens, J.T.P. Pierik: Integrating Wind Power in EU Electricity Systems: Economics and Technical Issues, GreenNet Project Report (WP4),

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