THE INFRASTRUCTURE IMPLICATIONS OF THE ENERGY TRANSFORMATION IN EUROPE UNTIL 2050 LESSONS FROM THE EMF28 MODELING EXERCISE

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1 Climate Change Economics, Vol. 4, Suppl. 1 (2013) (26 pages) The Authors DOI: /S X THE INFRASTRUCTURE IMPLICATIONS OF THE ENERGY TRANSFORMATION IN EUROPE UNTIL 2050 LESSONS FROM THE EMF28 MODELING EXERCISE FRANZISKA HOLZ *, and CHRISTIAN VON HIRSCHHAUSEN *, * DIW Berlin (German Institute for Economic Research) Mohrenstrasse 58, Berlin, Germany Workgroup for Economic and Infrastructure Policy Berlin University of Technology TU Berlin, Strasse des 17. Juni 135, Berlin, Germany fholz@diw.de Published 5 December 2013 This paper summarizes the approaches to and the implications of bottom up infrastructure modeling in the framework of the EMF28 model comparison Europe 2050: The Effects of Technology Choices on EU Climate Policy. It includes models covering all the sectors currently under scrutiny by the European Infrastructure Priorities: Electricity, natural gas, and CO 2. Results suggest that some infrastructure enhancement is required to achieve the decarbonization, and that the network development needs can be attained in a reasonable timeframe. In the electricity sector, additional cross-border interconnection is required, but generation and the development of low-cost renewables is a more challenging task. For natural gas, the falling total consumption could be satisfied by the current infrastructure in place, and even in a high-gas scenario the infrastructure implications remain manageable. Model results on the future role of Carbon Capture, Transport, and Sequestration (CCTS) vary, and suggest that most of the transportation infrastructure might be required in and around the North Sea. Keywords: Infrastructure; electricity; natural gas; CO 2 ; CCS; modeling. 1. Introduction Infrastructure questions of how to bring energy from the production site to the consumers are underlying all analytical efforts of drawing the perspectives of our energy systems. However, they are often forgotten or hidden behind the large numbers. For example, when the MIT discussed the Future of Natural Gas (MIT, 2011) and projected a temporary strong increase of natural gas use, they looked at the power plant capacities to burn the gas. But one must also take into consideration the pipeline Corresponding author. This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 3.0 (CC-BY) License. Further distribution of this work is permitted, provided the original work is properly cited

2 F. Holz & C. von Hirschhausen capacities required to bring the gas from the import or production sites to the power plants. It may actually not be worth the investment to install high-pressure pipelines for just a decade of utilization. Moreover, in the last years renewables have grown with a breath-taking speed in Europe, in particular in Germany, and are expected to continue this growth. But the Germans have started to realize that they will need an expanded electricity grid to bring the wind power to the consumers. This has resulted in the advent of network planning processes, both at the European and the Member States level. Moreover, offshore wind capacities bring a particular challenge in terms of connection to the onshore grid. And when the offshore wind in Germany blows, this does also affect its neighbors due to the loop flows in the electricity grid (ENTSO-E, 2012). This anecdotal evidence shows that transmission infrastructure needs to be taken into account when deriving projections of future energy systems such as the Energy Roadmap 2050 (EC, 2011). Infrastructure considerations shall at best directly be taken into account when forecasting future system states, but they need at least to be verified ex post to not represent unsurmountable (i.e., very costly) obstacles to the realization of the forecasts. The objective of this paper is to explicitly address the role of infrastructure in the transformation process in Europe on the way to a low-carbon, renewables-based system. The paper summarizes the approaches to and the implications of bottom up infrastructure modeling in the framework on the EMF 28 model comparison Europe 2050: The Effects of Technology Choices on EU Climate Policy. It is a complementary effort to the top down models presented in Knopf et al. (2013). The infrastructure subgroup included a number of different infrastructure models covering all the sectors currently under scrutiny by the European Infrastructure Priorities: Electricity, natural gas, and CO 2. In each case, the modelers investigated the specific role of transmission infrastructure in the achievement of a low-carbon energy system, what potential opportunities and obstacles may emerge from there, and what this implies for the respective decarbonization scenarios. Energy infrastructure sectors electricity, natural gas, and other network-based sectors are to some extent included in all top down models, be it energy system, Integrated Assessment or Computable General Equilibrium models. But we go into more detail than these models can, in particular with respect to transmission infrastructure (high-voltage electricity lines, high-pressure natural gas pipelines, and CO 2 pipelines). In this paper, we provide an overview of the sectoral models and the storylines that are covered, highlighting where and why models may differ and concluding on the main infrastructure issues for decarbonizing the European energy sector. Of course, our analysis is limited to the models in the EMF group and can only be a first attempt to systematically include the infrastructure aspect in the analysis. We have been somewhat limited by the fact that the models in this subgroup have partially used different data sets and that the model foci and boundaries vary considerably, even for the same

3 The Infrastructure Implications of the Energy Transformation in Europe Until 2050 Table 1. EMF 28 scenario definitions used in the subgroup infrastructure. Default w CCS Default w/o CCS Pessimistic Optimistic Green Technology dimension CCS on off off on off Nuclear energy ref ref low ref low Energy efficiency ref ref ref high high Renewable energies ref ref ref ref opt Policy dimension for the EU No policy baseline (no BASE policy, also without the 2020 target) Reference: including 40%DEF 40%noCCS 40%PESS 40%EFF 40%GREEN the 2020 targets and 40% GHG reduction by 2050 Mitigation1: 80%GHG reduction by 2050 (with Cap&Trade within the EU) 80%DEF 80%noCCS 80%PESS 80%EFF 80%GREEN sector, so that it is not straightforward to carry out a comparison. Hence, further research needs to follow up to confirm or refute the main trends and investment needs, in particular (but not exclusively) for the electricity sector. This paper is structured in the following way: The next section provides an overview of the sector models within the EMF 28 group. Sections 2 4 then provide the main results in some more detail, by crossing scenarios and results, and by extracting information from those runs that provide the deepest insight into why models may differ. For each of the infrastructure sectors covered, we provide an in-depth analysis of the potential infrastructure needs: Electricity, natural gas, and CO 2 pipelines. Section 5 concludes. We refer to the same scenario matrix in the EMF 28 as outlined in Knopf et al. (2013) (see Table 1). It includes Reference Scenarios respecting the EU 2020 targets and 40% greenhouse gas reductions by 2050, and Mitigation 1 scenarios of an 80% reduction of GHG emissions by These reduction scenarios were crossed with technology-specific scenarios to give the matrix: Scenarios 40%DEF and 80%DEF describe a default case including CCTS (carbon capture, transportation, and storage) and reference assumptions on nuclear energy, energy efficiency, and renewable energies. Other 40% scenarios and 80% scenarios perform variations on the technology availability, taking the 40% or 80% GHG reduction as given, respectively. It was up to the individual modelers to concretely define the scenarios, i.e., to provide their estimates for the low-ref-opt-high ( low-reference-optimistic-high ) fields. All infrastructure models ran the scenarios 40%DEF and 80%DEF, and most of them also ran

4 F. Holz & C. von Hirschhausen Table 2. Reported models and scenarios in the EMF 28 infrastructure subgroup. EMF Scenario/Model Sector 40% DEF 80% DEF 80% noccs 80% PESS 40% EFF 80% EFF 40% GREEN 80% GREEN EMPS [1] Electricity X X LIMES-EUþ [2] Electricity X X ELMOD [3] Electricity X X X Combining [4] Electricity/ X X X natural gas GGM [5] Natural gas X X X X X X X X Ramona [6] Natural gas X X X X X X X X CCTSMOD [7] CCTS X X X X Note: [1]: Wolfgang et al. (2009); [2]: Haller et al. (2012); [3]: Leuthold et al. (2012); [4]: Abrell and Weigt (2012); [5]: Egging (2013); [6]: Hellemo et al. (2012, 2013); [7]: Herold et al. (2012). additional scenarios, amongst them scenarios the efficiency (optimistic) scenarios 40% and 80%EFF and the green scenarios 40% and 80%GREEN. 1 Table 2 provides an overview of the infrastructure models that were used in the comparison for different sectors (electricity, natural gas, CO 2 ), as well as the scenarios that they ran. The electricity models EMPS (by SINTEF Energy Research), LIMES-EUþ (PIK), and ELMOD (TU Berlin, DIW Berlin) concentrate on the default EMF 28 scenarios with CCTS (40%DEF and 80%DEF as well as 80%EFF) to compare their infrastructure results. The Combining model (Basel University, ETH Zurich, DIW Berlin), representing the electricity as well as the gas market, in addition analyzes the scenario 80%GREEN. The Global Gas Model (NTNU, DIW Berlin) and the RAMONA gas model (NTNU, SINTEF) run the scenarios 40% and 80%DEF, 40% and 80%EFF, 40% and 80%GREEN, as well as 80%noCCS, 80%PESS, and BASE. CCTSMOD (TU Berlin, DIW Berlin) is used for all scenarios which assume an implementation of the CCTS-technology: 40% and 80%DEF, as well as 40% and 80%EFF. Practically all models in the infrastructure group base their scenario-relevant input data for EU-27 on the respective scenario results of the PRIMES model (Capros et al., 1998). The PRIMES model provided the numerical analysis for the EU Energy Roadmap 2050 (EC, 2011) and participated in the EMF model comparison with these scenarios. There is a correspondence between the Roadmap scenarios and the EMF 28 scenarios because of similar scenario definitions. 2 PRIMES results were provided for a wide range of variables and on member-state level, which is a great advantage compared to the other models. PRIMES provided results for the scenarios 40% and 80%DEF, 40% and 80%EFF, 40% and 80%GREEN, as well as 80%noCCS, 80%PESS, and BASE. 1 The scenarios Mitigation 2 and Mitigation 3 investigate the international context and are not run by the models of the subgroup infrastructure. Some models also ran BASE, the No Policy Scenario to provide counterfactual results. 2 PRIMES results of 40%DEF correspond to the Reference scenario in EC (2011), 80%DEF to the scenario Diversified supply technologies, 40%EFF to Reference-CPI, 80%noCCS to Delayed CCS, 80%EFF to High energy efficiency, and 80%GREEN to High RES

5 The Infrastructure Implications of the Energy Transformation in Europe Until 2050 Given the reliance on PRIMES for the input variable definition, this scenario set was also the upper bound for the modeling teams in the infrastructure group. 2. Electricity Network Requirements Electricity transmission infrastructure is often in the focus of the debate, as it is important for the low-carbon energy transformation. The EU has institutionalized a Ten-Year Network Development Plan (TYNDP), that is developed jointly by the transmission system operators (TSOs) and the Agency for the Coordination of Energy Regulators ACER (e.g., ENTSO-E, 2012). Studies on the need of massive transmission investments abound, e.g., Tröster et al. (2011) proposing a European backbone grid, European Climate Foundation (2010, 2011) estimating the needs for a decarbonized European electricity sector, or, the EU Energy Roadmap (EC, 2011, p. 75). The vision of European-wide, high-voltage transmission corridors (often called SuperGrids ) has dominated the debate recently, with large-scale projects like Desertec or the North Sea Grid connection as prominent examples (von Hirschhausen, 2010). The European Infrastructure Priorities (Impact Assessment, EC 2010) assume investment requirements of 142 bn. for electricity transmission. Berger (2011) has argued that the financing of the significant infrastructure requirements was feasible if an appropriate regulatory framework was provided. Previous research has also highlighted the importance of investment in generation, for example in back-up capacities of fast-ramping gas-fired power plants, so that another challenge in the decarbonization is generation, in addition to transmission infrastructure (ECF, 2012; ECF, 2011). Given this current policy setting, we provide a model comparison of the two models that assess the electricity infrastructure implications of some of the EMF 28 scenarios (EMPS, and ELMOD); we add evidence from a third model that has analyzed different technology options on the decarbonization route (LIMES-EUþ). These sectoral models take the results of the energy-system model PRIMES in terms of electricity generation capacity by each technology as given, and optimize the network that is necessary to efficiently serve the demand from these capacities. Figure 1 shows these generation capacities by technologies for the two DEFAULT scenarios (40%DEF and 80%DEF). Unsurprisingly, renewable capacities are drastically expanded between 2010 and 2050, in the stronger decarbonization case even more than in the 40%DEF. Renewables are not implemented uniformly across Europe but according to the national renewable allocation plans and the renewable potential (i.e., wind potential, solar irradiation). At the same coal generation capacity is gradually reduced, with about half of the 2050 capacity being equipped with CCS in both scenarios. However, gas capacity is expanded over time, despite a rather stable generation level from gas-fired power plants, because it is needed as back-up capacity in peak demand hours that coincide with low renewable generation. Two bottom up electricity system models have provided an assessment of the infrastructure implications of the scenarios 40% and 80%DEF: The EMPS model (Bakken et al., 2013), and the ELMOD model (Egerer et al., 2013), the latter also

6 F. Holz & C. von Hirschhausen Figure 1. Electricity generation capacities by technology in PRIMES runs 40%DEF and 80% DEF between 2010 and 2050 (in GW). analyzing the 80%GREEN scenario. Both models optimize the investments in transmission capacity with the objective to minimize combined transmission infrastructure and variable generation costs. At the national level, PRIMES EMF 28 results of generation capacity, demand, fuel prices, annual renewable generation levels and CO 2 prices are used as inputs to the electricity models. In a rolled planning approach the models apply the results for one year as start topology for the consecutive period. Each model focuses on either of the two following aspects: The level of spatial detail for the implementation of the transmission infrastructure (ELMOD), and the varying levels of physical weather and system conditions over time on the scale of Europe (EMPS). Both models use their own data sets on costs and transmission lines, but the PRIMES levels for electricity generation capacity or renewables production of each technology. The consumption levels are computed endogenously in the model runs by EMPS, but taken as model input together with the CO 2 and fuel prices (from PRIMES) by ELMOD. These differences on the date treatment in addition to the modeling approaches must be kept in mind when comparing the results. In addition, EMPS and ELMOD apply two different modeling approaches and by that are representative for the two major streams in the literature: EMPS is a model based on net transfer capacities (NTCs), while ELMOD is a DC load flow model. The difference between both approaches is explained below, and also triggers some of the differences between results. The EMPS model increases the time resolution for the generation dispatch to 75 years with a total of 655,200 h differing by weather data in 40 national nodes. The spatial resolution remains at the Member State level with one node per country, plus several additional offshore nodes and the neighboring states in East Europe and North Africa. The optimization of the cross-border NTC is implemented with an iterative investment algorithm which determines line upgrades according to price differences between nodes. NTCs are not only cross-border line capacity levels but their values also include the absorption capacity of the national transmission grid behind the border. Hence, the result of whether there is expansion of NTC capacities cannot be

7 The Infrastructure Implications of the Energy Transformation in Europe Until 2050 directly interpreted as a need to invest in cross-border lines but it also reflects an investment need in the national grids though it does not correctly quantify this. NTC model results should rather be compared among each other for different scenarios than to real-world transmission line approaches. The ELMOD model, in contrast, determines the optimal infrastructure expansion in a line-sharp approach of the high-voltage network, i.e., all lines and network nodes (e.g., power plants, transformer stations) are included in the data set (3,525 network nodes and 5,145 transmission lines). This detailed disaggregation level requires that the PRIMES results on national level provided for EMF 28 scenarios (generation capacity, renewable generation and demand) be down-scaled to the nodal level. In order to minimize generation costs the model can endogenously expand the existing electricity grid by upgrading lines to higher voltage levels, adding circuits (and thereby capacity) on existing lines or building new HVDC lines. The model applies the DC ( direct current ) load flow approach for the AC ( alternating current ) networks and calculates nodal prices (Schweppe et al., 1988). The change in physical line characteristics after grid expansion is implemented in an iterative optimization for each period to keep the linear mixed-integer character of the dispatch and investment model. The results provide detailed insights in individual line investments and thus calculate well-founded cost figures. Compared to the EMPS model, the ELMOD team models a small number of 18 representative hours, which differ by demand and renewable generation level that are given by PRIMES. Each scenario is applied uniformly to all nodes of one country. The results of the EMPS model indicate that the expansion of the European transmission network focuses on two regions where renewables will play a particular role (Fig. 2):. In the North Sea, the offshore wind integration requires additional transmission capacity in both scenarios which is mostly realized by radial lines to the closest shore line. In the North and Baltic Sea region, additional cross-border network capacity of about 5 GW is built between Great Britain, the Netherlands, Germany, Denmark, Sweden and Poland to balance temporal differences in wind power generation;. In southern Europe, the axis Spain, France and Italy is reinforced, with major investments throughout the model horizon, to accommodate the additional renewables generation and balance temporal production differences between the countries. The 80%DEF scenario includes additional cross-border capacity of about GW from Northern Africa through Italy all the way to France and Germany. The total cumulative expansion of cross-border lines in EMPS until 2020 is 83 GW in the 40% scenario and 82 GW in the 80% scenario. Only after 2020, the scenarios differ substantially: until 2050, a total of 200 GW (40% scenario) or 362 GW (80%) are installed. The model results clearly indicate a positive correlation between the renewable shares and required cross-border extensions until

8 F. Holz & C. von Hirschhausen Source: Bakken et al. (2013). Figure 2. Total cumulative expansion of cross-border transmission capacities in EMPS for scenarios 40%DEF (left) and 80%DEF (right) for ELMOD differentiates investments in the AC network and an overlay high voltage DC (HVDC) topology. Figure 3 shows that it finds somewhat less investment needs in the network than EMPS to accommodate the decarbonization of the electricity sector, even in the 80%DEF scenario. The results for all scenarios indicate investments in HVDC connectors, mostly between the non-synchronized networks (Scandinavia, Ireland, United Kingdom, and Continental Europe) to allow for balancing of temporal and spatial differences of renewable generation between non-these zones. With increasing shares of renewable generation in the 80% scenarios (in GREEN even more than in DEF) the HVDC investments increase from 7 connectors of 2 GW in 40%DEF to 9 connectors in 80%DEF and 12 connectors in 80%GREEN. Table 3 shows the investment expenditures for the three EMF 28 scenarios that were run by the ELMOD team, differentiating between cross-border and intra-country investments in the regular AC network and investments in the new HVDC transmission technology. While the 40% scenario with less strict GHG emission targets only implies investments of about 30 bn, the 80% scenarios have investment expenditures above 56 bn. A large share of the investments occurs before 2020 to implement the expansions currently under construction or in the official planning process. Another large investment push in the 80% scenarios arises close to the model horizon s end, i.e., in or after 2040, to obtain the flexible electricity transmission needed to accommodate the large share renewables that is necessary to attain the ambitious GHG targets

9 The Infrastructure Implications of the Energy Transformation in Europe Until 2050 Note: Straight bold lines in the North and Baltic Sea are HVDC lines, all others are AC lines. The darker the line, the earlier the investment takes place. Figure 3. ELMOD line expansion until 2050 in scenarios 40%DEF and 80% DEF. Table 3. Total investment expenditures per technology in the ELMOD model (in bn. ). Scenario HVDC AC National AC Cross-Border Total 40%DEF %DEF %GREEN All HVDC connections built by the ELMOD model are cross-border connections. However, the majority of investment occurs nationally, i.e., on lines that are located within one country. This reflects the fact that domestic lines are needed to link power plant sites to consumption regions, and that national links can generally be provided at lower costs due to shorter distances. Cross-border exchange is rather a complement to the domestic operations. The result of dominating national transmission expansion is a useful counterpart to the results of the aggregated, one node per country, NTC model which finds a rather high cross-border investment: It shows that domestic grid upgrades and cross-border interconnector expansion should go hand in hand. An additional NTC model participated in EMF 28: LIMES-EUþ. This team follows a different approach in the evaluation of the 80% emissions reduction scenario for the electricity system (80%DEF): Acknowledging that the future development of

10 F. Holz & C. von Hirschhausen Figure 4. LIMES-EUþ80%DEF scenario (assuming moderate renewable investment costs and integration with MENA): Cumulative NTC capacity expansion until 2050 assuming a low (high) NTC expansion rate (i.e., 0.25 (1) GW per country-connection and year) in the left (right) panel. specific investment costs for variable renewable technologies is highly uncertain, Schmid and Knopf (2013) apply the model to quantify the economic benefits of electricity grid integration under various conceivable developments paths of renewable investment costs from pessimistic over moderate to optimistic, based on the literature survey provided by Schröder et al. (2013b). 3 In addition, the degree of European electricity system integration is varied through restrictions on annual cross-border NTCs. Their scenario definitions lead to a regional or integrated system development, depending on the restrictions on the expansion of the NTCs and the possibility to install Desertec-type connections between the ENTSO-E and the North African/ Middle East (MENA) region. The scenario results indicate that NTC expansion is not performed uniformly across Europe (Fig. 4). If larger expansions of the NTCs are allowed, this opportunity will be particularly exploited in Northern Europe, connecting high-yield wind sites to the demand regions. The option to import electricity from the MENA regions is not exploited to the extent technically possible, indicating that Northern and Central Europe remain the core regions of an integrated European electricity market. Since EMPS and ELMOD also show a similar focus of the investments on Northern and Central Europe, this result seems robust. It is due to the concentration of demand in the region which is served by regional renewables by as much as the potential allows for it instead of importing over very long distances. It indicates a cost and efficiency disadvantage of very-long-distance transmission over domestic renewable generation in Northern and Central Europe. However, LIMES-EUþ obtains a visibly less meshed electricity network than in EMPS, though a very similar approach is used. 3 The LIMES-EUþ team did not submit results of other scenarios than the 80%DEF scenario

11 The Infrastructure Implications of the Energy Transformation in Europe Until 2050 However, the effect of transmission expansion is relatively modest with respect to reducing total discounted system costs (3 6%). But at the same time, the LIMES-EUþ team finds that CO 2 prices required to enforce mitigation can be lower by up to 40% if decarbonization is accompanied by extensive NTC expansion. Moreover, average electricity prices in the ENTSO-E region decrease by up to 5 8% and the standard deviation of electricity prices decreases by 20 30% by facilitating the cross-border balancing of temporal and spatial differences in renewable generation. Given that NTC investments amount to only 1 2% of total discounted system costs across scenarios and acknowledging that NTC expansions are economically beneficial, Schmid and Knopf (2013) conclude that NTC investments are a no-regret option. In other words, if NTCs can be expanded, than they will, because these investments ultimately reduce system costs. However, the impact of differences in renewable investment cost reductions is stronger than the effect of NTC expansion in all cases. To summarize, the three electricity network models that participated in the EMF 28 Subgroup Infrastructure focus on different aspects of electricity transmission expansion, and analyze different levels of spatial and technological detail. 4 In their modeling methodology they are similar in that respect that they all use a cost minimization approach and do not include market power considerations. They show that for a given decarbonization target and path (as prescribed by input from PRIMES), different technology utilization and network investment patterns can emerge, depending on the cost development and the interconnection possibilities that are assumed. EMPS and LIMES-EUþ make strong (but common) simplifying assumptions by modeling each country as a one node and linking these nodes with NTCs. This approach does not take into account the physical properties of electricity, namely its resistance-determined flows in a meshed network, and it models electricity as any other commodity (e.g., natural gas). The ELMOD model, in contrast, uses a DC load flow approach and while this is still a simplification of the AC network, it allows including the intra-country flows and detecting real-world network bottlenecks. Indeed, ELMOD results differ from EMPS and LIMES-EUþ results by their smaller crossborder investment needs but also by their smaller total investments in the grid. Hence, the results of the NTC models must be carefully interpreted as they do not necessarily reflect the investment needs in the total system. Interestingly, the EMPS and LIMES-EUþ models yield quite similar amounts of investments in transmission capacities: EMPS finds approx. 83 GW expansion of the NTCs by 2020, compared to 64 GW estimated by the TYNDP 2010 (Bakken et al., 2013). By 2050, they find between 200 GW expansion (40%DEF scenario) and 360 GW (80%DEF), respectively. These and the numbers of the LIMES-EUþ model must be compared to approximately 40 GW of NTC capacity in Europe today (Schmid 4 Another model focusing on electricity, the EMELIE-ESY model (DIW Berlin) participated in the EMF 28 model comparison. However, it concentrates on generation technology questions and did not base its EMF 28 runs on PRIMES input (see Schröder et al., 2013a)

12 F. Holz & C. von Hirschhausen and Knopf, 2013). LIMES-EUþ finds up to 400 GW of expansion in the most optimistic scenario on RES cost development and network expansion. ELMOD results are less generous and indicate investment costs up to 56 bn. up to 2050 that compare to 104 bn. computed by the European TYNDP up to 2022 (ENTSO-E, 2012, p. 70). The difference is to a certain extent due to the lower investment in HVDC lines (up to 7000 km in ELMOD until 2050, compared to 12,600 km in the TYNDP until 2022), but also to less AC network expansion in the base case (approximately 24,000 km in ELMOD s 40%DEF and 44,000 km in 80% EFF until 2050, compared to 39,000 km in the TYNDP until 2022). Evidently, a costefficient dispatch in the European network as it is assumed in the ELMOD model can make costly network expansion dispensable. A common result, however, is that investment needs are larger with more ambitious climate targets, to accommodate more renewables and replace more fossil generation. Hence, there is a need to develop the North Sea region s grid, as highlighted by all three models. More generally, North South seems to be the dominant direction of expansion plans. On the contrary, the potential contribution of external regions, e.g., in North Africa, to EU electricity supply is assessed differently by the models. There is some uncertainty on the absolute expenditure figures for transmission expansion, also in comparison to the TYNDP, though overall the ranges given by the model results are somewhat similar. Moreover, the effect of transmission expansion is modest relative to total discounted system costs, but a levelizing (volatility-reducing) and decreasing effect on electricity (and CO 2 ) prices is generally found. RES costs play a larger role in bringing the system costs down, and should be another focus of support policies to decarbonization. 3. Natural Gas Import Infrastructure Natural gas is often considered to play a major role in the low-carbon energy transformation in Europe; hence, natural gas, too, is attributed a strategic role to become a key for the energy future of Europe. 5 Due to its high flexibility, its versatility, as well as the diversified supply sources, natural gas is considered to be an ideal partner for variable renewables. Hence, the European Commission and others have agreed to develop a pan-european network development plan, similar to the electricity sector, to respond to the perceived need for more infrastructure. At least 70 bn. are expected to be invested in pipelines, LNG-terminals, and the necessary connecting infrastructure until 2020 (EC, 2010; ENTSO-G, 2013). However, the role of natural gas in the Energy Roadmap (EC, 2011) and in the scenarios of the EMF 28 model comparison somewhat contradicts the plans for natural gas in the European institutions. Indeed, natural gas consumption in the reference 5 Speech of Energy Commissioner G. Oettinger at the 10th Gas Infrastructure Europe Annual Conference in Krakow, Poland, May 24, 2012, quoted by the GIE article available at Accessed on January 23,

13 The Infrastructure Implications of the Energy Transformation in Europe Until 2050 Figure 5. Natural gas consumption levels in GGM for EU-27 by scenario (in EJ/y). scenario (40%DEF) is expected to decrease from about 18 EJ (2010) to below 15 EJ (2050) in the EU-27. Consequently, little additional infrastructure development may be required until 2050 in high-pressure pipelines and LNG import terminals. Depending on the scenario, the decline can even become more pronounced (see Fig. 5), primarily caused by the reduction of consumption by industrial and household consumers due to substantial efficiency efforts. 6 This reflects the PRIMES model s Energy Roadmap results in these scenarios and seems to be due to the larger need for natural gas-fired electricity generation to replace coal in the electricity system (scenario with no/delayed CCTS) or the higher need for flexible back up (high efficiency scenario). 7 Given the decreasing demand for natural gas, infrastructure implications in this sector are predominantly given by the regional reallocation between domestic production and imports. A major factor affecting the need for new infrastructure in and to Europe is the parallel growing demand for natural gas in the rest of the world, in particular in the Asia-Pacific region: When Asian demand draws LNG and pipeline imports from Russia, Europe has to find new sources (especially in Africa and the Caspian region) and needs to develop the adequate import infrastructure. 8 This section reports on three modeling efforts, two of them being unique in this EMF model comparison: The Combining model looks at two and not only one sector by linking the natural gas to the electricity sector and allows for a more detailed 6 The only exception is the no-policy BASE scenario which is designed as counterfactual by assuming away any previous climate policy or the targets. 7 Given the unclear picture of the future role of natural gas, and leaning on other projections than the PRIMES/Energy Roadmap, the GGM team derived additional own scenario sets where natural gas is either a back-up fuel, running in parallel to the renewables and not being phased out, or a bridge -scenario, where natural gas only serves to bridge a strong growth of renewables in the s and is then phased out. Both scenarios have different patterns of natural gas production and consumption than the EMF scenarios, and, consequently, different infrastructure implications (cf. Holz et al., 2013). 8 It should be noted that in the scenarios considered by the subgroup infrastructure the rest of the world outside Europe does not undertake additional climate policies, which means that countries outside Europe are assumed to follow a Muddling-Through strategy without international cooperation in climate policies and without international carbon trading. See De Cian et al. (2013) for the analysis of the international implications of EMF 28 results

14 F. Holz & C. von Hirschhausen view at the utilization of natural gas in power generation (Abrell et al., 2013); the RAMONA model is used to analyze uncertainty on the demand side in a stochastic model setup (Fodstad et al., 2013). Indeed, the optimization results of the market players can be very different when they react to a deterministic realization of the variables in the future periods (as in all other models in this EMF 28 group) than when they react to stochastic realizations. A TSO, for example, might decide to invest in more capacity than the average flow volume on its pipeline (if this is the value given by the deterministic model) in order to be able to also accommodate the peak flow (which may be realized only with a small probability). The RAMONA team investigates whether the infrastructure investments differ when future demand for natural gas is uncertain. Two natural gas-only models participated in the EMF 28 model comparison: The Global Gas Model (GGM) and RAMONA. The results of both models are relatively comparable because both teams used similar data sets and geographic disaggregation for EMF 28. However, the actual model setups differ in the way that the natural gas value chain is described: While GGM is a complementarity (equilibrium) model considering the market behavior of competing players along the value chain, RAMONA is a multistage (stochastic) optimization model where social welfare is maximized. The RAMONA team assumes a deterministic demand development until 2020 with data realizations corresponding to the average of the PRIMES results of all scenarios. From 2025 onwards each scenario s specific reference demand is implemented. For the model s time horizon this results in a scenario tree with 51 nodes. Both models include endogenous investment decisions in expanding the natural gas transportation infrastructure (i.e., pipelines, LNG terminals). They take data for production capacities and reference consumption for the EMF 28 scenarios from PRIMES results for the same scenarios and years. Data is included for each EU-27 country and most other European countries at the individual level and aggregate data for the other world regions (Russia, Caspian, Middle East, North America, South America, Africa, and Asia-Pacific). This allows for a detailed analysis of the trade flows and infrastructure requirements to Europe and between European countries. However, it neglects intra-country flows and network bottlenecks. For the EU-27, Fig. 6 shows a strong increase of import dependency due to the drastic drop of domestic production (to 1.3 EJ/y. in 2050, from 6.6. EJ in 2010); this figure reports GGM results, but RAMONA results are similar. The role of LNG trade, too, diminishes after a peak in Thus, the rise in import dependency (from 65% to 90%) is mainly accompanied by increasing pipeline imports. The aggregate EU-27 numbers hide a large heterogeneity: While most countries follow the general trend of consumption reduction, some others actually increase their natural gas consumption until In the GGM, the strongest decrease in natural gas consumption takes place in Germany ( 27% between 2010 and 2050), the UK ( 35%), the Netherlands ( 41%) and France ( 35%); while Greece (þ126%), Spain (þ30%) and Bulgaria (þ26%) increase their natural gas consumption in a shift away from coal

15 The Infrastructure Implications of the Energy Transformation in Europe Until 2050 in EJ per year % 90% 85% 80% 75% 70% 65% 60% 55% 50% Produc on Consump on Import Dependency (right axis) Figure 6. Natural gas production, consumption, and import dependency in the GGM reference scenario 40%DEF (in EJ/y and percentages.) With respect to infrastructure, the implications of scenario 40%DEF are modest (Fig. 7). Most expansions take place until 2020 and are already started and/or prescribed by the European planning (ENTSO-G, 2013). In both the GGM and the RAMONA results, there are only small additions to the existing LNG import (regasification) capacities and several pipeline expansions decided in 2010 (i.e., added exogenously in the models). The later endogenous pipeline expansions serve primarily to accommodate a shift of import flows, away from Russia and LNG (that have to serve the Asian market) to imports from North Africa and the Caspian region. The pipeline expansions take place along these import routes: From Africa to Spain and Italy and further on to France, respectively to Austria and Germany; and from the Figure 7. Cumulative expansions in and to Europe until 2050 in the GGM

16 F. Holz & C. von Hirschhausen Caspian region via the new White Stream pipeline to Romania and further on to other South East European countries. In contrast to the expansions from the South, there seems to be no need to expand the pipeline infrastructure from Scandinavia (Norway) to continental Europe. In total, the GGM points to an investment bill of around 25 bn.. Until More than 65% arise before 2020, more than 94% before There is little variation of the investment needs between the EMF decarbonization scenarios due to the similar demand development predicted by PRIMES. Very similarly, the RAMONA model finds as main trends in the global market that the largest pipeline investments will be directed towards Asia, and that there is a trend of a larger gas supply from Africa to Europe (Fig. 8). Moreover, both models show that some reverse flow capacities will be installed in South-East Europe, i.e., pipeline infrastructure opposite the traditional East West direction that was from Russia westwards. In addition to the new import sources, the reverse flow capacities contribute to the diversification of imports in South East Europe and, thus, increase the security of natural gas supply of these countries. South-East Europe indeed is hardly diversified from Russian exports today. Peculiarly, the GGM suggests to build two new pipelines across the Black Sea: The White Stream pipeline (Caspian Romania) in addition to the South Stream pipeline (Russia Bulgaria, started in 2012 and hence exogenously added with an initial capacity available from 2015 on) and the Blue Stream pipeline (Russia Turkey, online since 2005). While RAMONA includes the two exogenous pipelines, it does not forecast the White Stream pipeline. The difference may be due to different assumptions of the Figure 8. Cumulative expansions in and to Europe until 2050 in the RAMONA gas model (expected value model)

17 The Infrastructure Implications of the Energy Transformation in Europe Until 2050 (relative) costs of production and operation of the pipeline. In particular, the detailed technical representation of pipeline costs and operations in the RAMONA model may exclude the additional offshore (and, hence, expensive) pipeline in the Black Sea. The Value of Stochastic Solution (VSS) in the RAMONA model, i.e., the additional benefit from taking into account uncertainty compared to the average of deterministic model runs, is very small. This reflects that the deviations between the PRIMES-based EMF 28 scenarios are small. It can be concluded that the option value of postponing decisions on pipeline development in Europe until 2020 is very limited if one invariantly expects a decrease of natural gas consumption as suggested by the Energy Roadmap (EC, 2011) scenarios. The GGM and the RAMONA model focus solely on the natural gas sector, taking the demand developments suggested by the PRIMES results as input parameters. However, the different consumption sectors of natural gas are not clearly distinguished, which makes it hard to disentangle the demand reduction effect in the electricity sector from effects in other sectors (industry, residential space heating, or transportation). Yet, one could expect a stronger demand reduction from the electricity sector due to the emissions disadvantage of the fossil fuel natural gas compared to renewables and due to the cost disadvantage of natural gas with Carbon Capture, Transport, and Storage (CCTS) compared to coal with CCTS. To better take into account the interaction of the natural gas and electricity sector, additional results were produced in the EMF 28 model comparison by an integrated network model combining natural gas and electricity networks (Abrell et al., 2013). This modeling effort follows the observation that the interactions between electricity and natural gas infrastructure are becoming more intense with an increasing share of intermittent renewables for which flexible gas power plants can provide back-up capacity. The equilibrium model by Abrell and Weigt (2012) consists of a natural gas and an electricity module which both take into account the respective transmission grid and can be run independently one of another or in a combined manner. The natural gas module includes pipelines and LNG routes, similarly to the natural gas sector models. The electricity model includes the transmission grid using a DC load flow approach and a market hub system, comparable to ELMOD but with aggregated zones instead of a nodal disaggregation. As long as the electricity model is used independently of the natural gas model, all fuel prices are exogenous. The link between the electricity module and the natural gas module is that the demand of natural gas fired power plants is endogenized in the natural gas model; consequently, the natural gas price becomes endogenous in the combined model. The model is run for three scenarios (40% and 80%DEF, 80%GREEN) in a time static approach for a reference hour of each of the periods 2010, 2020, 2030, 2040, and In contrast to the previous models, it neglects the global gas market interactions by focusing on the European demand and it assumes perfectly competitive markets without any market power

18 F. Holz & C. von Hirschhausen The model results for the natural gas sector suggest, somewhat like the previous models, that under EMF 28 demand conditions network congestion is only a minor issue if the currently planned transmission pipeline and LNG projects come online in the next decade. The import pattern in the European natural gas market remains similar to the current observed setting with a high dependency on Russian and African gas that can be reduced by increases in energy efficiency and renewable generation. The production pattern in electricity depends on the emission price and RES capacities and shows a general shift first from coal towards gas and then from gas towards RES. The use of natural gas for electricity generation differs over the decades and the scenarios. In all cases the highest gas share is obtained in This is a result of a low natural gas price coupled with the emission price that reduces the comparative advantage of coal plants while at the same time RES capacities are not yet sufficient to fully cover the difference. From there on, electricity generation from natural gas is basically phased out in all scenarios in In the 40%DEF case the RES share increases the slowest and in 2050 some brown coal plants are still needed to satisfy electricity demand. This is based on the price spread between natural gas and lignite that is sufficiently high to compensate the emission price, thereby rendering lignite more competitive than gas plants. In both default scenarios (40% and 80%), RES ensure the bulk of electricity provision until 2050, with nuclear as second utilized fuel. This is only partly a result of the assumed increase in RES capacities. More importantly, the simultaneously strongly increasing emission price renders fossil generation uncompetitive and phases it out. In the gas market, the development until 2050 reflects the electricity sector developments as the share of demand by power plants is steadily reduced until The endogenous supply within Europe declines to about 70% of the 2010 values over the decades. In the 40%DEF case the relatively stable gas demand is satisfied by increasing pipeline imports from Russia and the Caspian region while LNG imports decline. In the 80%DEF scenario the gas demand declines to about 80% of its 2010 values till 2050 which also leads to a different supply pattern. As demand in Central Europe goes down, imports from Russia can also be reduced, whereas the demand level in South Europe remains relatively stable, maintaining the need for LNG imports. This development is accelerated in the 80%GREEN case leading to a sharp reduction of imports both via LNG and pipeline till 2050 while the demand level is reduced to about 60% of the 2010 values. These developments have an impact on the import dependence of Europe: whereas the reference 40%DEF case has an import share of about 70% in 2050, the 80%DEF has a share of about 64% and the 80%GREEN scenario a share of about 51%. Thus, the higher environmental targets lead to less import dependency due to an overall reduction of gas demand. Natural gas infrastructure is analyzed by two natural gas-only models on the one hand and a combined natural gas-electricity model on the other hand. While the models of the natural gas sector alone have a global coverage, the Combined model focuses on Europe and the relevant suppliers. All three models apply the EMF

19 The Infrastructure Implications of the Energy Transformation in Europe Until 2050 decarbonization scenarios based on the PRIMES natural gas results of the respective scenarios. PRIMES finds a decreasing trend in natural gas consumption in Europe in all decarbonization scenarios. The natural gas-only models (GGM, RAMONA) show that the European Union with its climate policy will be in competition with Asia for Russian and LNG supplies. Hence, the traditional supply picture, in which Russia plays an important role may not be sustained and new infrastructure is needed to accommodate the imports from new suppliers from the Caspian and the North African region. This takes place despite the large pipeline investments from Russia in the last years (Nordstream, South Stream), but is similar to the recent list of Projects of Common Interest published by the European Commission (EC, 2013a). There is little variation when introducing uncertainty on demand, as done by the RAMONA team. In contrast to the gas-only models, the Combined model assumes smaller changes to the supply structure. All models find support for some small, but decisive investments in reverse-flow capacities within the European network. These reverse-flow capacities would go opposite the traditional East West direction, particularly in East Europe. Hence, they will increase the supply diversification and supply security in East Europe by reducing the dependence from Russia. All models invariantly find little support for shale gas production in Europe. Regrettably, all models here did not model the intra-national high-pressure or lowpressure pipeline grid which is, however, the spatial level at which bottlenecks have been reported in the last years. For example, the German energy market in early 2012 suffered from a lack of sufficient pipeline capacities to bring natural gas from the border connection points to the power plants in the South of the country (FNB, 2012). Hence, models with a more refined level of detail are needed to investigate the complete natural gas infrastructure chain. 4. Carbon Capture, Transport and Storage Pipeline Infrastructure Large scale CO 2 abatement by Carbon Capture, Transport, and Storage (CCTS) utilization in Europe is assumed in all major top down models starting from 2020 onwards. In a recent communication, the European Commission (EC, 2013b) reiterated the potential contribution of CCTS for carbon abatement. In the EMF scenarios 80% DEF and 80%GREEN, CCTS is expected to contribute with up to 15% to European electricity generation in the 80% scenarios (Knopf et al., 2013). In this section, we report on the results of the CCTSMOD model (Oei and Mendelevitch, 2013)) that was applied to all those EMF 28 scenarios that include the option of the CCTS-technology (40% and 80%DEF, 40% and 80%EFF). 9 The model team took the CO 2 price paths of the PRIMES results of these scenarios as input to their model runs where the certificate 9 The 40%DEF and 40%EFF scenarios were analyzed jointly as there is nearly no difference between their outcomes due to the similar CO 2 price development

20 F. Holz & C. von Hirschhausen price induces investments in both the capture and the transportation infrastructure. The CO 2 prices go from around 15 /t CO 2 in 2010 to 52 /t CO 2 in 2050 in the 40% scenarios, and to more than 250 /t CO 2 in the 80% scenarios. The authors add an important technical feature by assuming that Carbon Capture, Transport, Usage, and Storage (CCTUS) might be an option for applying this technology in the near future. Such a combination with Enhanced Oil Recovery (EOR) would lead to additional revenues from the additionally extracted oil thanks to increased oil production. Regional scenarios for the riparian countries of the North Sea including the option of EOR have been modeled for all EMF 28 scenarios with CCTS and are analyzed independently from the basic EMF scenarios. The model calculates an optimal (i.e., cost-minimal) investment in capture facilities and a pipeline network linking CO 2 producers to potential EOR and storage sites, with the time horizon of Table 4 and Fig. 9 show the main results of the European-wide EMF 28 scenarios with respect to CO 2 captured (from industry and the energy sector), remaining storage sites, and CCTS investment costs. In the 40% scenarios, only some iron and steel factories in Norway and Estonia invest in the capture technology as they have the lowest variable and fixed costs. The location of the investing plants is directly on the shore, or linked to it, which leads to lower transport costs than for other industrial facilities. CCTS is not considered as abatement option for power plants due to CO 2 prices hardly rising above 50 /t CO 2. CCTS deployment in the 80% scenarios starts when passing beyond the 50 /t threshold, which happens in the year 2030 (80%DEF) or 2035 (80%EFF). The iron and steel sector is again the first mover and cement works start to capture CO 2 five years later. At that point a certificate price of 75 /t CO 2 is reached. CCTS only becomes economically interesting for power plants and refineries when the price exceeds 100 /t CO 2 which is in the year The annual captured emissions in 2045 are above 1 billion t CO 2 in both scenarios and then start to decrease again due to the phase out of several older fossil power plants. Table 4. Summary of the results of CCTSMOD of the European-wide EMF scenarios. European-Wide Scenario Share of Stored Industry Emissions [%] Pipeline Network [1000 km] Stored Emissions Until [GtCO 2 ] Storage Left in 2050 [GtCO 2 ] CCTS Invest. Costs [bn. ] CCTS Var. Vosts [bn. ] Reference (40% DEF, EFF) Mitigation 1 80%DEF Mitigation 1 80%EFF

21 The Infrastructure Implications of the Energy Transformation in Europe Until 2050 Source: Oei and Mendelevitch (2013). Figure 9. Captured CO 2 emissions over time in the 80%DEF scenario in CCTSMOD. The 80% scenarios assume a CO 2 certificate price above 250 /t in the year Investing into the CCTS technology is then cost-efficient for all emitters. A CO 2 pipeline network of 44,800 km (80%DEF) and 33,700 km (80%EFF), respectively, all across Europe would be installed, with overall system costs of 800 1,000 bn.. How do these results relate to the top down models in the EMF 28 model comparison? In fact, for the 80%DEF and 80%EFF scenarios, there is not a big gap between the results of CCTSMOD and most top down models. Due to efficiency gains assumed in the EFF scenarios, less ambitious CCTS deployment is required to achieve the 40% goal, thereby resulting in lower annual storage rates and lower penetration rates. But in all models of the EMF 28 exercise, CCTS utilization significantly increases to achieve the 80% reduction goal. The spread of penetration rates between 54% and 99% can again be explained by the different assumptions on capital costs and different CO 2 prices resulting from the exogenous GHG cap. While some of the models are optimization models (e.g., WITCH, TIMES PAN-EU, CCTSMOD) that can produce extreme results as optimal solutions, other models use an equilibrium approach (e.g., POLES, PRIMES) employing elasticities driving the model towards more balanced solutions. Different European countries have shown different degrees of enthusiasm about the development of CCTS, and certainly the North Sea riparian countries have most actively pursued pilot projects, such as the UK, the Netherlands, and Norway. It therefore seems worth the while to model a regional deployment of CCTS in the North Sea region, and to calculate to what extent the usage of CCTS may also be linked to the

22 F. Holz & C. von Hirschhausen technology of EOR. Model results with CCTSMOD suggest that, similar to the basic EMF scenarios, the use of CCTS is mainly economical for the industrial sector, particularly iron and steel plants. These facilities invest in a CCTS infrastructure from 2015 to 2020 and benefit from EOR profits from 2025 onward in all four EMF 28 scenarios. Around 100 Mt CO 2 are stored annually until the full exploitation of the EOR fields ten to fifteen years later. When EOR capacity is exhausted, from 2035 onwards, additional storage sites in saline aquifers and hydrocarbon fields closer to the shore are used by the same industrial capturing sites. With certificate prices exceeding 75 /t CO 2 in the 80% scenarios, more distanced industrial facilities also start to run capturing units. Power plants only start the CCTS process from 2040 onwards, similar to the outcome of the previous scenarios without the EOR option. Figure 10 and Table 5 summarize the results from the modeling exercise for the 40% Reference scenarios and the Mitigation 1 (80%) scenarios for the North Sea region. Figure 10 shows a regional cluster of CO 2 pipelines to the North Sea from the riparian countries. CCTS is, however, mostly interesting for CO 2 sources close to the storage sites, while more distant emitters do not invest in the technology (e.g., in Poland and East Germany). The total stored emissions and pipeline network in Table 5 Source: Oei and Mendelevitch (2013). Figure 10. CO 2 Infrastructure in Reference EOR Scenarios in 2050 in CCTSMOD