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Grid vs. storage in a 100% renewable Europe
Renewable Energy 50 (2013) 826e832
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Renewable Energy journal homepage: www.elsevier.com/locate/renene
Grid vs. storage in a 100% renewable Europe Florian Steinke a, *, Philipp Wolfrum a, Clemens Hoffmann b a b
Siemens Corporate Technology, Otto-Hahn-Ring 6, Munich 81739, Germany Siemens Infrastructure & Cities, Erlangen, Germany
a r t i c l e i n f o
a b s t r a c t
Article history: Received 23 January 2012 Accepted 31 July 2012 Available online 13 September 2012
Intermittent renewable power production from wind and sun requires significant backup generation to cover the power demand at all times. This holds even if wind and sun produce on average 100% of the required energy. Backup generation can be reduced through storage e averaging in time e and/or grid extensions e averaging in space. This report examines the interplay of these technologies with respect to the reduction of required backup energy. We systematically explore a wide parameter space of combinations of both technologies. Our simple, yet informative approach quantifies the backup energy demand for each scenario. We also estimate the resulting total system costs which allow us to discuss costoptimal system designs. 2012 Elsevier Ltd. All rights reserved.
Keywords: Intermittency Renewable energy Grid expansions Storage
1. Introduction Renewable power generation is growing quickly in Europe. In 2006, the EU defined its 20-20-20 goals calling for 20% renewables in 2020 [1]. Today, already 40% of renewables seem a realistic target for the end of this decade, at least in Germany with its strong photovoltaics (PV) sector [2]. Scenarios with close to 100% renewables are now typically dated for 2050, e.g. [3,4], but they may possibly arrive much earlier. Yet there is still considerable uncertainty about what a 100% renewable Europe would look like. The largest renewable contributions will probably come from wind turbines and PV, since water, biomass and waste resources are limited. Due to their intermittent nature, significant backup generation is needed for power from wind and sun, even if they cover on average 100% of the demand. This is depicted with an exemplary time line in Fig. 1. Regarding the required backup power, one can see from Fig. 1 that almost the whole demand has to be covered from backup sources alone at certain times. Thus the installed backup capacity should roughly match the peak demand. This is in line with [5] where the capacity credit of wind is rated at only 16%, assuming a well-connected Europe. The interesting question about the backup system is thus rather how much energy the backup power plants have to provide e and how this depends on grid extensions and novel storage capacities. Grids and storage help to reduce backup energy demand by averaging intermittent power generation in space and time. The * Corresponding author. Tel.: þ49 89 636 41519; fax: þ49 89 636 49767. E-mail address: [email protected] (F. Steinke). 0960-1481/$ e see front matter 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.renene.2012.07.044
interplay of these technologies and their effect on the remaining backup energy demand is the central focus of this paper. Backup energy demand is critical in two respects. First, backup energy would have to come from renewable sources in a fully renewable scenario. The most important source of renewable backup energy is biomass, since only a minimal fraction of Europe’s water energy is dispatchable. The yearly energy potential of biomass, however, is limited to roughly 10% of the average power consumption [6]. It could thus be asked under which constellation of generation, grids and storage a fully renewable power system in Europe would be possible at all. For other constellations the backup energy is directly related to remaining CO2 emissions of the system, since additional backup energy would have to come from fossil sources. Such not fully renewable systems might be acceptable even in the long run, if the financial benefits are significant. The backup energy is thus also an important measure for determining the optimal trade-off between CO2 emission reduction targets and economic feasibility. In this paper we use scenarios for Europe where wind and sun, the variable renewable energies (VRE), produce on average 100% or more of the required electric energy. We employ a simple model to quantify the backup energy demand for a wide range of choices of grid and storage. While there are many ways of modelling the different power system components in much greater detail, we think it is important to reduce the problem to the most basic characteristics to maintain an overview and obtain robust order of magnitude results. We also determine optimal system designs. First, optimality means minimising the use of backup energy, a physics perspective. Second, we estimate the financial consequences of each scenario to determine also economically optimal designs.
F. Steinke et al. / Renewable Energy 50 (2013) 826e832 Typical Winter Week PV Wind Backup Demand
Fig. 1. Time course of generation and demand of one model cell in a typical winter week. Wind and PV produce on average 100% of the demand of the year, but significant backup is still needed. At certain points in time the backup has to cover the complete demand alone.
Fully renewable energy systems have recently been analysed in various ways. (See Connolly et al. [7] for an extended list of tools for this purpose.) Seven different technologies for integrating renewable energies into the Danish power system are examined in [8], hydrogen storage cycles for different islands in [9], and compressed air storage and CHP plants in [10]. These studies use fixed grid assumptions that permit the estimation of a single time series for the wind and PV feed-in which in turn determines the optimal operation of dispatchable integration technologies. In contrast, we examine in this paper how fluctuations of renewables can be reduced by gradually increasing grid installations. Our work is in this respect similar to the TradeWind study [5] which describes the effects of European transmission grid extensions on averaging out the fluctuations of wind power. We extend their approach by introducing a second dimension for reducing variations, namely storage. The interplay between the two technologies, transmission and storage, is challenging since both technologies can replace each other to some degree. A systematic study of this interdependence, varying the two degrees of freedom separately over a wide range of values, is the main new contribution of this paper. Another two-dimensional parametric study has been undertaken by [11] determining the optimal mix between wind and PV installations at different levels of renewable energy supply. From their work, we obtain the normalised generation time series for wind and PV and the optimal renewable mix of 65% wind and 35% PV in the average yearly energy production in a 100% renewable power system. Along the same dimensions, cost-optimal grid extensions in Europe are balanced against flexible power plants in [12]. Chowdhury et al. [13] describe technical requirements of grid and storage technologies for renewable integration. In Section 2, we present our model and the results for computing the backup energy demand in dependence on grid and storage capacities. In Section 3, we extend the modelling to estimate the economic consequences of different storage/grid choices. This requires more assumptions than the first model, which is why we have separated this part into a separate section. In Section 4, we discuss the obtained results and give an outlook on possible directions of future research. 2. Physical modelling e specifying backup energy demand 2.1. Data and model Our analysis builds on detailed time series characterising the temporal and spatial distribution of renewable energy production
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from wind and sun. European weather model data [14] was used to compute capability factors for wind and PV in hourly resolution for an 8 year period [11]. The spatial resolution of this data is 50 by 50 km and covers all of Europe. To estimate the amount of energy produced from this, plausible assumptions about the installed capacities for wind and PV are required. One way to obtain these would be to set them proportional to the regional full load hours for each generation type, that is, based on economics. However, this is not what is commonly observed today. Installations are rather determined by political support schemes. We thus use EU’s published 2020 targets [15] and scale them up such that wind and sun produce on average 100% of the required power. Within large countries, an internal distribution is assumed based on regions that are roughly aligned with the grid control areas. Within each region, capacities were distributed uniformly. The scaling of the installation capacities was performed separately for wind and sun. We set the yearly energy production of wind to 65% of consumption and that of PV to 35%. This weighting between the two intermittent sources was found optimal for reducing the distance to the load by [11]. The demand time series were based on entsoe data.1 Total consumption was assumed to remain constant at 2007 levels. In sum, this procedure yielded an 8 year time series for wind, sun and demand in each of the 50 by 50 km cells in Europe. To determine the backup energy demand and its dependence on grid and storage we then took the following simple approach: we assumed circular regions with radius R and an attached storage facility with energy reservoir of size C. The grid within the region was assumed to be strong enough to render it a copper plate, i.e. that no grid limitations are considered in this region. This simple procedure allowed us to characterise the grid and storage setup with single figures. To gain independence from regional specialities, we repeated all calculations independently for 16 distributed centre points of the circles (see Fig. 2) and averaged the results. In each circular region the time series of all contained weather cells were aggregated. The aggregated renewable generation time series was scaled a second time to obtain a local 100% scenario. Wind and sun were scaled with the same factor so that different regional shares of wind or PV persisted. For example, in Bavaria the regional renewable mix is dominated by PV, whereas in areas close the North Sea, wind plays the major role. We computed an optimal scheduling of the storage facility in each region to minimise the required backup energy, assuming that each region is equipped with a backup power plant of sufficient power. Storage levels at the beginning of the modelling period were constrained to match the storage levels at the end of the interval. Grids and storage were assumed to cause no losses. The resulting scheduling problem (for details see Appendix A) was solved with the GAMS/CPLEX optimisation package. In sum, we obtained the optimal cumulative backup energy demand for each circular region as a function of R and C. 2.2. Results The backup energy demand in relation to grid and storage is shown in Figs. 3 and 4. In Fig. 3(a), backup energy demand depending on region size R is examined, assuming no storage. It can be seen that the median backup energy need in a 100% renewable Europe is roughly 40% of the power consumption without grid or storage extensions. This number is significant, well beyond the potential of biomass. If one considers the distribution for different centre points, one observes
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Fig. 2. Assumed wind and solar capacities on the 50 by 50 km raster determined by the underlying weather model. The black stars mark the centres of different, circular model regions.
that solar-dominated regions have an even higher backup demand. This is not surprising since PV production is limited to daytime only. As specific values for R we consider the following choices: 25 km, corresponding to one cell of the weather model, 100 km, a state or province level, 500 km, a roughly national level, and 3000 km, the all-encompassing European level. For the maximally extended grid, the backup energy demand is reduced to 19%, still large in comparison to the assumed 10% biomass potential. For intermediate radii R, a gradual reduction of backup demand on a logarithmic scale is observed, without significant steps. In Fig. 3(b), we vary the second degree of freedom, namely storage size C, while keeping the grid fixed at the minimal level determined by the spatial resolution of the weather data. Storage capacity is measured by the time the storage facility could cover the average regional power demand. One can see that long-term storage reduces the backup energy demand to zero. This is not
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surprising, since by definition we have a regional 100% scenario, i.e. in the long time average no backup is needed. This infinite storage assumption can practically already be achieved with 90 days of storage capacity for all but a few high-wind regions. If one assumes 10% of available renewable backup, then a 100% renewable Europe can be achieved with between 7 and 30 days storage. It is interesting to note that while the median backup demand for all regions decreases relatively linearly with storage size C on a log-like scale, one can observe a big step for PV dominated regions. Between 4 and 12 h storage capacity, their backup demand drops significantly. This seems plausible since 12 h of storage are just enough to equal out the dayenight rhythms of solar energy production. A storage capacity of 12 h corresponds in absolute numbers to 1.4 kWh per installed kW of PV, if a 100% PV region with 1000 full load hours per year is assumed. Since today’s pumped water storage capacities are below 1 h of average demand, no
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Fig. 3. Reduction of backup energy with (a) grid extensions (no storage) or (b) storage capacity (no grid). In the upper figures, the central mark is the median, the edges of the box are the 25th and 75th percentiles, the whiskers extend to the most extreme data points not considered outliers, and outliers are plotted individually. In the second row of figures below, the same backup requirements are shown for three exemplary regions with differing shares of PV in their local renewable energy mix.
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significant reduction of backup energy demand can be achieved with these. The initial findings on backup energy demand for scenarios with only storage or only grid extensions are extended in Fig. 4 where mixtures of the two averaging mechanisms are examined. There, it can be seen, for example, that 1 h storage with a full European grid is roughly equal to one day of storage in a national grid, or seven days of storage on a 50 km level. As an alternative to extending grid or storage, one could also install more renewable capacities than those that are needed to produce 100% of the yearly power consumption. One such case is shown in Fig. 4 (right) where an installation of 130% is assumed. Compared with the 100% scenario, similar backup energy demand results for grid or storage extensions that are one step smaller. The backup energy demand, however, remains generally quite significant, e.g. more than 30% backup is required for the no grid/no storage scenario. 3. Economic modelling e estimating total system costs Additional to the above physical modelling, we now consider the economic implications of the scenarios. An important goal is to identify cost-optimal system configurations. Note that economic modelling requires assumptions about future price developments. These are potentially less reliable than the rather robust analysis of past weather data that was used so far. The calculations are thus somewhat speculative but yield interesting results. 3.1. Additional modelling Total system costs are composed of VRE, backup, grid and storage costs. We assume variable backup costs of 60 V/MWh plus additional investment costs with a yearly annuity of 100 V/kW. For VRE energy we assume full costs of 60 V/MWh. While this seems very
low at the moment, it seems realistic for future scenarios with 100% renewables. Quantifying grid costs is more difficult. We separate transmission and distribution grid costs and base our analysis on current rates charged by grid operators in Europe. In order to estimate the required size of the transmission grid which makes each circular model region a copper plate, we take the following approach: we assume that each 50 by 50 km cell within the circle is connected to its four nearest neighbours. We minimise these transmission capacities while guaranteeing that no renewable energy is wasted due to grid constraints. The optimisation takes into account 8 time steps which are selected to cover the whole range of values of the aggregated residual load in the region. This way many extreme load situations are considered while the computational effort remains feasible. The optimisation yields the length and capacity of the required transmission grid. We use this number in units of GW km to compute the grid costs in relation to current costs of roughly 10 V/MWh [16] for a transmission grid which we assume to be appropriate for the national level, i.e. for R ¼ 500 km. A second important contribution to the grid costs comes from the distribution grid. An exact model of these is beyond the scope of this paper. We thus use the following plausibility argument. Today, grid costs are paid by consumers in most countries [16]. In contrast, we assume that the costs are calculated based on the generation, focussing especially on decentral PV which causes the largest burden to low and medium voltage grids. Per installed PV capacity, we thus assume costs equal to the rate a consumer with the same temporal pattern would currently pay in Munich. This estimate amounts to costs for the distribution grid of 45 V/kW per year. Storage costs are calculated as the sum of investment costs for power and reservoir size, O&M as well as variable costs to replace losses in the storage cycle. The scheduling described in the last section considered only a single type of 100% efficient storage. Here, we examine the economic consequences if this storage is pumped water, batteries or hydrogen generation with methanisation. We assume typical investment costs and efficiencies for each
Table 1 Assumed investment costs, O&M costs and efficiencies for the different storage options considered. H2 refers to hydrogen storage with methanisation and re-electrification via gas turbines. Note that costs are representative of different technology choices, but their exact value in 2050 is highly uncertain. Storage type
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technology, see Table 1. From this we compute yearly annuities at a weighted average cost of capital (WACC) of 7%. Storage losses are assumed to be replaced with additional backup fuel for the same price as assumed above. The losses can be computed from the amount of energy that is put into or taken out of storage, a result of the scheduling described in the last section. Note that we do not consider mixtures of different storage technologies. While this might practically be reasonable to cover different storage cycle lengths and sizes, it would require a much more complicated modelling which may be work for the future. Here, at first, we try to keep the model as simple as possible and evaluate the effects of each storage type separately. Since largescale storage is still in its infancy it might well be that a single technology might dominate all other options in the long run. While some cost assumptions, e.g. the ones for pumped water, are in line with current project values, many others are rather speculative. Large-scale synthesis and methanisation of H2 is not available on the market today and the costs in a distant future with 100% renewables are uncertain. However, the assumed costs might also be seen as representative for certain storage characteristics: battery costs are determined by the storage energy, the rated power is high and they are rather efficient. For the H2 cycle, power investments are significant whereas the size of energy reservoir is negligible and efficiency is low. An important interplay between storage and grid costs occurs for battery storage. In contrast to the other two technologies which are assumed to be installed centrally, this decentral storage technology helps to lower the requirements for the distribution grid through short-term smoothing of the short, but high power peak of PV arrays. We model this effect in a crude all-or-nothing way, i.e. we assume the distribution costs to be negligible if batteries are installed, and otherwise distribution costs are as described above. 3.2. Results Our estimate of the grid requirements to make circular regions of radius R a copper plate are shown in Fig. 5(a). For the 25 km radius, the data’s cell radius, no grid is required in our calculations. For the other radii, the grid requirement increases significantly; from the regional to the national level by a factor of 17, from the national to the European level by a factor of 3. In Fig. 5(b), we show the number of full storage cycles that result from the scheduling model. Naturally, smaller storage volumes are cycled more often, roughly once per day, whereas larger storage tanks are used only a few times per year. A better grid mostly reduces the number of storage cycles due to interregional averaging effects. However, for a storage size of 4 h the effect is reversed. This could be explained by the fact that PV production in our model is concentrated in high subsidy countries and larger R means that storage from non-solar regions can help to buffer the solar peaks.
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Model results regarding system costs are shown in Fig. 6. The backup costs are roughly proportional to the backup energy demand, as variable costs play the major role for the assumed flexible power plants. Grid costs depend only on the radius R of the model regions, except for batteries where we assume that non-zero storage helps to reduce distribution grid costs significantly. Storage costs depend mostly on storage size, but also on R, since larger regions with better regional averaging imply fewer storage cycles and thus fewer storage losses. Considering the total system costs shown in the far-right column of Fig. 6, one can see that reasonable model setups results in electricity costs of between 110 V/MWh and 140 V/MWh. This is roughly twice the assumed costs for VRE energy. Compared with current consumer prices of roughly 120 V/MWh e endcustomer electricity prices in Germany are about 200 V/MWh of which 60% are for generation and grids e the price level for a renewable system only increases slightly. This depends of course a lot on the assumptions about price developments where we have assumed future but not completely unrealistic values. It can be seen that the lowest overall system cost is achieved for systems with small, decentral battery storage. This is due to the idea that batteries can help to reduce distribution grid costs significantly if they are able to buffer solar production peaks. Fig. 6 also permits to draw conclusions about cost-optimal system designs. These depend on the assumed type of storage. In sum, it can be observed that regional grids seem sufficient for pumped water and battery storage, whereas H2 favours larger connected areas. This is due to the large cycle losses of this technology which make an optimal smoothing of the residual load before storage mandatory when minimising system costs. Optimal storage size is 4 h for pumped water, with smaller values for batteries e whose economic merit is not so much in reducing backup energy but in relieving the burden on distribution grids. For H2, larger storage sizes seem favourable due to their low investment cost per energy unit. However, seasonal storage with storage sizes above 1 week does still not seem cost-optimal. The calculations so far assumed a 100% scenario, i.e. that wind and sun produce exactly the energy needed on average. It is interesting to ask whether a slight excess installation could help to reduce system costs. The physical consequences regarding backup energy demand were already shown in Fig. 4. Fig. 7 shows the economic results of this approach. It implies that above question has to be answered negatively since the total system costs increase with higher renewable installations, even for the respective optimal system settings. Another interesting question is to look at the economic importance of the different sectors in a future 100% renewable power system. In Fig. 8 we thus multiply the costs per energy unit for the optimal system design from Fig. 6 by the total energy consumed in Europe. This gives the total costs for each of the sectors backup and VRE generation, grid and storage. These numbers are a good indicator as to what the market sizes of these technologies could be in the future.
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F. Steinke et al. / Renewable Energy 50 (2013) 826e832
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Fig. 6. System costs per energy unit for different grid and storage options R and C. The first three columns show the partial system costs of backup, grid and storage. The last column is total system cost, i.e. the sum of the first three cost components plus the cost of the VREs. The colour-coding of values is upper bounded at 50 V/MWh and 160 V/MWh, respectively. Each row shows the costs of a different kind of storage with different price characteristics.
The results depend again on the storage technology that is considered. In all scenarios, VRE generation is the largest player. Backup generation is also significant in all scenarios, especially if one considers that a fraction of the storage costs also comprises backup generation for replacing storage losses. The storage type has a large influence on the market share of transmission and distribution grids. Batteries minimise grid costs by reducing expensive distribution grid extensions. Low efficiency storages like H2 render it important to reduce fluctuations before storage and thus require a well-extended grid. Choosing H2 storage as the major storage option thus leads to the largest grid as well as storage market. 4. Discussion and outlook This paper uses a simple model to estimate the required backup energy in 100% renewable scenarios in Europe. While the required backup power is assumed to be on the order of the peak load, the
study focuses on the required backup energy and how this depends on the interplay of the two averaging technologies grids and storage. Physical backup demand modelling is combined with an economic assessment of each power system design. A major result of the study is that even in a fully renewable Europe, where wind and sun produce on average 100% of the required electricity, significant dispatchable backup generation is needed. Without grid and storage it amounts to roughly 40% of the demand, with an ideal European grid still 20% is required. For many settings the remaining backup demands exceed the available energy from biomass, the dominant renewable power source that is dispatchable on a large scale. Important design criteria for the future power system are its CO2 emissions and the system costs. Both aspects are treated in this paper. CO2 emissions are closely linked to the use of non-renewable backup energy from coal or gas. Our physical backup energy model suggests that there is a certain degree of freedom to choose between large grid
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F. Steinke et al. / Renewable Energy 50 (2013) 826e832
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Fig. 8. Predicted market volume of the different power system components in dependence on the predominant storage solution for the 100% scenario. Values are calculated for the optimal system design for each storage type. The red fraction of storage costs is for backup fuel to compensate storage losses. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
extensions and large storage capacities. However, in order to come close to the energy potential of biomass e assumed to be around 10% e both technologies have to be extended significantly from today’s level. Especially for storage the technical feasibility of this is still questionable. For pumped water the land resources in many countries are limited, batteries with a joint capacity in the TWh range are still a long way into the future as is the technology for large-scale hydrogen generation and methanisation. In contrast, economic modelling suggests fewer grid and storage extensions, with the smallest cost-optimal storage size attained for battery storage. This paper has examined the backup energy demand in 100% renewable scenarios for Europe. The model only considered one type of storage in order to reduce the number of necessary assumptions. Future work should examine more closely the interactions between different types of storage and how each of them could service specific storage needs. Moreover, it would be interesting to study how the system could develop continuously from today’s situation into the future and how the planned storage and grid extension would behave economically throughout this transition phase.
[2]
[3]
[4]
[5]
[6]
[7]
Appendix A. The optimisation problem
[8]
The storage was scheduled by solving the following optimisation problem
[9]
min
P
bðtÞ
[10]
t
s:t: bðtÞ þ sðtÞ resDR ðtÞ SLðt þ 1Þ ¼ SLðtÞ sðtÞDt 0 SLðtÞ C; SLð0Þ ¼ SLðTÞ; bðtÞ 0 Above, b(t) is the backup power in time step t˛f0; :::; tg of length Dt, s(t) the storage output (positive)/input (negative) power, resDR(t) the residual demand in a region with radius R, SL(t) the storage level, and C the storage energy capacity. Backup and storage are not limited in power capacity. References [1] EU Commission. Commission of the European Communities: renewable energy road map: renewable energies in the 21st century: building a more
[11]
[12]
[13]
[14] [15]
[16]
sustainable future, communication from the commission to the council and the European parliament 848. BMU-BMWi Germany. Energiekonzept für eine umweltschonende, zuverlässige und bezahlbare Energieversorgung, http://www.bmu.de/energiekonzept; 2010. McKinsey KEMA. The energy futures lab at imperial college London, oxford economics, European climate foundation, roadmap 2050: a practical guide to a prosperous, low-carbon Europe: technical analysis, vol. I; 2010, http://www. roadmap2050.eu/; 2010. PricewaterhouseCoopers. European climate forum, potsdam institute for climate impact research, international institute for applied system analysis, 100% renewable electricity e a roadmap to 2050 for Europe and North Africa; 2010. TradeWind. Integrating wind e tradewind: developing Europe’s power market for the large-scale integration of wind power, study by Sintef Energieforskning AS, Riso DTU, 3E, Kema Nederland BV, technical research centre of Finland. Garrad Hassan and Partner Ltd, Tracabel Engineering and Deutsche Energie-Agentur GmbH (dena), http://www.ewea.org/fileadmin/ewea_ documents/documents/publications/reports/TradeWind_Report_01.pdf; 2009. G. U. Wissenschaftlicher Beirat der Bundesregierung, Welt im Wandel: Zukunftsfähige Bioenergie und nachhaltige Landnutzung, http://www.wbgu. de/index.php?id¼110/; 2009. Connolly D, Lund H, Mathiesen B, Leahy M. A review of computer tools for analysing the integration of renewable energy into various energy systems. Applied Energy 2010;87(4):1059e82. Mathiesen B, Lund H. Comparative analyses of seven technologies to facilitate the integration of fluctuating renewable energy sources. Renewable Power Generation, IET 2009;3(2):190e204. Krajaèiæ G, Martins R, Busuttil A, Duiæ N, da Graça Carvalho M. Hydrogen as an energy vector in the islands’ energy supply. International Journal of Hydrogen Energy 2008;33(4):1091e103. Lund H, Salgi G. The role of compressed air energy storage (caes) in future sustainable energy systems. Energy Conversion and Management 2009;50(5): 1172e9. Heide D, von Bremen L, Greiner M, Hoffmann C, Speckmann M, Bofinger S. Seasonal optimal mix of wind and solar power in a future, highly renewable Europe. Renewable Energy 2010;35:2483e9. Schaber K, Steinke F, Mühlich P, Hamacher T. Parametric study of variable renewable energy integration in Europe: advantages and costs of transmission grid extensions. Energy Policy 2012;42:498e508. Chowdhury M, Haque M, Aktarujjaman M, Negnevitsky M, Gargoom A. Grid integration impacts and energy storage systems for wind energy applications e a review. In: Power and energy society general meeting, 2011 IEEE. IEEE; 2011. p. 1e8. WEPROG, Weather and wind energy PROGnosis. Möhrlen C, Jorgensen J. URL www.weprog.com. Beurskens F, Hekkenberg M. Renewable energy projections as published in the national renewable energy action plans of the European member states covering all 27 EU member states. European Environment Agency; 2011. ENTSO-E. Overview of transmission tariffs in Europe, https://www.entsoe.eu/ market/transmission-tariffs/; 2011.
Contents lists available at SciVerse ScienceDirect
Renewable Energy journal homepage: www.elsevier.com/locate/renene
Grid vs. storage in a 100% renewable Europe Florian Steinke a, *, Philipp Wolfrum a, Clemens Hoffmann b a b
Siemens Corporate Technology, Otto-Hahn-Ring 6, Munich 81739, Germany Siemens Infrastructure & Cities, Erlangen, Germany
a r t i c l e i n f o
a b s t r a c t
Article history: Received 23 January 2012 Accepted 31 July 2012 Available online 13 September 2012
Intermittent renewable power production from wind and sun requires significant backup generation to cover the power demand at all times. This holds even if wind and sun produce on average 100% of the required energy. Backup generation can be reduced through storage e averaging in time e and/or grid extensions e averaging in space. This report examines the interplay of these technologies with respect to the reduction of required backup energy. We systematically explore a wide parameter space of combinations of both technologies. Our simple, yet informative approach quantifies the backup energy demand for each scenario. We also estimate the resulting total system costs which allow us to discuss costoptimal system designs. 2012 Elsevier Ltd. All rights reserved.
Keywords: Intermittency Renewable energy Grid expansions Storage
1. Introduction Renewable power generation is growing quickly in Europe. In 2006, the EU defined its 20-20-20 goals calling for 20% renewables in 2020 [1]. Today, already 40% of renewables seem a realistic target for the end of this decade, at least in Germany with its strong photovoltaics (PV) sector [2]. Scenarios with close to 100% renewables are now typically dated for 2050, e.g. [3,4], but they may possibly arrive much earlier. Yet there is still considerable uncertainty about what a 100% renewable Europe would look like. The largest renewable contributions will probably come from wind turbines and PV, since water, biomass and waste resources are limited. Due to their intermittent nature, significant backup generation is needed for power from wind and sun, even if they cover on average 100% of the demand. This is depicted with an exemplary time line in Fig. 1. Regarding the required backup power, one can see from Fig. 1 that almost the whole demand has to be covered from backup sources alone at certain times. Thus the installed backup capacity should roughly match the peak demand. This is in line with [5] where the capacity credit of wind is rated at only 16%, assuming a well-connected Europe. The interesting question about the backup system is thus rather how much energy the backup power plants have to provide e and how this depends on grid extensions and novel storage capacities. Grids and storage help to reduce backup energy demand by averaging intermittent power generation in space and time. The * Corresponding author. Tel.: þ49 89 636 41519; fax: þ49 89 636 49767. E-mail address: [email protected] (F. Steinke). 0960-1481/$ e see front matter 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.renene.2012.07.044
interplay of these technologies and their effect on the remaining backup energy demand is the central focus of this paper. Backup energy demand is critical in two respects. First, backup energy would have to come from renewable sources in a fully renewable scenario. The most important source of renewable backup energy is biomass, since only a minimal fraction of Europe’s water energy is dispatchable. The yearly energy potential of biomass, however, is limited to roughly 10% of the average power consumption [6]. It could thus be asked under which constellation of generation, grids and storage a fully renewable power system in Europe would be possible at all. For other constellations the backup energy is directly related to remaining CO2 emissions of the system, since additional backup energy would have to come from fossil sources. Such not fully renewable systems might be acceptable even in the long run, if the financial benefits are significant. The backup energy is thus also an important measure for determining the optimal trade-off between CO2 emission reduction targets and economic feasibility. In this paper we use scenarios for Europe where wind and sun, the variable renewable energies (VRE), produce on average 100% or more of the required electric energy. We employ a simple model to quantify the backup energy demand for a wide range of choices of grid and storage. While there are many ways of modelling the different power system components in much greater detail, we think it is important to reduce the problem to the most basic characteristics to maintain an overview and obtain robust order of magnitude results. We also determine optimal system designs. First, optimality means minimising the use of backup energy, a physics perspective. Second, we estimate the financial consequences of each scenario to determine also economically optimal designs.
F. Steinke et al. / Renewable Energy 50 (2013) 826e832 Typical Winter Week PV Wind Backup Demand
Fig. 1. Time course of generation and demand of one model cell in a typical winter week. Wind and PV produce on average 100% of the demand of the year, but significant backup is still needed. At certain points in time the backup has to cover the complete demand alone.
Fully renewable energy systems have recently been analysed in various ways. (See Connolly et al. [7] for an extended list of tools for this purpose.) Seven different technologies for integrating renewable energies into the Danish power system are examined in [8], hydrogen storage cycles for different islands in [9], and compressed air storage and CHP plants in [10]. These studies use fixed grid assumptions that permit the estimation of a single time series for the wind and PV feed-in which in turn determines the optimal operation of dispatchable integration technologies. In contrast, we examine in this paper how fluctuations of renewables can be reduced by gradually increasing grid installations. Our work is in this respect similar to the TradeWind study [5] which describes the effects of European transmission grid extensions on averaging out the fluctuations of wind power. We extend their approach by introducing a second dimension for reducing variations, namely storage. The interplay between the two technologies, transmission and storage, is challenging since both technologies can replace each other to some degree. A systematic study of this interdependence, varying the two degrees of freedom separately over a wide range of values, is the main new contribution of this paper. Another two-dimensional parametric study has been undertaken by [11] determining the optimal mix between wind and PV installations at different levels of renewable energy supply. From their work, we obtain the normalised generation time series for wind and PV and the optimal renewable mix of 65% wind and 35% PV in the average yearly energy production in a 100% renewable power system. Along the same dimensions, cost-optimal grid extensions in Europe are balanced against flexible power plants in [12]. Chowdhury et al. [13] describe technical requirements of grid and storage technologies for renewable integration. In Section 2, we present our model and the results for computing the backup energy demand in dependence on grid and storage capacities. In Section 3, we extend the modelling to estimate the economic consequences of different storage/grid choices. This requires more assumptions than the first model, which is why we have separated this part into a separate section. In Section 4, we discuss the obtained results and give an outlook on possible directions of future research. 2. Physical modelling e specifying backup energy demand 2.1. Data and model Our analysis builds on detailed time series characterising the temporal and spatial distribution of renewable energy production
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from wind and sun. European weather model data [14] was used to compute capability factors for wind and PV in hourly resolution for an 8 year period [11]. The spatial resolution of this data is 50 by 50 km and covers all of Europe. To estimate the amount of energy produced from this, plausible assumptions about the installed capacities for wind and PV are required. One way to obtain these would be to set them proportional to the regional full load hours for each generation type, that is, based on economics. However, this is not what is commonly observed today. Installations are rather determined by political support schemes. We thus use EU’s published 2020 targets [15] and scale them up such that wind and sun produce on average 100% of the required power. Within large countries, an internal distribution is assumed based on regions that are roughly aligned with the grid control areas. Within each region, capacities were distributed uniformly. The scaling of the installation capacities was performed separately for wind and sun. We set the yearly energy production of wind to 65% of consumption and that of PV to 35%. This weighting between the two intermittent sources was found optimal for reducing the distance to the load by [11]. The demand time series were based on entsoe data.1 Total consumption was assumed to remain constant at 2007 levels. In sum, this procedure yielded an 8 year time series for wind, sun and demand in each of the 50 by 50 km cells in Europe. To determine the backup energy demand and its dependence on grid and storage we then took the following simple approach: we assumed circular regions with radius R and an attached storage facility with energy reservoir of size C. The grid within the region was assumed to be strong enough to render it a copper plate, i.e. that no grid limitations are considered in this region. This simple procedure allowed us to characterise the grid and storage setup with single figures. To gain independence from regional specialities, we repeated all calculations independently for 16 distributed centre points of the circles (see Fig. 2) and averaged the results. In each circular region the time series of all contained weather cells were aggregated. The aggregated renewable generation time series was scaled a second time to obtain a local 100% scenario. Wind and sun were scaled with the same factor so that different regional shares of wind or PV persisted. For example, in Bavaria the regional renewable mix is dominated by PV, whereas in areas close the North Sea, wind plays the major role. We computed an optimal scheduling of the storage facility in each region to minimise the required backup energy, assuming that each region is equipped with a backup power plant of sufficient power. Storage levels at the beginning of the modelling period were constrained to match the storage levels at the end of the interval. Grids and storage were assumed to cause no losses. The resulting scheduling problem (for details see Appendix A) was solved with the GAMS/CPLEX optimisation package. In sum, we obtained the optimal cumulative backup energy demand for each circular region as a function of R and C. 2.2. Results The backup energy demand in relation to grid and storage is shown in Figs. 3 and 4. In Fig. 3(a), backup energy demand depending on region size R is examined, assuming no storage. It can be seen that the median backup energy need in a 100% renewable Europe is roughly 40% of the power consumption without grid or storage extensions. This number is significant, well beyond the potential of biomass. If one considers the distribution for different centre points, one observes
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Fig. 2. Assumed wind and solar capacities on the 50 by 50 km raster determined by the underlying weather model. The black stars mark the centres of different, circular model regions.
that solar-dominated regions have an even higher backup demand. This is not surprising since PV production is limited to daytime only. As specific values for R we consider the following choices: 25 km, corresponding to one cell of the weather model, 100 km, a state or province level, 500 km, a roughly national level, and 3000 km, the all-encompassing European level. For the maximally extended grid, the backup energy demand is reduced to 19%, still large in comparison to the assumed 10% biomass potential. For intermediate radii R, a gradual reduction of backup demand on a logarithmic scale is observed, without significant steps. In Fig. 3(b), we vary the second degree of freedom, namely storage size C, while keeping the grid fixed at the minimal level determined by the spatial resolution of the weather data. Storage capacity is measured by the time the storage facility could cover the average regional power demand. One can see that long-term storage reduces the backup energy demand to zero. This is not
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surprising, since by definition we have a regional 100% scenario, i.e. in the long time average no backup is needed. This infinite storage assumption can practically already be achieved with 90 days of storage capacity for all but a few high-wind regions. If one assumes 10% of available renewable backup, then a 100% renewable Europe can be achieved with between 7 and 30 days storage. It is interesting to note that while the median backup demand for all regions decreases relatively linearly with storage size C on a log-like scale, one can observe a big step for PV dominated regions. Between 4 and 12 h storage capacity, their backup demand drops significantly. This seems plausible since 12 h of storage are just enough to equal out the dayenight rhythms of solar energy production. A storage capacity of 12 h corresponds in absolute numbers to 1.4 kWh per installed kW of PV, if a 100% PV region with 1000 full load hours per year is assumed. Since today’s pumped water storage capacities are below 1 h of average demand, no
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Fig. 3. Reduction of backup energy with (a) grid extensions (no storage) or (b) storage capacity (no grid). In the upper figures, the central mark is the median, the edges of the box are the 25th and 75th percentiles, the whiskers extend to the most extreme data points not considered outliers, and outliers are plotted individually. In the second row of figures below, the same backup requirements are shown for three exemplary regions with differing shares of PV in their local renewable energy mix.
F. Steinke et al. / Renewable Energy 50 (2013) 826e832
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significant reduction of backup energy demand can be achieved with these. The initial findings on backup energy demand for scenarios with only storage or only grid extensions are extended in Fig. 4 where mixtures of the two averaging mechanisms are examined. There, it can be seen, for example, that 1 h storage with a full European grid is roughly equal to one day of storage in a national grid, or seven days of storage on a 50 km level. As an alternative to extending grid or storage, one could also install more renewable capacities than those that are needed to produce 100% of the yearly power consumption. One such case is shown in Fig. 4 (right) where an installation of 130% is assumed. Compared with the 100% scenario, similar backup energy demand results for grid or storage extensions that are one step smaller. The backup energy demand, however, remains generally quite significant, e.g. more than 30% backup is required for the no grid/no storage scenario. 3. Economic modelling e estimating total system costs Additional to the above physical modelling, we now consider the economic implications of the scenarios. An important goal is to identify cost-optimal system configurations. Note that economic modelling requires assumptions about future price developments. These are potentially less reliable than the rather robust analysis of past weather data that was used so far. The calculations are thus somewhat speculative but yield interesting results. 3.1. Additional modelling Total system costs are composed of VRE, backup, grid and storage costs. We assume variable backup costs of 60 V/MWh plus additional investment costs with a yearly annuity of 100 V/kW. For VRE energy we assume full costs of 60 V/MWh. While this seems very
low at the moment, it seems realistic for future scenarios with 100% renewables. Quantifying grid costs is more difficult. We separate transmission and distribution grid costs and base our analysis on current rates charged by grid operators in Europe. In order to estimate the required size of the transmission grid which makes each circular model region a copper plate, we take the following approach: we assume that each 50 by 50 km cell within the circle is connected to its four nearest neighbours. We minimise these transmission capacities while guaranteeing that no renewable energy is wasted due to grid constraints. The optimisation takes into account 8 time steps which are selected to cover the whole range of values of the aggregated residual load in the region. This way many extreme load situations are considered while the computational effort remains feasible. The optimisation yields the length and capacity of the required transmission grid. We use this number in units of GW km to compute the grid costs in relation to current costs of roughly 10 V/MWh [16] for a transmission grid which we assume to be appropriate for the national level, i.e. for R ¼ 500 km. A second important contribution to the grid costs comes from the distribution grid. An exact model of these is beyond the scope of this paper. We thus use the following plausibility argument. Today, grid costs are paid by consumers in most countries [16]. In contrast, we assume that the costs are calculated based on the generation, focussing especially on decentral PV which causes the largest burden to low and medium voltage grids. Per installed PV capacity, we thus assume costs equal to the rate a consumer with the same temporal pattern would currently pay in Munich. This estimate amounts to costs for the distribution grid of 45 V/kW per year. Storage costs are calculated as the sum of investment costs for power and reservoir size, O&M as well as variable costs to replace losses in the storage cycle. The scheduling described in the last section considered only a single type of 100% efficient storage. Here, we examine the economic consequences if this storage is pumped water, batteries or hydrogen generation with methanisation. We assume typical investment costs and efficiencies for each
Table 1 Assumed investment costs, O&M costs and efficiencies for the different storage options considered. H2 refers to hydrogen storage with methanisation and re-electrification via gas turbines. Note that costs are representative of different technology choices, but their exact value in 2050 is highly uncertain. Storage type
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technology, see Table 1. From this we compute yearly annuities at a weighted average cost of capital (WACC) of 7%. Storage losses are assumed to be replaced with additional backup fuel for the same price as assumed above. The losses can be computed from the amount of energy that is put into or taken out of storage, a result of the scheduling described in the last section. Note that we do not consider mixtures of different storage technologies. While this might practically be reasonable to cover different storage cycle lengths and sizes, it would require a much more complicated modelling which may be work for the future. Here, at first, we try to keep the model as simple as possible and evaluate the effects of each storage type separately. Since largescale storage is still in its infancy it might well be that a single technology might dominate all other options in the long run. While some cost assumptions, e.g. the ones for pumped water, are in line with current project values, many others are rather speculative. Large-scale synthesis and methanisation of H2 is not available on the market today and the costs in a distant future with 100% renewables are uncertain. However, the assumed costs might also be seen as representative for certain storage characteristics: battery costs are determined by the storage energy, the rated power is high and they are rather efficient. For the H2 cycle, power investments are significant whereas the size of energy reservoir is negligible and efficiency is low. An important interplay between storage and grid costs occurs for battery storage. In contrast to the other two technologies which are assumed to be installed centrally, this decentral storage technology helps to lower the requirements for the distribution grid through short-term smoothing of the short, but high power peak of PV arrays. We model this effect in a crude all-or-nothing way, i.e. we assume the distribution costs to be negligible if batteries are installed, and otherwise distribution costs are as described above. 3.2. Results Our estimate of the grid requirements to make circular regions of radius R a copper plate are shown in Fig. 5(a). For the 25 km radius, the data’s cell radius, no grid is required in our calculations. For the other radii, the grid requirement increases significantly; from the regional to the national level by a factor of 17, from the national to the European level by a factor of 3. In Fig. 5(b), we show the number of full storage cycles that result from the scheduling model. Naturally, smaller storage volumes are cycled more often, roughly once per day, whereas larger storage tanks are used only a few times per year. A better grid mostly reduces the number of storage cycles due to interregional averaging effects. However, for a storage size of 4 h the effect is reversed. This could be explained by the fact that PV production in our model is concentrated in high subsidy countries and larger R means that storage from non-solar regions can help to buffer the solar peaks.
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Model results regarding system costs are shown in Fig. 6. The backup costs are roughly proportional to the backup energy demand, as variable costs play the major role for the assumed flexible power plants. Grid costs depend only on the radius R of the model regions, except for batteries where we assume that non-zero storage helps to reduce distribution grid costs significantly. Storage costs depend mostly on storage size, but also on R, since larger regions with better regional averaging imply fewer storage cycles and thus fewer storage losses. Considering the total system costs shown in the far-right column of Fig. 6, one can see that reasonable model setups results in electricity costs of between 110 V/MWh and 140 V/MWh. This is roughly twice the assumed costs for VRE energy. Compared with current consumer prices of roughly 120 V/MWh e endcustomer electricity prices in Germany are about 200 V/MWh of which 60% are for generation and grids e the price level for a renewable system only increases slightly. This depends of course a lot on the assumptions about price developments where we have assumed future but not completely unrealistic values. It can be seen that the lowest overall system cost is achieved for systems with small, decentral battery storage. This is due to the idea that batteries can help to reduce distribution grid costs significantly if they are able to buffer solar production peaks. Fig. 6 also permits to draw conclusions about cost-optimal system designs. These depend on the assumed type of storage. In sum, it can be observed that regional grids seem sufficient for pumped water and battery storage, whereas H2 favours larger connected areas. This is due to the large cycle losses of this technology which make an optimal smoothing of the residual load before storage mandatory when minimising system costs. Optimal storage size is 4 h for pumped water, with smaller values for batteries e whose economic merit is not so much in reducing backup energy but in relieving the burden on distribution grids. For H2, larger storage sizes seem favourable due to their low investment cost per energy unit. However, seasonal storage with storage sizes above 1 week does still not seem cost-optimal. The calculations so far assumed a 100% scenario, i.e. that wind and sun produce exactly the energy needed on average. It is interesting to ask whether a slight excess installation could help to reduce system costs. The physical consequences regarding backup energy demand were already shown in Fig. 4. Fig. 7 shows the economic results of this approach. It implies that above question has to be answered negatively since the total system costs increase with higher renewable installations, even for the respective optimal system settings. Another interesting question is to look at the economic importance of the different sectors in a future 100% renewable power system. In Fig. 8 we thus multiply the costs per energy unit for the optimal system design from Fig. 6 by the total energy consumed in Europe. This gives the total costs for each of the sectors backup and VRE generation, grid and storage. These numbers are a good indicator as to what the market sizes of these technologies could be in the future.
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Fig. 6. System costs per energy unit for different grid and storage options R and C. The first three columns show the partial system costs of backup, grid and storage. The last column is total system cost, i.e. the sum of the first three cost components plus the cost of the VREs. The colour-coding of values is upper bounded at 50 V/MWh and 160 V/MWh, respectively. Each row shows the costs of a different kind of storage with different price characteristics.
The results depend again on the storage technology that is considered. In all scenarios, VRE generation is the largest player. Backup generation is also significant in all scenarios, especially if one considers that a fraction of the storage costs also comprises backup generation for replacing storage losses. The storage type has a large influence on the market share of transmission and distribution grids. Batteries minimise grid costs by reducing expensive distribution grid extensions. Low efficiency storages like H2 render it important to reduce fluctuations before storage and thus require a well-extended grid. Choosing H2 storage as the major storage option thus leads to the largest grid as well as storage market. 4. Discussion and outlook This paper uses a simple model to estimate the required backup energy in 100% renewable scenarios in Europe. While the required backup power is assumed to be on the order of the peak load, the
study focuses on the required backup energy and how this depends on the interplay of the two averaging technologies grids and storage. Physical backup demand modelling is combined with an economic assessment of each power system design. A major result of the study is that even in a fully renewable Europe, where wind and sun produce on average 100% of the required electricity, significant dispatchable backup generation is needed. Without grid and storage it amounts to roughly 40% of the demand, with an ideal European grid still 20% is required. For many settings the remaining backup demands exceed the available energy from biomass, the dominant renewable power source that is dispatchable on a large scale. Important design criteria for the future power system are its CO2 emissions and the system costs. Both aspects are treated in this paper. CO2 emissions are closely linked to the use of non-renewable backup energy from coal or gas. Our physical backup energy model suggests that there is a certain degree of freedom to choose between large grid
100% Scenario Total Balancing Costs PumpedWater [EUR/MWh]
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Fig. 7. Total system costs for different levels of renewable installations and pumped water storage.
F. Steinke et al. / Renewable Energy 50 (2013) 826e832
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Fig. 8. Predicted market volume of the different power system components in dependence on the predominant storage solution for the 100% scenario. Values are calculated for the optimal system design for each storage type. The red fraction of storage costs is for backup fuel to compensate storage losses. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
extensions and large storage capacities. However, in order to come close to the energy potential of biomass e assumed to be around 10% e both technologies have to be extended significantly from today’s level. Especially for storage the technical feasibility of this is still questionable. For pumped water the land resources in many countries are limited, batteries with a joint capacity in the TWh range are still a long way into the future as is the technology for large-scale hydrogen generation and methanisation. In contrast, economic modelling suggests fewer grid and storage extensions, with the smallest cost-optimal storage size attained for battery storage. This paper has examined the backup energy demand in 100% renewable scenarios for Europe. The model only considered one type of storage in order to reduce the number of necessary assumptions. Future work should examine more closely the interactions between different types of storage and how each of them could service specific storage needs. Moreover, it would be interesting to study how the system could develop continuously from today’s situation into the future and how the planned storage and grid extension would behave economically throughout this transition phase.
[2]
[3]
[4]
[5]
[6]
[7]
Appendix A. The optimisation problem
[8]
The storage was scheduled by solving the following optimisation problem
[9]
min
P
bðtÞ
[10]
t
s:t: bðtÞ þ sðtÞ resDR ðtÞ SLðt þ 1Þ ¼ SLðtÞ sðtÞDt 0 SLðtÞ C; SLð0Þ ¼ SLðTÞ; bðtÞ 0 Above, b(t) is the backup power in time step t˛f0; :::; tg of length Dt, s(t) the storage output (positive)/input (negative) power, resDR(t) the residual demand in a region with radius R, SL(t) the storage level, and C the storage energy capacity. Backup and storage are not limited in power capacity. References [1] EU Commission. Commission of the European Communities: renewable energy road map: renewable energies in the 21st century: building a more
[11]
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[16]
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