Efficient and equitable spatial allocation of renewable power plants at the country scale

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1 In the format provided by the authors and unedited. SUPPLEMENTARY INFORMATION VOLUME: 2 ARTICLE NUMBER: Efficient and equitable spatial allocation of renewable power plants at the country scale Supplementary Table 1: Socio-demographic characteristics of the sample. One can see that the sample is not completely representing the German population. In the sample the share of women is smaller and the share of people with higher education is significantly higher than the German average. The higher share of highly educated people is known for online-surveys (e.g., in the comparison [1] of internet panels to other survey modes) and might have been magnified by the topic of the survey. As a consequence, also the monthly household income is above average. Variable Unit Value Average age years Females percentage 45 Education (degree) Secondary school (years 5-9) percentage 6 Secondary school (years 5-10) percentage 24 Secondary school (years 5-13) percentage 27 University percentage 43 Household income ( /month) 3052 Supplementary Table 2: Attributes and their levels used in the choice experiment (see [2] for further details). Attribute Level Minimum distance to residential areas (in m) 300, 600, 900, 1600, 2500 Size of renewable energy facility small, medium, large* Number of renewable energy facilities 1,2,3,4,5 Protection of landscape view (in %) 10, 20, 30, 40, 50 High-voltage transmission lines overhead, underground Cost in Euro per month and household -10, -5, 2, 7, 14, 23 (surcharge or rebate to energy bill) * Wind: small (5-10 turbines), medium (18-25 turbines), large (35-50 turbines) Solar: small (1-10 football fields), medium (20-60 football fields), large ( football fields) Supplementary Table 3: Average and standard deviation (in parentheses) of the LCOE of wind and solar electricity, measured in ct/kwh, for three levels of the annual discount rate. One can see that both quantities moderately increase with increasing discount rate. However, the ratio of wind LCOE versus solar LCOE is nearly constant, as is the coefficient of variation. We conclude that the impact of the discount rate is only minor, which is also confirmed by the fact that the efficient minimum settlement distances are unaffected by the discount rate (see below). Discount rate LCOE wind LCOE solar 2% 4.0 (0.9) 3.6 (0.2) 5% 4.8 (1.2) 4.4 (0.3) 8% 5.7 (1.5) 5.3 (0.3) NATURE ENERGY DOI: /nenergy

2 Supplementary Figure 1: Production costs (panel a) and external costs (panel b), both measured in ct per kwh in colour scale, as functions of the minimum settlement distances of wind turbines (vertical axis) and photovoltaic power plants (horizontal axis). In the grey area the national electricity production target cannot be reached. Solar investment costs are at 36% compared to 2014 levels and the annual discount rate is 5%. One can see that the production costs increase (colour changes from green to red) and the external costs decrease (colour changes from red/green to blue) with increasing minimum settlement distances. Except for small minimum distances between settlements and wind turbines, the gradient of the production costs is stronger than that of the external costs, suggesting that on net an increase in the minimum settlement distances increases the social cost which is the sum of production and external costs. NATURE ENERGY DOI: /nenergy

3 Supplementary Figure 2: Social cost [ct per kwh] (in colour scale) as a function of the minimum settlement distances of wind turbines (vertical axis) and photovoltaic power plants (horizontal axis). In the grey area the national electricity production target cannot be reached. Solar investment costs are at 36% compared to 2014 levels and the annual discount rate is 5%. Moving from small to large minimum settlement distances, the colour changes from blue to red, which indicates an increase in the social cost. The minimum of the social cost is obtained at 800 m minimum settlement distance for wind turbines and 3000 m minimum settlement distances for photovoltaic power plants. Note that the latter minimum is very weak and obtained for a level of solar investment costs where no photovoltaic power plants are installed (see Supplementary Figure 3), so this minimum is irrelevant. NATURE ENERGY DOI: /nenergy

4 Supplementary Figure 3: Efficient shares of wind (blue) and solar power (red) in Germany as functions of the solar investment costs (in % compared to 2014 levels). Solid lines: with consideration of external costs; dashed lines: without consideration of external costs. One can see that solar power outcompetes wind power throughout the country when the solar investment costs drop to 30% of 2014 levels or below while wind power outcompetes solar power at solar investment costs of 34% or higher (solid lines). If external costs are ignored the second threshold is shifted to higher solar investment costs (dashed lines). These results are largely independent of the discount rate, because the efficient shares of wind and solar electricity mainly depend on the relative investment costs of wind turbines and photovoltaic power plants which are nearly unaffected by the discount rate (Supplementary Table 3). In addition, the effect of the minimum settlement distances on the social cost is not changed qualitatively in particular, the combination of minimum settlement distances that minimises the social cost is affected only very weakly (see Supplementary Figure 4). NATURE ENERGY DOI: /nenergy

5 Supplementary Figure 4: Social cost [ct per kwh] (in colour scale) as a function of the minimum settlement distances of wind turbines (vertical axis) and photovoltaic power plants (horizontal axis). In the grey area the national electricity production target cannot be reached. Solar investment costs are at 36% compared to 2014 levels and the annual discount rate is 2% (panel a) respectively 8% (panel b). NATURE ENERGY DOI: /nenergy

6 Supplementary Figure 5: Steps taken to determine potential sites for wind turbines and photovoltaic power plants. Panel a: identification of physically suitable areas for power plants; panel b: subtraction of legally forbidden sites, such as human settlements and infrastructure, as well as certain minimum distances to these; panel c: filling of physically and legally suitable areas with potential power plant sites, maximising packing density and considering minimum distances between neighboured wind turbines to avoid the park effect. NATURE ENERGY DOI: /nenergy

7 Supplementary Note 1 Our results are based on a number of assumptions: (i) In our definition of equity we assumed that renewable power plants impose burdens but ignored that they can also create monetary benefits, either in the form of income from selling renewable electricity and/or through tax revenues. The consequences of this on the equitable spatial allocation of renewable power plants in Germany are not easy to predict because the owners of the renewable power plants are not necessarily those residents bearing the burdens - in fact creating significant acceptance problems in some of those regions. If we assumed that all renewable power plants were locally owned the most equitable allocation pattern might shift a little from even to concentrated because local revenues then would be maximised by installing the plants in the most productive regions. (ii) We ignored the volatility of wind and solar power (except in the electricity grid model which includes hourly regional time series). In a non-spatial analysis [3] a rather even mix of wind and solar power was found to minimise the combined volatility of these two energy sources). In a spatial setting this is likely to translate into a more even spatial allocation of renewable power plants (compared to our results) because wind speeds and solar radiation are not perfectly correlated across Germany, so a spatially more dispersed allocation reduces overall volatility. A more even regional allocation reduces overall volatility and therefore also additional costs of renewable integration which are not included in the analysis (e.g., backup generation). To some extent the volatility is priced in the grid model as electricity has to be transported from currently productive regions to unproductive ones. In addition, during winter more wind electricity has to be transported from the north to the south and vice versa photovoltaic in the summer. (iii) We ignored other renewable electricity sources like bio-energy and rooftop solar panels. Bioenergy could be especially relevant with regard to the issue of volatility, since biomass can be stored and transformed into electricity when there is a shortfall in wind and solar electricity. The consideration of rooftop panels would probably not change the efficient spatial allocation because it would still be efficient to install them mainly in southern Germany like the open-field plants. Due to their higher LCOE, however, rooftop panels would become competitive only at stronger declines in solar investment costs (cf. Fig. 1 and Supplementary Figure 3). The equity problem might be slightly alleviated with rooftop panels if these panels are owned by the local residents (cf. point (i) above). (iv) We did not allow for different minimum settlement distances among the German federal states, since the high dimensionality of this decision problem would have made the analysis intractable. However, since the variation in the production costs strongly dominate the variation in the external costs, we expect that allowing for heterogeneous minimum settlement distances would not strongly alter our result that renewable power plants should be installed as close to human settlements as is legally possible. (v) The willingness-to-pay levels provided in the choice experiment for an increased settlement distance were fitted to a linear (both for wind and solar power) external cost function. One could also have used a non-linear function to allow, e.g., for decreasing marginal costs [4]. As a consequence, for small settlement distances the marginal cost would probably be higher than our present estimate while for larger settlement distance it would be lower. A higher marginal external cost at small settlement distances might lead to efficient minimum settlement distances above 800 m respectively 0 m, but due to the decrease of the marginal external cost towards larger settlement distances, the increase in the efficient minimum settlement distance would be low. NATURE ENERGY DOI: /nenergy

8 Further, the online survey from which the willingness-to-pay levels were obtained contained overproportionally many respondents with higher education probably leading to an overestimate of the willingness-to-pay and the external costs. Considering a less biased sample would therefore reduce the external costs and amplify our result that renewable power plants should be installed as close to human settlements as is legally possible. (vi) We assumed that both, wind turbines and solar panels, have the same life time of 20 years. However, life times for wind turbines may be slightly lower (15-20 years) while those of solar panels may be higher (20-30 years). This implies that we underestimated the LCOE of wind turbines compared to that of photovoltaic power plants. The error, however, is small compared to the difference between the present LCOEs of the two energy sources, so wind power would presently still outcompete solar power. However, the transition where solar power becomes competitive (cf. Fig. 1 and Supplementary Figure 3) would shift to higher solar investment costs. (vii) In the modelling of the electricity grid we considered only the transmission network (220 kv and 380 kv) and ignored distribution networks (high-voltage of 110 kv, medium-voltage, and low-voltage). In Germany, wind turbines connect about one half each to the 110 kv and the medium-voltage network while large photovoltaic power plants connect mostly to the medium-voltage network. It would be technically possible to add the 110 kv network to our model analysis, which probably affects the allocation of power plants on a regional level (and to a lower extend at the federal level) considering additional location-specific integration costs dependent on the distance to the transmission network. Due to computational and data limitations, however, we abstracted from modelling the 110 kv network. Integration costs in the medium-voltage network become more relevant for research on the allocation of wind turbines and large photovoltaic power plants at the regional level while the low-voltage network is not relevant for these plants at all. (viii) In the determination of the production costs we ignored the opportunity cost of land, such as foregone agricultural profits. However, these opportunity costs are small compared to the investment and operation/maintenance costs. For example, in 2013 highest rents for farmland in the two German states of Lower Saxony and Schleswig-Holstein ranged between 490 and 520 Euro per ha [5]. We ignored these costs, which does not affect our results. (ix) Based on [6] we considered the investment costs of wind turbines as constant in time for Germany. For the US a moderate decline has been reported [7]. This decline, however, is mainly due to the development of capacity factors like turbine heights and rotor diameters. Taking this development into account would require an entire replication of our analysis, including a recalculation of the potential areas, potential sites, LCOE, etc., because of higher required distances between turbines (park effect) and very likely higher distances to protected areas, settlements and other infrastructure for legal reasons. The impact on our results is highly non-trivial due to opposing effects (fewer but more efficient turbines per area) and could be an interesting subject of future research. (x) We did not address the dynamics of renewable energy deployment but analyzed only optimal endpoints of such a process. This ignores, among others, problems of path dependence, meaning that for instance the optimal allocation identified in the present study may not be attainable or only at high cost. Additionally, we did not address the question whether current policies such as the German Renewable Energy Act [8] are able to induce the spatial allocation patterns that were identified as optimal. NATURE ENERGY DOI: /nenergy

9 Supplementary References [1] Lindhjem H, Navrud S, Using internet in stated preference surveys: a review and comparison of survey Modes. International Review of Environmental and Resource Economics 5, [2] Oehlmann M, Meyerhoff J, Stated preferences towards renewable energy alternatives in Germany do the consequentiality of the survey and trust in institutions matter? Journal of Environmental Economics and Policy 6, [3] Tafarte P, Das S, Eichhorn M, Thrän D, Small adaptations, big impacts: options for an optimized mix of variable renewable energy sources. Energy 72, [4] Drechsler M, Ohl C, Meyerhoff J, Eichhorn M, Monsees J, Combining spatial modeling and choice experiments for the optimal spatial allocation of wind turbines. Energy Policy 39, [5] DBV, Situationsbericht 2014/2015. Trends und Fakten zur Landwirtschaft. Deutscher Bauernverband DBV. [6] ISE, Stromgestehungskosten Erneuerbare Energien. Fraunhofer ISE, Freiburg, Germany. _Stromgestehungskosten_Erneuerbare%20Energien_final.pdf (last access March 2017). [7] Wiser R, Jenni K, Seel J, Baker E, Hand M, Lantz E, Smith A, Expert elicitation survey on wind energy costs. Nature Energy 1, Article number [8] BMWi, Das erneuerbare-energien-gesetz. Bundesministerium für Wirtschaft und Energie. (last access March NATURE ENERGY DOI: /nenergy