Renewable and Sustainable Energy Reviews 42 (2015) 1–15

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Renewable and Sustainable Energy Reviews

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The CLIMIX model: A tool to create and evaluate spatially-resolved scenarios of photovoltaic and development

S. Jerez a,b,n, F. Thais c, I. Tobin a, M. Wild d, A. Colette e, P. Yiou a, R. Vautard a a Laboratoire des Sciences du Climat et de l’Environnement (LSCE), IPSL, CEA-CNRS-UVSQ, 91191 Gif sur Yvette, France b Department of Physics, University of Murcia, 30100 Murcia, Spain c Institut de Technico-Economie des Systèmes Energétiques (I-Tésé), CEA/DEN/DANS, 91191 Gif sur Yvette, France d Institute for Atmospheric and Climate Science, ETH Zurich, 8092 Zurich, Switzerland e Institut National de l’Environnement Industriel et des Risques (INERIS), Parc Technologique Alata, 60550 Verneuil en Halatte, France article info abstract

Article history: Renewable energies arise as part of both economic development plans and mitigation strategies aimed Received 16 April 2014 at abating climate change. Contrariwise, most renewable energies are potentially vulnerable to climate Received in revised form change, which could affect in particular solar and wind power. Proper evaluations of this two-way 5 August 2014 climate– relationship require detailed information of the geographical location of the Accepted 26 September 2014 renewable energy fleets. However, this information is usually provided as total amounts installed per administrative region, especially with respect to future planned installations. To help overcome this Keywords: limiting issue, the objective of this contribution was to develop the so-called CLIMIX model: a tool that Wind power performs a realistic spatial allocation of given amounts of both photovoltaic (PV) and wind power Solar photovoltaic power installed capacities and evaluates the energy generated under varying climate conditions. This is done Spatial distribution of renewable energy over a regular grid so that the created scenarios can be directly used in conjunction with outputs of plants ° fi Europe climate models. First, we used the 0.44 resolution grid de ned for the EURO-CORDEX project and Euro-Cordex applied the CLIMIX model to spatially allocate total amounts of both unreported 2012 and future 2020 PV and wind power installations in Europe at the country level. Second, we performed a validation exercise using the various options for estimating PV and wind power production under the created scenarios that are included in the model. The results revealed an acceptable agreement between the estimated and the recorded power production values in every European country. Lastly, we estimated increases in power production derived from the future deployment of new renewable units, often obtaining non-direct relationships. This latter further emphasizes the need of accurate spatially-resolved PV and wind power scenarios in order to perform reliable estimations of power production. & 2014 Elsevier Ltd. All rights reserved.

Contents

1. Introduction...... 2 2. Data...... 2 2.1. PV and wind power installed capacity ...... 2 2.2. Climate simulation...... 3 2.3. Population ...... 3 2.4. PV and wind power production ...... 3 3. Methodology: the CLIMIX model ...... 5 3.1. Creation of installed power spatial scenarios...... 5 3.1.1. Criteria for differentiating small and large PV plants...... 5 3.1.2. Determining factor, forbidden locations and efficiency filter...... 5 3.1.3. Basic algorithm ...... 5 3.2. Estimation of power production under the created scenarios...... 7

n Corresponding author at: Edificio CIOYN (Oficina 1.3), Departamento de Física, Campus de Espinardo, Universidad de Murcia, 30100 Murcia, Spain. Tel.: þ34 868 88 85 52. E-mail address: [email protected] (S. Jerez). http://dx.doi.org/10.1016/j.rser.2014.09.041 1364-0321/& 2014 Elsevier Ltd. All rights reserved. 2 S. Jerez et al. / Renewable and Sustainable Energy Reviews 42 (2015) 1–15

3.2.1. Options for the estimation of PV power production ...... 7 3.2.2. Approaches for the estimation of wind power production ...... 7 4. Results...... 8 4.1. Spatial scenarios of PV and wind power installations ...... 8 4.2. Estimates of PV and wind power production under the created scenarios ...... 8 4.2.1. Validation...... 8 4.2.2. Application: future expectations ...... 8 5. Conclusions and discussion...... 9 Acknowledgments...... 13 Appendix A. The hindcast climate simulation ...... 13 References...... 14

1. Introduction The various data used in this work are listed in Section 2. Section 3 provides a detailed description of the CLIMIX model, as A spectacular deployment of new renewable energy installations, well as of the assumptions made for the creation of the 2012 and in particular solar plants and wind farms, is taking place in both 2020 PV and wind power scenarios. Section 4 presents the created developing and developed countries [1]. The commitment on low scenarios along with the results of the validation and application carbon energies as a substitute of traditional fossil energy resources exercises. Finally, Section 5 summarizes and discuss the main has a double motivation and objective. On the one hand, the conclusions. investments devoted to exploit the free, local and renewable resources, such as solar radiation and wind, are expected to rebound positively on economy by both promoting the local employment and favoring 2. Data the energetic independence of the countries that nowadays need to import expensive amounts of energy from abroad [2–4]. On the other 2.1. PV and wind power installed capacity hand, renewable energies constitute a major part of the mitigation strategies aimed at abating climate change by reducing greenhouse The total amounts of PV or wind power capacity installed or to gas (GHG) emissions [5,6].Inthisscenario,the20–20–20 target set by be installed in each European country are listed in Tables 1 and 2. the European Union commits countries to reduce GHG emissions, These correspond to 2012 reported quantities [17,18] or to 2020 increase the efficiency of the energy generation systems, and raise the planned fleets according to the European Climate and Energy share of energy production from renewable resources by 20% in 2020 compared to 1990s levels [7]. However, wind and solar renewable energies, among those that depend on the atmospheric conditions, Table 1 are, contrariwise, part of the sectors potentially vulnerable to changes Amounts of on-shore and off-shore wind power installed at known locations, at the in climate [8–11]. Besides, a massive development of renewable end of 2012 and planned for the year 2020 in each European country. Gray energy plants could have an additional effect on local weather and numbers in the 2012 columns indicate that we already know the locations of all the wind power installed at the end of 2012. Gray background colors in the 2020 climate by modifying atmospheric circulations [12–16]. columns indicate that the 2020 planned amounts have already been exceeded by In order to evaluate the effectiveness and environmental impacts of the year 2012. Units: MW. regional or global scenarios of renewable energy deployment along with climate change impacts on it, spatially-resolved scenarios with On-shore WP installations Off-shore WP installations the location of current and planned production units as accurate as Known 2012 2020 Known 2012 2020 possible are required. However, the lack of public information and detail in this respect is notable. The actual location of all current Austria 1314.4 1314.4 2578.0 0 0 0 renewable energy plants is often difficult to survey with accuracy, Belgium 920.8 920.8 2320.0 1791.2 1791.2 2000.0 especially regarding the diffuse network of solar photovoltaic (PV) Bulgary 636.0 668.0 1115.0 0 0 0 Cyprus 0 0 300.0 0 0 0 production. Moreover, future national or regional plans for the Czeh Rep. 261.3 261.3 743.0 0 0 0 development of new installations are generally provided at a very Denmark 2999.6 3373.0 2621.0 1466.2 1466.2 1339.0 low resolution, usually as totals over large administrative areas. Estonia 434.6 434.6 400.0 700.0 700.0 250.0 In this context, the objective of this work is to design and test a Finland 198.7 198.7 1600.0 800.3 800.3 900.0 model that provides gridded spatial scenarios of PV and wind power France 7759.6 7894.0 19,000.0 2311.5 2311.5 6000.0 Germany 31,334.1 32,118.0 35,750.0 10,307.9 10,307.9 10,000.0 installed at each location (i.e. grid cell) accounting for either present Greece 1237.9 1611.0 7200.0 536.0 536.0 300.0 deployment or future targets of power installed or to be installed Hungary 516.5 542.0 750.0 0 0 0 respectively over a region. As one of the major benefits of having these Ireland 1725.2 1823.0 4094.0 25.2 25.2 555.0 scenarios is that it allows assessments on climate and energy mix, we Italy 7858.5 8006.0 12,000.0 284.0 284.0 680.0 Latvia 31.8 32.0 236.0 0 0 180.0 called the model CLIMIX. The goal is to provide realistic scenarios Lithuania 142.9 198.0 500.0 0 0 0 rather than ideal or optimal ones. Hence, beyond the spatial distribu- Luxembourg 43.7 44.0 131.0 0 0 0 tion of the resources, the known location of current installations and Malta 0 0 14.45 0 0 0 factors such as the population density and the existence of protected Netherlands 2471.9 2535.0 6000.0 3128.0 3128.0 5178.0 areas where the installation of renewable units may be forbidden are Poland 1842.0 1867.0 5600.0 0 0 500.0 Portugal 4249.8 4488.0 6800.0 4.0 4.0 75.0 also taken into account. The CLIMIX model has been applied to obtain Romania 2197.9 2197.9 4000.0 500.0 500.0 0 present (year 2012) and near future (year 2020) PV and wind power Slovakia 5.1 5.1 350.0 0 0 0 scenarios for each European country. Several options for estimating PV Slovenia 0 0 106.0 0 0 0 andwindpowerproductionunderthecreatedscenarioswerealso Spain 22,145.9 22,145.9 35,000.0 10.0 10.0 3000.0 Sweden 2153.5 2720.0 4365.0 1207.6 1207.6 182.0 implemented in the model. We used them in both validation and UK 59,000.3 5900.3 14,890.0 6652.8 6652.8 12,990.0 application exercises. S. Jerez et al. / Renewable and Sustainable Energy Reviews 42 (2015) 1–15 3

Table 2 Amounts of PV power installed at known locations, at the end of 2012 and planned for the year 2020 in each European country: in all kinds of PV installations (2nd–4th columns; gray background colors in the 2020 column indicate that the 2020 planned amounts have already been exceeded by the year 2012), in large PV installations (5th– 7th columns) and in small PV installations (8th–10th columns). Units: MW. As defined in the text, all the current PV installations at known locations are large installations. The small-to-large ratio (last column) is computed for all countries considering its known value for France (in bold) and assuming its proportionality to the population density of each country (11th column; units: population/km2). From the values of this ratio, the amounts depicted in the 2nd and 3rd columns were split into the amounts corresponding to either large or small PV plants.

All PV installations Large PV installations Small PV installations Pop Ratio S/L

Known 2012 2020 Known 2012 2020 Known 2012 2020

Austria 0 418 322 0 187.2 187.2 0 230.8 230.8 97 1.233 Belgium 46.1 2650 1340 46.1 501.5 501.5 0 2148.5 2148.5 337 4.284 Bulgary 15.1 908 303 15.1 483.8 483.8 0 424.2 424.2 69 0.877 Cyprus 0 9 192 0 3.6 77.2 0 5.4 114.8 117 1.487 Czeh Rep. 420.6 2072 1695 420.6 781.0 781.0 0 1291.0 1291.0 130 1.653 Denmark 0 394 6 0 152.2 152.2 0 241.8 241.8 125 1.589 Estonia 0 0.2 0 0 0.1 0.1 0 0.1 0.1 28 0.356 Finland 0 1 10 0 0.8 8.3 0 0.2 1.7 16 0.203 France 336.6 4003 4860 336.6 1660.3 2015.8 0 2342.7 2844.2 111 1.411 Germany 1693.0 32,411 51,753 1693.0 8180.5 13,062.3 0 24,230.5 38,690.7 233 2.962 Greece 19.3 1536 2200 19.3 759.7 1083.7 0 779.4 1116.3 81 1.030 Hungary 0 4 63 0 1.7 26.5 0 2.3 36.5 108 1.373 Ireland 0 3 0 0 1.6 1.6 0 1.4 1.4 66 0.839 Italy 556.3 16,361 8000 556.3 4796.5 4796.5 0 11,564.5 11,564.5 192 2.441 Latvia 0 1 2 0 0.7 1.4 0 0.3 0.6 37 0.470 Lithuania 0 6 10 0 3.5 5.9 0 2.5 4.1 55 0.699 Luxembourg 0 30 113 0 9.4 35.3 0 20.6 77.7 173 2.199 Malta 0 12 27.88 0 0.7 1.6 0 11.3 26.3 1260 16.017 Netherlands 2.3 266 722 2.3 44.4 120.4 0 221.6 601.6 393 4.996 Poland 0 7 3 0 2.7 2.7 0 4.3 4.3 124 1.576 Portugal 82.0 244 1000 82.0 102.3 419.1 0 141.7 580.9 109 1.386 Romania 0 30 260 0 14.9 128.9 0 15.1 131.1 80 1.017 Slovakia 0 523 300 0 216.9 216.9 0 306.1 306.1 111 1.411 Slovenia 0 198 139 0 89.7 89.7 0 108.3 108.3 95 1.208 Spain 2363.0 5166 8367 2363.0 2437.9 3948.6 0 2728.1 4418.4 88 1.119 Sweden 0 19 8 0 15.2 15.2 0 3.8 3.8 20 0.254 UK 0 1829 2680 0 430.5 630.7 0 1398.5 2049.3 255.6 3.249 package [7]. The distance to the 2020 targets strongly varies from 0.44° in both latitude and longitude and covering the whole one country to another. While the 2020 plans will still require a European continent. Such a horizontal grid is used to construct substantial growth in some countries, they have already been the PV and wind power scenarios for each European country, exceeded in others, especially those for PV installations. In the whose masks over that grid are depicted in Fig. 1a. Besides, the latter case, we consider the same scenario for both years 2012 land use assigned by WRF to each grid cell of the domain, based on and 2020. the U.S. Geological Survey's 24-category land uses classification, is Only part of the reported amounts of installed power corre- used to identify the grid cells occupied by forests or cities (Fig. 1b) sponds to installations whose geo-localization is publicly known. since the model may restrict the allocation of renewable In particular, we could find out where the majority of the current units there. wind power installations are located (all the off-shore and almost all the on-shore ones) (Table 1), including further specifications such as the hub height of the turbines comprised in each installa- 2.3. Population tion [17]. However, only the locations of the current largest PV plants, i.e. those with a power capacity larger than 2 MW (1 MW Besides the climatology of the resources, the CLIMIX model in the case of France), are known [19,20] (Table 2). The amounts of requires the spatial distribution of the population over the target power capacity to be spatially allocated by the CLIMIX model are regions to create the scenarios. This information was obtained thus given by the difference between the total amounts and the from a database developed at the INERIS that consists of an amounts that correspond to installations at known locations. optimal combination of INSEE data for France, JRC for the rest of Europe, and CINES for the rest of the world. This database was 2.2. Climate simulation remapped to our working grid from its higher resolution by addition of the values enclosed in each grid cell (Fig. 1e). Also, Gridded climate information accounting for the availability of we use data of population spatial density in each country obtained the resources is required both to create the PV and wind power from Ref. [24]. scenarios and to estimate afterward power production under the created scenarios. This information was retrieved from a hindcast climate simulation performed with the Weather Research and Forecast (WRF) V3.3.1 regional climate model [21], driven and 2.4. PV and wind power production nudged by ERA-Interim [22], that spans the period 2000–2012 providing 3-h time-series of the prognostic variables (see The created 2012 scenarios were applied to estimate annual PV Appendix A for further details of the simulation and its validation). and wind power production values in each European country. As a The simulation domain follows the specifications established for matter of validation, these were compared with actual records of the EURO-CORDEX project [23], having an horizontal resolution of annual power production that were obtained from Ref. [25]. 4 S. Jerez et al. / Renewable and Sustainable Energy Reviews 42 (2015) 1–15

Fig. 1. (a) Masks of the target regions (European countries) over the working grid. (b) Grid cells identified as forest (gray) and cities (black) according to the 24 USGS land uses categorization. (c and d) Simulated climatologies of surface downward solar radiation (in W/m2) and 10-m wind speed (in m/s) used for the creation of the PV and wind power scenarios respectively. (e) Spatial distribution of the population density over Europe (units: population/km2). (f) Three zones defined by different thresholds of the population density: light green denotes a value below the 60th percentile, medium green between the 60th and the 90th percentiles, and dark green above the 90th percentile. (A color version of this figure is provided in the web version of this article.)

Table 3 Summary of the main factors determining the spatial distribution of the renewable installations.

Determining factor Forbidden locations Efficiency filter Other observations

PV installations Small Resource population Forest 30th Percentile Sea Large Resource Forest 30th Percentile Cities Sea

Wind power installations On-shore Resource/population Forest 30th Percentile Cities Sea Off-shore Resource Land 50th Percentile Max. distance to the coastline (1 grid cell) S. Jerez et al. / Renewable and Sustainable Energy Reviews 42 (2015) 1–15 5

Table 4 Regarding the option of marking grid cells as forbidden loca- Turbine models selected for the unknown/new wind farms that need to be tions, we opted for avoiding those classified as forest and sea (or fi speci ed. water masses) for the installation of both kind of PV plants and on- shore wind power installations. Cities were also excluded for the ID P (MW) H (m) VI (m/s) VO (m/s) VR (m/s) D (m) Manufacturer spatial allocation of large PV plants and on-shore wind power 1 2.5 60 4 25 14 80 installations (Fig. 1b). Finally, off-shore installations are obviously 2 2.3 80 4 25 13 90 Nordex placed only over sea areas, having to specify how far (in number of 3 0.9 60 3 25 13 55 GE grid cells) from the coastline they can be placed. We decided to 4 1.0 91 3 23 11.5 60 DeWind 5 0.6 65 2.5 28 12 44 restrict the installation of off-shore wind power plants to the first 6 1.5 98 2.5 28 12 66 Enercon sea grid cells after the land ones given that our working grid has a 7 1.8 98 2.5 28 12 70 Enercon horizontal resolution of approximately 50 km at the European latitudes. CLIMIX also allows for the application of an efficiency filter 3. Methodology: the CLIMIX model aimed to avoid having installations everywhere within the author- ized areas, which could not sound very realistic. This is done by 3.1. Creation of installed power spatial scenarios selecting a minimal percentile of the determining factor distribu- tion as a threshold that needs to be exceeded in order to allow The developed model to create spatial scenarios of power having power units. Otherwise the grid cell is discarded as if it was installed works differently for allocating on-shore or off-shore a forbidden location. For this specification we have been more wind power installations and small/decentralized or large/centra- restrictive for the off-shore installations than for the rest, choosing lized PV plants. For each of these types of installations, different to occupy at most 50% of the available sea locations of each factors will determine the final spatial scenario and different country with off-shore wind farms, i.e. that threshold was taken as locations will be labeled as forbidden (Section 3.1.2). Nonetheless, the median of the determining factor over the authorized grid aside from the different assumptions made in each case, the basic cells, and 70% of the land area with the rest of installations, i.e. the algorithm constituting the CLIMIX core (described in Section 3.1.3) efficiency threshold was fixed to the 30th percentile of the is the same in all cases. determining factor. However, if some of the known installations are located in any of these either non-efficient or forbidden locations, the model lifts the ban so that the grid cell can include 3.1.1. Criteria for differentiating small and large PV plants renewable installations. The difference between on-shore and off-shore wind power installations is clear and the information for each type regarding the amount installed or to be installed is as well clearly provided 3.1.3. Basic algorithm (Table 1). However, this is not the case for differentiating between Under the previous assumptions, the determining factor is used small and large PV installations. Hence, the first issue to overcome to compute a normalized spatial density for all the power installed is to establish a criterion for doing so. of each type over the target region as follows: The regular reports published by the French Ministry of Sustainable Development [26] provide data of PV power installed ¼ Fi ð Þ differentiating, in particular, between plants with more and less Di 1 ∑ Fi than 250 kW of power capacity. We took this value as the upper (bottom) limit for defining small (large) PV installations. Note that where Fi is the value of the determining factor in the ith grid cell of since the current PV installations at known locations are those the target region and the denominator is the sum of Fi over all grid with a power installed larger than 1 (2) MW in France (Europe), cells comprised in the target area and authorized to yield units. all of them are classified as large PV installations based on this The value of power installed of each type in each grid cell is definition. According to these reports, the ratio between the given by amount of PV power installed in small installations and the Pi ¼ DiPtot ð2Þ amount of PV power installed in large installations (small-to-large ratio) was 1.4 at the end of June 2012 in France. We assumed that where Ptot denotes the total amount of power installed of that type this value is proportional to the population spatial density of each in all the target region. country and remains constant in time, and thereby extrapolated its In parallel, in case information on the location of current value for each European country. Using these values of the small- installations has been provided, the CLIMIX model aggregates to-large ratio, we split the total amount of PV power installed or to the power installed of each type at such known locations per grid be installed into the contributions coming from large and small cell (Fig. 2a and b). Then, these already-known amounts are installations (Table 2). removed from the former amounts obtained with Eq. (2): 0 ¼ K ð Þ Pi Pi Pi 3 3.1.2. Determining factor, forbidden locations and efficiency filter k where Pi is the current known power installed in the ith cell. The factors determining the spatial distribution of the various Negative values of power installed are obviously not allowed, installations within each target region are summarized in Table 3. 0 fi hence if Pi results in a negative value, it is xed to 0. The amount of the resource is the main driving factor for the Then, a new spatial density is computed in order to allocate the allocation of each type of installation. This information is provided amount of power installed at unknown locations as follows: by the climatologies of surface downward short-wave radiation 0 0 P and 10-m wind speed obtained from the simulation described in D ¼ i ð4Þ i ∑ 0 Section 2.2 (Fig. 1c and d). However, the CLIMIX model uses the Pi population factor to shape the distribution given by these clima- Finally, the amount of power installed in each grid cell of the tological patterns in two cases: the higher the population, the target region is estimated as higher the number of small PV installations and the lower the ¼ 0 U þ K ð Þ number of on-shore wind power installations (see Table 3). Pi DiPtot Pi 5 6 S. Jerez et al. / Renewable and Sustainable Energy Reviews 42 (2015) 1–15

Fig. 2. Power capacity installed per grid cell (units: MW). (a) Large PV and (b) on-shore and off-shore wind power installed at known locations aggregated per grid cell. (c and e) PV (small plus large) and (d and f) wind power (on-shore plus off-shore) scenarios created for the years (c and d) 2012 and (e and f) 2020. (A color version of this figure is provided in the web version of this article.)

U where Ptot is the total amount of power installed in the entire target region at unknown locations. The diagram of Fig. 3 sum- marizes schematically all these steps. Additionally, we can determine a top limit for the installed

power in each grid cell (Pmax). If Pi exceeds that maximum as a U fi consequence of the spatial allocation of the quantity Ptot,itis xed to Pmax and the exceedance (PiPmax) redistributed repeating the formerly described procedure. We decided that the fraction of the area enclosed in each grid cell to be occupied with renewable energy plants would be, at most, 20%. Thereby, according to the specifications provided by Ref. [27] and given that the surface of 2 our grid cells is about 2500 km , Pmax was taken as 2500 MW for both PV and wind power installed per grid cell. This restriction Fig. 3. Scheme illustrating the main steps of the basic algorithm developed to was applied to each type of renewable installation, PV or wind, distribute spatially the total amounts of power installed in a region. independently, and was not exceeded in any case. S. Jerez et al. / Renewable and Sustainable Energy Reviews 42 (2015) 1–15 7

3.2. Estimation of power production under the created scenarios outputs of climate models for the estimation of PV power potential and production, while it likely reduces the accuracy of such In order to estimate the power production in each target region estimations. However, new formulations, beyond the scope of this (i.e. European country) under the created spatial scenarios of PV paper, could be implemented in the model with moderate efforts. and wind power installed, several options/methods have been implemented in the CLIMIX model. These options are used to 3.2.2. Approaches for the estimation of wind power production compute either the PV or the wind power potential (PV and W pot pot Wpot is estimated by using the common wind power curve respectively), a parameter that multiplied by the power installed proposed by Ref. [30] that establishes the four well-known at each site will give the power production of such installation. working regimes of a (Eq. (11)). If the wind speed This parameter varies according to the atmospheric conditions. is below a certain value, the cut-in speed (VI)oftheturbine, The time-series of the atmospheric variables involved in its there is no production; between the cut-in and the rated speed computation were retrieved from the climate simulation described (VR), the potential grows with the cube of the wind speed; in Section 2.2, thus having 3-h time resolution. Once we have between the rated speed and the cut-out speed (VO), the computed the power production at each site and every 3 h, these potential is 1 (i.e. the production is maximum, equaling the values were spatially and temporally aggregated per country and power installed); and finally, if the wind speed is higher than year respectively. the cut-off speed, Wpot is set to 0 since the turbine stops for security reasons. 3.2.1. Options for the estimation of PV power production 8 > 0ifV oV I CLIMIX offers three different formulations (options 1, 2 and 3) > > V 3 V3 < I if V rV oV for the estimation of the PVpot. All can be summarized in the V3 V 3 I R Wpot ¼ R I ð11Þ general expression given in Eq. (6): > r o > 1ifV R V V O :> G 0ifV ZV O PVpot ¼ PR ð6Þ GSTC Hence, in order to estimate the wind power potential, we first where G is the surface downward short-wave (solar) radiation, need to estimate the characteristics of the wind turbines G is G under standard test conditions (1000 W/m2) and P is the STC R installed, not only in terms of cut-in, rated and cut-off nominal performance ratio of the PV cells accounting for losses due to speeds, but also in terms of hub heights since the wind speed increases of the PV cells temperature. varies with the altitude. In order to account for this latter issue, we have implemented in the model the wind profile power law fi 3.2.1.1. Option 1. The rst option included in the model consists of first established by Ref. [31] relating the wind speed at whatever taking PR as a constant value fixed to 0.75, given that it typically altitude (h) to the known wind speed at a reference level (h0)as ranges between 0.7 and 0.8, as in Ref. [27]. In this option the only follows: meteorological field involved in the estimation of the PV 1=7 production is thus the surface solar radiation. h VhðÞ¼VhðÞ0 ð12Þ h0 3.2.1.2. Option 2. Following the assumptions made in Ref. [9], this Then the type of turbines installed in each farm needs to be option establishes the negative gradient linear relationship for the estimated. From the TheWindPower data base [17] we know the efficiency of a PV cell as a function of cell temperature as nominal power and the hub height of most of the currently ¼ β þγ ð Þ PR 1 Tcell Tref log 10G 7 operative turbines. However, the cut-in, rated and cut-out speeds were missing in this database and we decided to fix them to their where β and γ, the temperature and irradiance coefficients set by most common values: 3.5, 13 and 25 m/s respectively. For the the cell material and structure, are taken as 0.0045 and unknown/new wind farms, we have implemented the three 0.1 respectively, which is typical for monocrystalline silicon cells, different approaches described below aimed at specifying the type and T and T denote the cell and reference temperatures. The cell ref of turbine underlying each installation. latter is 25 °C and the former is modeled as ¼ þ þ ð Þ Tcell c1 c2T c3G 8 3.2.2.1. Approach 1. This is the simplest approach and consists where T is the temperature of the air around the cells (in °C), and of considering a single turbine model for all unknown or new c1, c2 and c3 depend on details of the module and mounting that on-shore and off-shore installations. Based on the averaged affect heat transfer from the cell and were taken as 3.75 °C, 1.14 characteristics of current and future wind turbines, we and 0.0175 °Cm2 W1. considered for the on-shore parks turbines with hub height of 80 (100) m and for the off-shore of 100 (150) m for the 2012 ¼ ¼ 3.2.1.3. Option 3. This option follows the approach used in Ref. [28] (2020) scenarios; in all cases we considered VI 3.5 m/s, VR 13 m/ ¼ that estimates the performance ratio as s and VO 25 m/s. ¼ þγ ð Þ PR 1 Tcell Tref 9 3.2.2.2. Approach 2. CLIMIX tries out all the wind turbines provided in a list (we considered all commercial models listed in with γ¼0.005. Tcell is modeled this time as Ref. [32] plus all used in Ref. [33]; a total of 75 different models) ¼ þ þ þ ð Þ Tcell c1 c2T c3G c4V 10 and, for each grid cell, identifies and selects the turbine model where V is the wind speed (in m/s) and c1 ¼4.3 °C, c2 ¼0.943, that maximizes the wind power production according to Eqs. (11) 2 1 1 c3 ¼0.028 °Cm W and c4 ¼1.528 °Csm according to Ref. and (12). [29]. It should be acknowledged that none of these options considers 3.2.2.3. Approach 3. This is similar to the previous approach, but the tilt of PV panels, thus no distinction between the contribution this procedure optimizes the ratio of the production divided by the of direct and diffuse solar radiation to the panels output is taken running costs of the constructed wind farms. These costs are into account. This simplification makes it easy to use directly the defined as in Ref. [33] using Eq. (13), being mainly related to the 8 S. Jerez et al. / Renewable and Sustainable Energy Reviews 42 (2015) 1–15 maintenance of the wind parks, without considering initial 4.2. Estimates of PV and wind power production under the created economic investments, and increase with the number of turbines scenarios to be installed (N). N is given in each grid cell by dividing the wind power installed through turbines with unknown characteristics by The created 2012 and 2020 scenarios were used to estimate the nominal power of the tested turbine model in each case (and annual PV and wind power production values in each European rounded down the resulting value to be an integer, which may country using the three options for computing PV power produc- causes some small disagreement between the initial and the final tion and the three approaches for the refinement of the wind total amounts). power scenarios described in Section 3.2. The 3-h time-series of surface downward short-wave radiation, 10-m wind speed and 2 1 2-m air temperature provided by the hindcast simulation costs ¼ N þ exp 0:00174N2 ð13Þ 3 3 described in Section 2.2 and in Appendix A were used as inputs. The annual values of PV and wind power production were obtained as time-aggregates over either one single year, in order Both approaches 2 and 3 were implemented in the model so to perform a validation exercise (Section 4.2.1), or the whole that they allow for additional restrictions related to the max- simulated period, in order to obtain climatological values and imum nominal power (i.e. size) of the turbines to be installed in assess future expectations (Section 4.2.2). each grid cell. We used this restriction and defined three different land areas within Europeaccordingtothepopulation 4.2.1. Validation density of each grid cell (Fig. 1f) where different maximum The time series of power production obtained under the turbine sizes are permitted. Those grid cells with a value of the created 2012 scenario were time-aggregated for the year 2012. population density above the 90th percentile compose a first The resulting estimated values were compared with recent past area where only turbines of up to 1 MW of nominal power can power production records in each country in the years 2011 and be allocated. Those grid cells whose values of the population 2012 as reported in Ref. [25] in order to assess the reliance of the density are between the 60th and the 90th percentiles form a overall methodology. However, a direct comparison of these secondareawhereturbinesofupto1.5MWofnominalpower estimates and the reported values is difficult because the incor- are allowed. Last, any limit for the turbine size is imposed in the poration of new power plants takes place progressively over time rest of grid cells, that are labeled as the third area. According to along the year while our scenarios are static, which combined with suitability studies based on public perception and acceptance the strong seasonality in the resource may introduce large dis- [34,35], the idea of this procedure is to favor the farms crepancies between our estimations and the actual values that are comprised of small wind turbines in the highly populated areas, not attributable to the methodology itself. Besides, errors can be of medium size models in semi-populated areas and of big ones due to biases in the meteorological time-series. in sparsely populated or deserted areas. Under the mentioned cautions, a reasonable agreement for most countries between the estimated values of power production under the 2012 scenario and those recorded in the recent past is observed (Figs. 5 and 6). In most cases, the former are larger than the latter though, which can be attributed to several factors. 4. Results First, the estimated values consider all the power installed at the end of 2012 being operative since the beginning of the year, while 4.1. Spatial scenarios of PV and wind power installations it actually occurs progressively along the year as commented. Second, a positive bias in wind and PV production is actually Fig. 2c–f shows the sum of the power installed at known locations expected since both wind speed and solar radiation are over- plus the power installed at unknown locations that has been spatially estimated in the model simulation (see Ref. [36] and Appendix A). distributed by the CLIMIX model (according to the assumptions Other sources of biases, leading to overestimation of the simulated described in Section 3.1) applied to each country separately, for (c production, are also expected: our simulations assume that the and e) large plus small PV installations, and (d and f) on-shore plus whole fleets are operational and functioning well, while main- off-shore wind power installations. These are thus the final spatial tenance operations, possible curtailment measures, malfunction- scenarios created for (c and d) 2012 and (e and f) 2020. They reflect ing of wind mills due to icing or dirt, inhibition of PV production both national commitments and resource abundance as considered due to snow cover, incomplete reporting, do occur. by the model for the spatial allocation of the installations. Spatial differences related to the former feature are clearly visible in the case of PV power, with Germany and Italy holding the major part of all 4.2.2. Application: future expectations European PV power capacity, both in present and future scenarios. As an immediate application of the model and the scenarios This applies also for the case of wind power, whose installations are developed, we have assessed the power generation increase mainlyallocatedintheMediterraneanandNorthSeacountries. derived from the 20–20–20 national commitments that could be Apart from the administrative factor, the role of the determining expected from a climatic point of view. For that, the mean annual factorswithineachcountrycanalsobeappreciated,forinstance power production values obtained under both the 2012 and the regarding the overall southward (northward) concentration of PV 2020 scenarios using the simulated 2000–2012 time series of the (wind) power installations. atmospheric variables were compared. Note that the results from Fig. 4 shows the characteristics of the unknown/new wind this analysis do not account for possible changes in climate. power parks needed to complete the 2012 and 2020 scenarios Fig. 5 reveals that the wind power production values obtained depicted in Fig. 2d and f as obtained with the approaches 2 and when the unknown/new wind fleets are designed following the 3 described in Sections 3.2.2.2 and 3.2.2.3 respectively. The approach 1, which does not include any optimization, are lower selected turbines in each case are described in Table 4 where, than the values obtained with the approach 3 that optimizes the despite the clear predominance of some models over the others ratio of production to costs, which itself provides lower values observed in Fig. 4, a great variety of heights, sizes and nominal than those obtained with the approach 2 that optimizes only speeds of the selected turbines can be appreciated. production, as expected from the very nature of these approaches. S. Jerez et al. / Renewable and Sustainable Energy Reviews 42 (2015) 1–15 9

Fig. 4. Characteristics of the unknown wind power parks needed to complete the (a and c) 2012 and (b and d) 2020 scenarios obtained with the approaches (a and b) 2 and (c and d) 3 described in the text. The gray shadows depict the number of turbines to be placed in each grid cell (N) and the colored bubbles indicate the turbine model selected (the colored numbers listed in the left hand side of each plot refer to the ID numbers of the turbine models listed in Table 4). (a) 2012 unknown WP parks (App. 2), (b) 2020 new WP parks (App. 2), (c) 2012 unknown WP parks (App. 3) and (d) 2020 new WP parks (App. 3). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

This is more visible under the 2020 scenario than using the 2012 wind power case, this analysis reveals a quasi perfect agreement scenario just because the amount of wind power installations with between the respective relative increases in installed power and unknown characteristics is larger for 2020 than for 2012. However, production in each country that may respond to the higher the differences between the latter two approaches are not large in linearity of the solar resource – solar power relationship compared most of the cases (i.e. countries) at the annual scale assessed here. to the wind power case. Also, the solar resource exhibits a weaker The increase in wind power installed in each country and the spatial variability within countries than the wind, which may also corresponding increase in wind power production are correlated contribute to such agreement. However, there are some excep- but do not scale perfectly. This is due to the non-linearity of the tions. In particular, the planned increase of 20% in PV power relationship between installed and produced power in a given installed in France will give rise to an increase of more than 35% in location expressed in Eq. (11), and also, likely, to the sensitivity of PV power production according to our estimations, thus showing a the estimated values aggregated per country to the spatial alloca- large potential of efficiency improvement. tion exercise performed in this work. For instance, in Germany a growth of 8.5% in installed wind power gives rise to a growth between 5% and 7% in power production, while in Spain (both 5. Conclusions and discussion countries produce nowadays similar amounts of wind energy) a 70% of increase in wind power installed leads to an increase in This study presents the CLIMIX model, a tool designed to create wind power production between 80% and 95%. This is not a south- and evaluate gridded spatial scenarios of installed PV and wind north distinct behavior, because other pairs of countries would power provided assumptions of renewable energy deployment provide opposite results (e.g. Portugal vs. France). capacity at the scale of individual countries or smaller adminis- Differences in the PV power production values among the trative entities. This model was applied to the European continent various options are observed in Fig. 6 as well. Option 1 provides for estimating the 2012 spatial distribution of unreported PV and the lowest values and option 2 the highest in all the countries and wind power installations and generating spatially-resolved sce- under both scenarios. However, these differences totally disappear narios for 2020. The model was conceived to be fairly general and regarding 2020 vs. 2012 changes expressed as a percentage. includes some customizable options. It can also be adapted to any Apart from few exceptions, all formulations provide the same region, grid and future plans concerning renewable energy relative increase in PV power production derived from the 2020 commitments. plans, being of the same magnitude as the relative increase in PV The knowledge of the locations of PV plants and wind farms is power installed also in the great majority of the cases, with the necessary to properly estimate power production given that the only exceptions being Portugal and France. Hence, contrary to the resource is spatially heterogeneous and actual production is 10 S. Jerez et al. / Renewable and Sustainable Energy Reviews 42 (2015) 1–15

Fig. 5. Wind power production (units: TWh/year) recorded in the years 2011 and 2012 (empty black circles referred to the left y-axes) and estimated using the created scenarios and the three approaches described in the text (colored circles also referred to the left y-axes) for some European countries. Empty colored circles depict the annual values obtained with the 2012 scenario and the simulated meteorological series corresponding to the year 2012. Solid colored circles depict the mean annual values obtained with either the 2012 or the 2020 scenario and the simulated meteorological series spanning the period 2000–2012. In the right hand side of each plot and referred to the right y-axes, the empty bars represent the increase in the installed wind power and the colored bars represent the increase in the wind power production raisedby comparing the climatological values obtained with the 2020 and 2012 scenarios (i.e. from the values depicted by solid bubbles); both increases are depicted in %. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) nonlinearly linked with climatic conditions. Moreover, it will allow should also help optimizing the deployment planning in space to assess production changes resulting from changes in climate and time in order to maximize benefits for local or state autho- and, simultaneously, changes in climate resulting from the massive rities, or for planning at the scale of Europe. deployment of new renewable energy installation that are planned ]However our objective was not to identify optimal but realistic for the near future by implementing the effect of having renew- distributions for the new plants to be installed (although some able units within climate models. With this motivation, we used optimization exercises were included in the model). Hence the here the 0.44° resolution grid defined for the EURO-CORDEX CLIMIX model takes into account not only spatial gradients in the project so that our created scenarios can be directly used in primary resources (i.e. surface solar radiation and wind speed) but conjunction with the climate simulations coming from such also political and practical information and assumptions (i.e. actual international initiative in future works. The developed model national commitments, forbidden locations, spatial distribution of S. Jerez et al. / Renewable and Sustainable Energy Reviews 42 (2015) 1–15 11

Fig. 6. Photovoltaic production (units: TWh/year) recorded in the years 2011 and 2012 (empty black circles referred to the left y-axes) and estimated using the created scenarios and the three options described in the text (colored circles also referred to the left y-axes) for some European countries. Empty colored circles depict the annual values obtained with the 2012 scenario and the simulated meteorological series corresponding to the year 2012. Solid colored circles depict the mean annual values obtained with either the 2012 or the 2020 scenario and the simulated meteorological series spanning the period 2000–2012. In the right hand side of each plot and referred to the right y-axes, the empty bars represent the increase in the installed PV power and the colored bars represent the increase in the PV power production raised by comparing the climatological values obtained with the 2020 and 2012 scenarios (i.e. from the values depicted by solid bubbles); both increases are depicted in %. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) the population). Nonetheless, it is worth emphasizing that there are were imposed. A more extended, precise (maybe in terms of several aspects of the model susceptible of being improved so that preferred manufacturers in each country) and updated (in particu- they can be considered in future versions or applications of CLIMIX. lar for the refinement of future wind power scenarios) list of wind Regarding the specifications to be provided and assumptions to be turbines to be chosen in each site would also provide more made, we admit some arbitrariness in the definition of small and realistic results. Regarding the methodology, it would be poten- large PV plants, as well as in the estimation of the small-to-large tially interesting to take into account future changes in the popula- ratio in each country (including the fact that we considered it to be tion distribution and to consider somehow the electricity demand constant in time). We also made somewhat arbitrary assumptions factor. regarding the forbidden locations and the identification of different The current version of the presented model also gathers several areas where top limits for the size of the turbines to be installed options for the estimation of the PV and wind power production 12 S. Jerez et al. / Renewable and Sustainable Energy Reviews 42 (2015) 1–15 using the created scenarios. As a matter of validation, we used those recorded in the recent past. Besides, the comparison of the them to estimate PV and wind power production in each European climatological power production values obtained under the 2020 country under the created 2012 and 2020 scenarios. For that, we and the 2012 scenarios revealed substantial increases derived from retrieved the required meteorological series of surface short-wave the 20–20–20 national targets, being overall proportional to the downward radiation, 10-m wind speed and 2-m temperature from planned increases in power installed for PV, but not showing any a hindcast climate simulation spanning the period 2000–2012 similar linearity in the case of wind power. performed over the same grid where the scenarios were devel- This latter result further highlights the importance of the oped. Despite the aforementioned improvable aspects of the spatial allocation exercise performed in this work for reliable model and the reported biases in the hindcast simulation, this future projections of power production. Although the CLIMIX training exercise provided reasonable values of power production model is probably not as realistic as to be used in operative in each country under the 2012 scenarios, since these agree with prediction systems, it showed sufficiently accuracy as to consider

Fig. A1. (a and b) Observed values of surface short-wave downward radiation (SSWDR; left panels) averaged over the period 2000–2007 and of 10-m wind speed (10-m WS; right panels) averaged over the period 2000–2012. These data were retrieved from the GEBA and the ISDLITE datasets respectively. (c and d) Biases of the hindcast simulation when reproducing these magnitudes, expressed in %. (e and f) Taylor diagrams where the simulated and the observed spatial patterns of both magnitudes are compared, including all the observational points in Europe (black points) and also per country (colored points), but only for those countries where more than five observational points were available. (A color version of this figure is provided in the web version of this article.) S. Jerez et al. / Renewable and Sustainable Energy Reviews 42 (2015) 1–15 13 the created scenarios in future studies focused on climate change Europe having a horizontal resolution of 0.44° both in latitude impacts on renewable energy production with high confidence. and longitude (roughly 50 km). In the vertical, 32 uneven levels, more closely spaced near the surface, were considered. We used the climatological patterns of surface short-wave down- Acknowledgments ward radiation (SSWDR) and 10-m wind speed (10-m WS) (Fig. 1cand d) retrieved from this hindcast simulation for the creation of the This work was supported by the internal DSM Energy program spatial scenarios of power installed. Instrumental records of both of the Atomic and Alternative Energy Commission (CEA). We thank variables over Europe were used for their validation. Monthly observa- Laurent Letinois (INERIS) for his contribution to the development tions of SSWDR were obtained from the Global Energy Balance of the population database used in this work, Francisco J. Gomariz Archive (GEBA) [38,39] for the period 2000–2007. For the 10-m WS Castillo and M. José Castejón (F-IEA, University of Murcia) for their we had at our disposal the 3-h ISDLITE series [40] for the whole period help with GIS issues, J.-Y. Peterschmitt and A. Stegehuis (LSCE) for of the simulation. Fig. A1a and b depicts the observed climatologies of their help post-processing the GEBA data, and V. Schmidt-Seiwert each variable at the locations of the observational networks; Fig. A1c and R. Binot (BBSR) for providing the locations of the largest PV and d provides the biases of the simulated climatologies considering plants in a friendly format. thenearestgridpointtosuchlocations.Ingeneral,anoverall overestimation of both variables across Europe (except for SSWDR in western areas such as Portugal and UK) can be recognized. Appendix A. The hindcast climate simulation For SSWDR (Fig. A1c), percentage biases are generally largest in the mountainous regions around the Alps and the Balkans, where they The hindcast climate simulation used in this work was per- sporadically reach 60–80%, while they remain below 20% in the rest of formed with the regional climate model WRF V3.3.1 [21] driven the domain. The overestimation of SSWDR is a long standing problem and nudged by the reanalysis ERA-Interim [22]. The physical in many climate models and over several model generations [41–43], parametrization schemes are those used for the EURO-CORDEX partially due to the still moderate accuracy of the modeled cloudiness simulations [16,37]. The simulation spans the period 2000–2012 [44]. Also the lack of solar absorption in the atmosphere caused by (plus one year of spin-up that was discarded) providing 3-h time- underestimated water vapor and aerosol absorption leads often to the series of the prognostic variables. The spatial domain covers overestimation of cloud free insolation [45,46].

Fig. A2. Left panels depict observational values from the 3-h ISDLITE wind speed series for the period 2000–2012, but having extrapolated the wind speed from the original 10-m to 90-m of altitude using Eq. (7). From these extrapolated 3-h series, we have retrieved only those values of the wind speed above 3.5 m/s and below 25 m/s, as these are common cut-in and cut-off speeds of the wind turbines (i.e. they define a representative turbine working regime, TWR). After this post processing and selection of data, (a) depicts the mean values of the series, and (c) the % of values retrieved from the whole series, i.e. the percentage of time-steps of the whole period that would actually contribute to the production of wind power. After the same post processing and selection of data from the simulated series, (b) depict biases in the mean values expressed in %, and (d) the difference between the simulated and the observational series in the percentage of time-steps of the whole period that would actually contribute to the production of wind power. (A color version of this figure is provided in the web version of this article.) 14 S. Jerez et al. / Renewable and Sustainable Energy Reviews 42 (2015) 1–15

For 10-m WS (Fig. A1d), large biases up to 80% appear either of the instrumental databases (e.g. temporal gaps, sparse spatial sporadically or recurrently in most of the countries, again espe- coverage and scarce length for multi-decadal studies) for this kind cially around the Alps and in eastern sectors. Only in particular of assessments [54]. areas (France, UK) they seem to be more constrained to the range – 20 40% (see also Ref. [36], who found mean biases of about 20% References over Central/Northern Europe). The 10-m wind biases in WRF are well known and have been attributed to the absence of account of [1] World Energy Outlook 2013. International Energy Agency. ISBN:978-92-64- orography roughness, which helps explaining that in Alpine areas 20130-9. the resolution used here does not allow a proper simulation of [2] Moreno B, López AJ. The effect of renewable energy on employment. The case – winds. Fig. A2 provides a more detailed assessment of the errors of of Asturias (Spain). 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