D4.5 Augmented open source highRES-Europe, JRC-EU-TIMES and micro-level model and scenario dataset around social and ecological impacts

HORIZON EUROPE PROGRAMME – TOPIC HORIZON-CL5-2021-D3-03-05 Wind energy in the natural and social environment Research and Innovation action (RIA) WIMBY Wind in My Backyard: Using holistic modelling tools to advance social awareness and engagement on large wind power installations in the EU Grant Agreement No. 101083460 Starting date: 1st January 2023 – Duration: 36 months Deliverable D4.5 Augmented open source highRES-Europe, JRC-EU-TIMES and micro-level model and scenario dataset around social and ecological impacts WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 2 of 91 DOCUMENT INFORMATION Deliverable number D4.5 Deliverable title Augmented open source highRES-Europe, JRC-EUTIMES and micro-level model and scenario dataset around social and ecological impacts Work Package WP4 Deliverable type R Dissemination level P Due date 30.06.2025 (Month 30) Pages 91 Document version 4.0 Lead author(s) James Price, UCL Contributors Guillermo Valenzuela-Venegas, UiO; Oskar Vågerö, UiO; Marianne Zeyringer, UiO; Meixi Zhang, PSI; Evangelos Panos, PSI; Alena Lohrmann, ETH; Wendy Reyes-Calle, ETH; Ruihong Chen, ETH; The WIMBY project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains. WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 3 of 91 DOCUMENT CHANGE HISTORY Version Date Author Description DRAFT 0.1 14.03.2025 Oskar Vågerö, UiO Creation 0.2 06.06.2025 James Price, UCL; Guillermo ValenzuelaVenegas, UiO; Oskar Vågerö, UiO Consolidation of input from contributors FIRST PEER REVIEW 1.0 12.06.2025 Russell McKenna, PSI/ETH Proofreading and peer review 1.1 20.06.2025 James Price, UCL; Guillermo ValenzuelaVenegas, UiO; Oskar Vågerö, UiO; Marianne Zeyringer, UiO; Meixi Zhang, PSI; Evangelos Panos, PSI; Alena Lohrmann, ETH; Wendy Reyes-Calle, ETH; Ruihong Chen, ETH Consolidation of input from reviewers SECOND PEER REVIEW 2.0 16.06.2025 Andrea N. Hahmann, DTU Second peer review 2.1 20.06.2025 James Price, UCL; Guillermo ValenzuelaVenegas, UiO; Oskar Vågerö, UiO; Marianne Zeyringer, UiO; Meixi Zhang, PSI; Evangelos Panos, PSI; Alena Lohrmann, ETH; Wendy Reyes-Calle, ETH; Ruihong Chen, ETH Consolidation of input from reviewers COORDINATOR APPROVAL 3.0 25.06.2025 Luis Ramirez, UU Coordinator review WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 4 of 91 3.1 27.06.2025 James Price, UCL; Guillermo ValenzuelaVenegas, UiO; Oskar Vågerö, UiO; Marianne Zeyringer, UiO; Meixi Zhang, PSI; Evangelos Panos, PSI; Alena Lohrmann, ETH; Wendy Reyes-Calle, ETH; Ruihong Chen, ETH Consolidation of input from coordinator 3.2 27.06.2025 Luis Ramirez, UU Coordinator approval FINAL VERSION 4.0 30.06.2025 Stella Arapoglou (VUB) Format review, version ready for submission WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 5 of 91 SHORT ABSTRACT FOR DISSEMINATION PURPOSES Abstract This report describes the development and application of energy systems modelling across multiple spatial scales to generate a set of scenarios which quantitatively assess the system level implications of key local factors that shape wind power siting. At the macro-scale (European continent) this includes the further development, linking and application of two models, JRC-EU-TIMES and highRES-Europe, to assess these implications for Europe’s transition to net-zero. This is combined with a local or micro-scale modelling approach which is developed and applied to a case study for the Styria region of Austria, to capture details the former models cannot. Where possible we make a concerted effort to ensure model transparency and result reproducibility. Initial insights from the scenario modelling show that greater prioritisation of social and environmental protections in wind siting can lead to a vastly different role for onshore wind across Europe and systems that are potentially up to nearly 15% more costly in 2050. We demonstrate that the decade 2025-2035 is crucial for scaling up renewable deployment in the electricity supply to help decarbonise the demand and meet the intermediate climate change mitigation targets. Our micro-scale analysis highlights a clear trade-off between land availability and economic efficiency, where greater energy production potential may come at the expense of substantially higher infrastructure investment, depending on the scenario. The intention is that these scenarios are further analysed in WIMBY Deliverable 4.6. WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 6 of 91 TABLE OF CONTENTS EXECUTIVE SUMMARY .......................................................................................... 14 1. INTRODUCTION ...............................................................................................16 2. THE MODELS ..................................................................................................... 17 2.1 highRES-Europe ...................................................................................... 17 2.1.1 Spatial disaggregation .................................................................................................................................. 18 2.1.2 Existing electricity system infrastructure ......................................................................................... 19 2.1.3 Bias-correction .................................................................................................................................................... 21 2.1.4 Solar PV advancements .............................................................................................................................. 23 2.1.5 Wind energy related employment ...................................................................................................... 24 2.1.6 Advancements in transparency and reproducibility ............................................................ 25 2.1.7 International interconnection updates ............................................................................................ 28 2.2 JRC-EU-TIMES......................................................................................... 28 2.2.1 Granularity in wind technology modelling .................................................................................... 30 2.2.2 Model extension in PtX conversion sectors .................................................................................... 31 2.2.3 Improvements in end-use consumer sector modelling .................................................... 32 2.2.4 Comprehensive policy implementation ......................................................................................... 33 2.2.5 Model Transparency and Reproducibility ...................................................................................... 35 2.3 Micro-scale modelling ......................................................................... 35 2.3.1 Wind farm layout generation .................................................................................................................. 36 2.3.2 External grid connection model .............................................................................................................. 41 3. SCENARIO MODELLING .................................................................................. 42 3.1 Model linkage ......................................................................................... 42 3.2 Land availability scenarios ................................................................ 44 3.2.1 Technical exclusions ....................................................................................................................................... 45 3.2.2 Environmental exclusions ........................................................................................................................... 47 3.2.3 Social exclusions ............................................................................................................................................... 50 3.2.4 Land availability in the spatial scenario combinations ...................................................... 53 3.3 Macro-scale scenario development ................................................ 54 3.3.1 Whole energy system scenarios ........................................................................................................... 54 3.3.1 Additional boundary conditions applied to highRES-Europe ......................................... 56 3.4 Micro-scale scenario development .................................................. 57 4. RESULTS ........................................................................................................... 59 4.1 Macro-scale modelling scenario results .......................................... 59 4.1.1 JRC-EU-TIMES results serve as boundary conditions for highRES-Europe ........... 59 4.1.2 Scenario Variations in energy supply and Power Conversion Sector ...................... 62 4.1.3 National-level cumulative new wind capacities trajectories and near-term challenges................................................................................................................................................................................ 65 WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 7 of 91 4.1.4 Cost and design implications for Europe’s electricity system in 2050 .................... 68 4.2 Micro-scale modelling scenario results ........................................... 74 5. SUMMARY AND CONCLUSIONS .................................................................... 77 5.1 Macro-scale ........................................................................................... 77 5.1.1 The pathway to a carbon-free Europe-wide energy system ......................................... 77 5.1.2 Cost and design implications for Europe’s electricity system in 2050 .................... 78 5.2 Micro-scale ............................................................................................. 79 REFERENCES ........................................................................................................... 81 ANNEX .................................................................................................................... 88 WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 8 of 91 LIST OF PARTNERS N o Logo Name Short Name Country 1 VRIJE UNIVERSITEIT BRUSSEL VUB Belgium 2 DANMARKS TEKNISKE UNIVERSITET DTU Denmark 3 INTERNATIONALES INSTITUT FÜR ANGEWANDTE SYSTEMANALYSE IIASA Austria 4 UNIVERSITÄT FÜR BODENKULTUR WIEN BOKU Austria 5 UNIVERSITETET I OSLO UiO Norway 6 NAZKA MAPPS BVBA NAZKA Belgium 7 KELSO INSTITUTE EUROPE GEMEINNÜTZIGE GMBH KIE Germany 8 DEEP BLUE SRL DEEP BLUE Italy 9 UNIVERSITEIT UTRECHT UU Netherlands 10 POLITECNICO DI TORINO POLITO Italy 11 UNIVERSITÀ DEGLI STUDI DI PALERMO UNIPA Italy 12 APREN-ASSOCIAÇÃO PORTUGUESA DE ENERGIAS RENOVAVEIS APREN Portugal 13 MULTICONSULT NORGE AS MCN Norway 14 EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZÜRICH ETH Zürich Switzerland 15 PAUL SCHERRER INSTITUT PSI Switzerland 16 UNIVERSITY COLLEGE LONDON UCL United Kingdom WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 9 of 91 ABBREVIATIONS Acronym Description AEP Annual Energy Production BECCS Bioenergy with Carbon Capture and Storage BtL Biomass-to-Liquids CCS Carbon Capture and Storage CDDA Common Database on Designed Areas CLC Corine Land Cover COPs Coefficients of Performance DAG Directed Acyclic Graph DC Direct Current DEM Digital Elevation Model EEA The European Economic Area EED Energy Efficiency Directive EF Emission Factor ENTSO-E European Network of Transmission System Operators for Electricity EPBD Energy Performance of Buildings Directive ES Emission Standards ETS European Trading System ERA5 ECMWF Reanalysis Version 5 FT Fischer-Tropsch GA Genetic Algorithm GEM Global Energy Monitor GHG Greenhouse Gases GIS Geographical Information Systems GWA Global Wind Atlas highRESEurope the high temporal and spatial Resolution Electricity System model IUCN The International Union for Conservation of Nature LULUCF Land Use, Land-Use Change and Forestry MCDA Multi-Criteria Decision Analysis MUSA MUlti-criteria Satisfaction Analysis NECPs National Energy and Climate Plans NUTS Nomenclature of territorial units for statistics OSM Open Street Map WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 16 of 91 1. INTRODUCTION As of 2022, wind power already supplies almost 14% of Europe’s electricity generation [1]. Some countries go well beyond this figure, with, for example, 57% of electricity generation in Denmark [2], 29% in the UK [3], and 27% in Germany [4] coming from wind in 2023. Going forward, given its cost competitiveness and maturity, wind is expected to play a key role in Europe achieving its goal of climate neutrality by enabling the deep decarbonisation of electricity generation. Indeed, the importance of netzero carbon electricity is only set to grow with the drive to electrify more of the energy system, such as transport and heat. However, the growth of wind power across Europe faces many challenges, see [5] for a detailed review. Many of these challenges stem from the concerns of local stakeholders regarding the potential impacts of wind development on, for example, landscape aesthetics, human wellbeing (i.e., due to noise or shadow flicker), and wildlife. It is the relative prioritisation amongst stakeholders and policymakers of these local, granular factors, balanced with the national and continent-wide objectives of mitigating climate change in a cost effective and secure manner, that will ultimately determine the role wind will play in helping to deliver net-zero. One of the primary objectives of WIMBY is to understand the system level implications of these trade-offs by assessing their impact on the integration of wind power across the European energy system. The goal of this report is to respond to this challenge by coupling a set of energy planning models across local (micro) to continental (macro) scales that capture key social and environmental factors that shape wind resource availability. We aim to elaborate the system cost and design trade-offs across these scenarios and quantify the contribution of wind to Europe’s net-zero future. At the macro level we bring together the high spatial and temporal resolution electricity model for Europe (highRES-Europe) and the whole energy system model JRC-EU-TIMES. We frame our analysis using land availability scenarios for wind deployment which reflect a range of potential futures where stakeholder’s priorities vary with respect to the social and environmental aspects of wind power. The Geographical Information System (GIS) part of highRES-Europe quantitatively models these land WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 17 of 91 availability constraints and provides them to JRC-EU-TIMES which optimises the transition of the whole European energy system to net-zero. Boundary conditions for the electricity system in 2050 are then used to constrain highRES-Europe, together with the land availability scenarios, to design spatially detailed net-zero electricity systems. In addition, the land scenarios are also used to set boundary conditions for the micro-scale modelling, which here focuses on a case study for the Austrian region of Styria. Where possible we make a concerted effort to ensure model transparency and result reproducibility. This report is structured as follows: the next section provides a description of the microand macro-scale models and their development undertaken as part of WIMBY. Section 3 describes the scenario modelling approach that we take and the model coupling. Finally, we highlight a selection of results in Section 4 and provide some brief conclusions in Section 5. 2. THE MODELS In this section we describe the models used in this Deliverable and detail the further augmentations made to them as part of WIMBY. These improvements serve to both facilitate the scenario implementation described in Section 3 as well as improve transparency and reproducibility. 2.1 highRES-Europe The high spatial and temporal Resolution Electricity System model for Europe (highRES-Europe) is an electricity systems modelling framework specifically designed for modelling high shares of variable renewable electricity generation in Europe [6], [7]. The model simultaneously makes planning decisions, i.e., location and capacity, for a snapshot year and hourly operational decisions within that year, i.e., dispatch, for electricity generation, storage, and transmission, ensuring that supply meets demand at least cost (for a more detailed overview of the model equations, see Supplementary Information in [7]). This optimisation is usually constrained to meet deep decarbonisation objectives such as net-zero. The version of highRES-Europe used in this project models 25 EU countries (excluding Cyprus and Malta) plus Norway, Switzerland, and the UK. WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 18 of 91 As part of WIMBY, the teams at UCL and UiO have made significant improvements to highRES-Europe (see the base version in [6] for more details). These include the integration of the social and environmental constraints from WIMBY WP1 and WP2, enhancements to the transparency and reproducibility of the framework, and improvements to the model’s spatial resolution. These augmentations, detailed in what follows, serve to enable and enhance the delivery of the overall objective of assessing the implications of social and environmental restrictions on the role of wind power in Europe achieving net-zero 1 . 2.1.1 Spatial disaggregation A key aspect of capturing the location specific factors that shape where wind power can be sited and, in turn, their implications on overall system design is being able to model deployment with sufficient spatial granularity. Furthermore, modelling variable renewables like wind and solar at higher spatial detail also helps better represent the variation in weather conditions across a country, thereby capturing whether specific regions offer more preferential conditions for renewables. With this in mind, the one node per country representation of the European electricity system in highRES-Europe [6] has been significantly improved, particularly for onshore/offshore wind and solar PV siting. All other generation, storage and transmission planning remains at the one node per country level. Following the testing of highRES-Europe at various resolutions and considering the trade-offs between gains in planning fidelity (and the usefulness to stakeholders) and computational expense, we concluded that a good balance is struck by modelling wind and solar deployment at the Nomenclature of Territorial Units for Statistics (NUTS) 2 level. 1 The enhanced version of the highRES-Europe used in this report is publicly available in: https://github.com/highRES-model/highRES-Europe-WF/releases/tag/WIMBYD4.5 https://github.com/highRES-model/highRES-Europe-GAMS/releases/tag/WIMBYD4.5. WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 19 of 91 Figure 1: Spatial coverage of countries (delineated by different colours) and NUTS2 regions (thin black lines) modelled in highRES-Europe. Figure 1 shows the spatial coverage of the European power system modelled in highRES-Europe, complete with the reference transmission network topology taken from ENTSO-E’s Ten Year Network Development plan from 2024 [8]. Subject to land availability, onshore wind and solar PV deployment can take place in any of the ~280 NUTS2 regions shown. 2.1.2 Existing electricity system infrastructure To enhance the model’s representation of the future electricity system, we implemented improvements that incorporate the existing capacity infrastructure in 2050 for renewable sources at the NUTS2 level and for nuclear at the country level. This approach enables the model to differentiate between existing and new installed capacity when making WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 20 of 91 investment decisions, resulting in a system configuration more aligned with reality. We estimate the existing capacity based on the Global Energy Monitor (GEM) [9], [10], [11]: an open-access platform, which provides detailed information on power generation facilities worldwide. This database contains information on onshore wind, offshore wind, solar, and nuclear installations, including project names, operational status, precise locations (latitude and longitude), and commissioning and decommissioning dates. To determine the existing facilities in 2050, we first extract the power plant data for the 28 European countries included in the model (25 EU countries plus Norway, Switzerland, and the UK). We then apply a projection approach to these entries based on their commissioning dates and expected operational lifetimes. The commissioning date is directly obtained from the provided information in the GEM database. The technology-specific lifetime is defined depending on the technology being considered. For renewable technologies, we set a lifetime of 30 years, while for nuclear infrastructures, we define a lifetime of 41 years based on the 75th percentile of the retired plant lifetime from GEM. Using these values, we estimate the retirement dates for the power plants currently in operation and under construction, as listed in GEM, by adding the expected lifetime to their commissioning dates. Lastly, we filter out all plants projected to retire before 2050. To aggregate the estimated existing infrastructure to the NUTS2 level, we allocate each plant to its corresponding NUTS2 region by overlaying latitude and longitude coordinates in GEM with the appropriate region. For onshore plants, we directly use the NUTS2 region that coincides with their specific locations, while for offshore plants, we associate the nearest coastal NUTS2 region as their reference area. With this information, we aggregate the filtered capacities by NUTS2 regions, except nuclear capacities, which are aggregated at the country level. Figure 2 illustrates the spatial distribution of existing capacity projected to remain operational in 2050. Although onshore wind capacity is present throughout Europe, Nordic countries exhibit a higher concentration. Solar capacity is predominantly located in southern regions, with Spain showing the highest concentration. Offshore wind installations are mainly sited in the WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 21 of 91 North Sea region, whereas nuclear facilities are limited to a few countries, notably Finland, France, Slovakia, and the UK. Figure 2: Estimated existing capacity by technology in 2050. 2.1.3 Bias-correction Accurate representation of wind potential across NUTS2 regions requires a reliable dataset that correctly estimates the wind speeds. For this purpose, highRES-Europe employs ERA5 reanalysis data to calculate the wind potential at the required height of the wind turbines. This dataset provides high-resolution weather data (0.25º×0.25º, roughly 31 km) [12], such as wind speed at 100 m, amongst others, that are used to calculate the renewable energy potential at the grid cell level, followed by an aggregation step of this data to the corresponding NUTS2 regions. WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 22 of 91 Although this dataset provides a good representation of the wind speed, it is well known to contain bias in specific areas, particularly in regions with complicated topography [13]. To mitigate this issue, bias-correction ratios derived from WIMBY Deliverable 1.1 are utilised on the ERA5 wind speed data [14]. These ratios were developed by averaging microscale wind speed values from the Global Wind Atlas (GWA) [15]over a 0.025°×0.025° grid and then dividing them by the corresponding ERA5 values (see Deliverable 1.1 for further details). Then, by applying these ratios, the ERA5 wind speeds are corrected and downscaled to a resolution of 0.025º×0.025º. To ensure consistency with other weather variables used in highRES-Europe, the resulting values are resampled back to the original 0.25°×0.25° resolution. Figure 3 shows the differences between the bias-corrected wind speed at 100 m using GWA ratios and the original ERA5 data. Grid cells coloured red highlight where ERA5 underestimates wind speed, while grid cells coloured blue highlight where wind speed is overestimated. The figure reveals that ERA5 tends to underestimate the wind speed throughout most of Europe, with particularly significant underestimations noted in mountainous regions, such as Norway and the Alps [13]. WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 23 of 91 Figure 3: Difference between the mean ERA5 wind speed (m/s) corrected by GWA ratios and the original ERA5 data at 0.25º×0.25º resolution. The red colour highlights areas where the wind speed from ERA5 is underestimated, and the blue colour indicates where it is overestimated. 2.1.4 Solar PV advancements While not the specific focus of this project and deliverable, solar PV is expected to play a critical role in enabling a net-zero emission European electricity and energy system. As part of WIMBY, we have sought to improve the representation of solar PV in highRES-Europe. Firstly, similar to wind power, solar PV also faces a range of technical, social, and environmental challenges related to its siting. On the technical side, we implemented a slope restriction using the Copernicus Digital Elevation Model at 90 m resolution [16], thus aligning our elevation model with that used elsewhere in WIMBY. We set the slope restriction such that ground- WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 24 of 91 mounted solar PV can be built in areas with a slope less than or equal to 11% or 6.3 degrees based on [17]. In terms of environmental protection, we have set ground-mounted solar PV so it is restricted from The International Union for Conservation of Nature (IUCN) category Ia-IV (taken from the Common Database of Designed Areas for Europe from 2023 [18] and for the UK from 2020 [19]) and Natura 2000 areas from 2020 [20]. For the former dataset, the European and UK data years differ because of the UK leaving the EU and ceasing to contribute to the database. This restriction is aligned with the medium level of the environmental restrictions for wind power, as explained later in this deliverable. For social restrictions, we have utilised the CORINE land cover 2018 [21] dataset at a 100 m resolution to exclude land covered by select urban areas, forests and semi-natural areas, water bodies, and wetlands. To account for concerns regarding food production, we use novel data [22] on agricultural intensity for Europe to restrict deployment from medium and high intensity crops and grasslands. 2.1.5 Wind energy related employment Although wind energy come with certain negative impacts, motivating the social and environmental exclusions in Section 3.2, it can also have positive effects, such as new employment opportunities both locally and nationally. Part of the work conducted in WIMBY D2.10 [23] included assessing the impact of wind energy deployment on job creation in the wind energy industry. To study this, a job creation model was developed, based on existing literature on the topic. The model considers three types of jobs (direct, indirect, and induced) created from wind power activities in five different stages of its lifespan (development, construction, manufacturing, operation and maintenance (O&M), as well as decommissioning). The resulting employment factors in the different lifecycle stages can be coupled to energy system models, such as highRES-Europe. Table 1: Employment factors for wind energy related jobs, based on Bucha et al. [23] Stage Direct Indirect Induced Total Geographical scope of jobs Development 0.69 0.59 0.59 1.87 local / national WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 25 of 91 Manufacturing α 5.68 3.65 international Construction β 3.08 4.16 local / national O&M γ 9.36 12.53 local / national Decommissioning δ 0.9 1.01 local / national Wind energy related jobs are calculated as a function of the number of projects, and the employment factors are shown in Table 1. The employment factor for direct jobs related to manufacturing, construction, O&M, and decommissioning is a function of the turbine size and whether it is an onshore or offshore turbine. 𝛼 =13.37−9.5⋅𝑂𝑆 (1) 𝛽 =9.46−3.65⋅𝑂𝑆−4.07 (2) 𝛾 =19.38+0.92⋅𝑇𝐶−15.74 (3) 𝛿 =2.82−2.11⋅𝑂𝑆 (4) Equations 1-4 present the resulting direct employment factors, where OS equals to one for an offshore wind turbine and zero for an onshore wind turbine, and TC represents the turbine capacity in MW. The number of jobs generated in the wind energy sector is hence derived from turbines of different sizes and scales very poorly when aggregated to a larger geographic area. An increase from 3 MW to 6 MW for an onshore turbine only generates an additional 2.5 job-years (+5%). Energy system model output on capacity deployment is typically presented for a larger geographical region (e.g. NUTS2 or country-level). Applying the values in Table 1 therefore needs to include assumptions on how many wind power plants of a certain size is present in the geographical region. To account for this, we take the same turbine sizes used when generating the capacity factors (for onshore: 3 MW and for offshore: 15 MW) and estimate the number of turbines per NUTS2 region. The wind energy employment estimation is calculated after the model results have been generated and provides additional information on the implications of the results, without being part of the model optimisation. The resulting wind energy related employment, based on the modelled scenarios described in Section 3 will be presented in deliverable 4.6. 2.1.6 Advancements in transparency and reproducibility WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 32 of 91 into the FT synthesis or methanol produced using renewable electricity or steam to be converted and refined further to produce crude oil. Figure 6: Graphic representation of the processes for each type of synthetic fuel. The implementation also reflects updated cost assessments and the integration of emerging technologies in electrolysis and e-fuel production. Furthermore, the model now includes endogenous trade of hydrogen (H₂) and PtX energy carriers, allowing for a more dynamic representation of global energy flows. Additionally, the resource potentials for and respective levelized cost of hydrogen and PtX imports from outside the EU have been revised based on the latest data from the PtX Atlas [29], [30], enhancing the reliability of cross-border supply projections. 2.2.3 Improvements in end-use consumer sector modelling The end-use consumer sectors drive the demand for energy consumption requirements, hence is a crucial factor in projection of the necessity for renewable electricity and the contribution of wind. The updated model features extensive improvements across all major sectors. In the industry sector, enhancements include detailed material flow tracking, revised metal scrap costs, modelling of iron pellet trade within and beyond Europe, and expanded technology granularity. New options for hydrogen use in process heat and updates in primary and secondary aluminium production pathways are also included, alongside refined cost and potential estimates for CO₂ capture technologies—bringing the total WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 33 of 91 number of modelled industrial technologies to over 400. In the buildings sector, the model now includes a more granular representation of electrical appliances, a new endogenous building stock evolution submodule, and geographically differentiated coefficients of performance (COPs) for heat pumps. Corrections were also made to address inconsistencies in technology characterizations. For the transport sector, the model reflects a more detailed treatment of aviation technologies based on travel distances and separates demand into national, intra-EU, and extra-EU segments. 2.2.4 Comprehensive policy implementation JRC-EU-TIMES also takes into consideration the spectrum of current EU directives and regulations on energy and climate. Structural changes were implemented to account for policy instruments such as bans, mandates, standards, and financial support mechanisms embedded in the EU legislation, including the introduction of ETS2 for the buildings and transport sectors. Table 2 includes legislations implemented until 1.1.2024 and are foreseen as pillars of legislation from the EU Green Deal, ReFuel Aviation, and REPower EU Plan (EU2023/435) which would alter the trajectory of the energy system. Though this subsection will not dive into the specificities of the legislation, which can be referred to in the citations, it will shortly elaborate on the method of policy implementations in a bottom-up technology-rich model. Table 2: Simplified representation of policy implementation targeting Greenhouse Gas Emissions. # Directive Simplified Representation 1 ETS and ETS2 Directives (EC2003/87 to EU2023/959) ∑𝐸𝑇𝑆𝐶𝑂2𝑟,𝑡,𝑠 𝑟∈𝑅 ≤𝐶𝐴𝑃𝑆 2 GHG effort sharing (EU2018/842, EU2023/857) until ETS2 is operational 𝐸𝑟,𝑡 −𝐸𝑇𝑆𝐶𝑂2𝑟,𝑡,𝐸𝑇𝑆 ≤𝐶𝐴𝑃𝑟,𝑡 3 CO2 standards cars (EU2019/631, EU2023/851) ∑𝐹𝑖,𝑡 ⋅𝐸𝐹𝑖 𝑛 𝑖=1 ≤𝐸𝑆𝑡⋅𝑉𝑡 4 CO2 standards heavy duty vehicles (EU2019/1242) ∑𝐹𝑖,𝑡 ⋅𝐸𝐹𝑖 𝑛 𝑖=1 ≤𝐸𝑆𝑡⋅𝐻𝑉𝑡 Overarching indices are r as region/country, t as time/year. Variables E stands for the Greenhouse Gas Emissions, 𝐸𝑇𝑆𝐶𝑂2 as CO2 covered within the ETS, 𝐹 as the consumption of a certain fuel type, 𝐸𝐹 as the emission factor, 𝐸𝑆 as emission standards (in gCO2/km) and 𝑉 and 𝐻𝑉 as stock of standard WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 34 of 91 passenger vehicles (in passenger kilometre) and heavy-duty vehicles (in passenger kilometre) respectively. An example is the vehicles’ emissions standards (Eq. 3 and Eq. 4). The emissions of the vehicles are dependent on their fuel efficiencies; thus the constraint cannot be directly on the efficiency as these are hard technical data. Instead of constraining individual vehicles, we apply an overall emission constraint on the whole stock. This approach leaves flexibility for the model to choose the vehicle type so long as it fulfils the overall emission constraint. Table 3: Simplified representation of policy implementation targeting Energy Supply. # Directive Simplified Representation 1 EED energy efficiency (EU2023/1791) ∑∑𝐹𝑟,𝑠,𝑓,𝑡 ≤(1−𝑅) ⋅∑∑𝐹𝑟,𝑠,𝑓,𝑡−1 𝑓∈𝐹𝑠∈𝑆𝑓∈𝐹𝑠∈𝑆 2 EPBD buildings energy performance (EU2018/844) ∑∑𝐹𝑟,𝑠,𝑓,𝑡 ≤(1−𝑅) ⋅∑∑𝐹𝑟,𝑠,𝑓,𝑡−1 𝑓∈𝐹𝑠∈𝐵𝑓∈𝐹𝑠∈𝐵 3 EU RED III renewable energy (EU2018/2001 & 2023/2413) 𝐺𝐶𝑟,𝑡 ≥ 𝑝𝑟,𝑡 ⋅(𝐸𝑟,𝑡 −𝐻𝑟,𝑡) 4 Coal phase out 𝐸𝑟,𝑡 =0 ∀𝑟 ∈𝐷𝐾,𝐹𝐼,𝐸𝐿,𝐻𝑈,𝐼𝐸,𝐼𝑇,𝑁𝐿,𝑃𝑇,𝑆𝐾,𝐸𝑆,∀𝑡 ≥2030 𝐸𝑟,𝑡 = 0,∀𝑟 ∈ 𝐷𝐸,𝑆𝐼 ∀𝑡 ≥2050 New indices shown in Table 3 are s for end-use consumer sectors, B for the building sector. 𝐹 represents fuel consumption and 𝑓 for the type of fuel. GC states for Green Certificates produced by renewable energy generation. 𝑅 for the reduction factor stipulated by the directives. 𝑝 as the percentage of renewable energy generation demanded by the directive. 𝐸 as renewable electricity generated, and renewable electricity generated for hydrogen production. It is worth noting, that in order to ensure that the additionality principle [31] is considered for green hydrogen production, a new parameter is created, serving as a form of certificate (GC in Eq. 3) to explicitly account for the renewable portion of heat or electricity production. Such a parameter ensures that when constructing the model constraints for renewable electricity or hydrogen production, they are for direct end-use instead of conversion or transformation purposes. WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 35 of 91 Finally, the individual countries’ emissions mitigation targets and renewable energy targets are collected from the latest National Energy and Climate Plans (NECPs) [32]. The projections for new nuclear power plants planned or under construction follow the national plans declared and collected by the World Nuclear Association [33]. Other major policies implemented in the model include buildings standards, appliances standards, and efficiency directives. We also consider Directives related to the use sustainable fuels in aviation and maritime transport. 2.2.5 Model Transparency and Reproducibility JRC-EU-TIMES has been an open-source model [34], which is based on the TIMES framework developed within the Energy Technology Systems Analysis Program (ETSAP) by the International Energy Agency (IEA) and extensively used by the EU Commission. The TIMES framework lays a solid foundation for the representation of Europe's energy system. The input files with the new data for calibration are listed in the Annex Table 14, and changes in model structure can be found in form of dmp.files on Gitea. With a valid GAMS/CPLEX license, the dmp.files can be solved by running the command scripts to produce gdx. files. The gdx. files are then processed with a gms. script to output the desired results from the model to be printed on the format of a result template in form of excel sheets. The graphs made uses a python script to further visualizes the results from the excel sheets. For a more detailed and specified description of the model, please refer to the Gitea repository 5 . 2.3 Micro-scale modelling In this section, the development of a micro-scale model of wind farm deployment is detailed. For this analysis, ETH has previously proposed contributing with the existing municipal energy system model, RE3ASON. 5 WIMBY/JRC-EU-TIMES - JRC-EU-TIMES - PSI GIT Service WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 36 of 91 However, we decided to develop a more comprehensive model that advances the micro-scale analysis with wind farm deployment. This model incorporates an integrated approach for wind farm design and a grid integration model – both developed on a very high spatial resolution. Compared to our new approach, the RE3ASON model has a lower spatial resolution and considerably lower complexity regarding the wind technology development. Moreover, our new approach can be applied to any location of interest. The new approach was tested for the areas identified as feasible for wind farm deployment. The scope of the analysis is oriented toward the technical feasibility of individual wind farm deployment; therefore, the internal and external cable costs are included in the analysis. The developed micro-scale model includes four stages: i) an optimal wind farm layout is generated for a given area, for which the theoretical annual energy production (AEP) and internal wind farm connection costs are estimated. ii) Dijkstra’s algorithm is applied to determine the most costeffective path to connect the wind farm to the power grid. iii) A multi-criteria decision analysis is conducted to exclude wind turbines with the highest perceived negative impact. iv) AEP and the grid connection costs are recalculated using the remaining wind turbines for the adjusted wind farm layout. 2.3.1 Wind farm layout generation The wind farm layout model combines spatial wind-oriented random layout generation, wake effect modelling, and multi-objective evolutionary optimisation. The methodology of the layout model, together with the grid connection model, is illustrated in Figure 7. The following sections describe the implementation of the model in more detail. WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 37 of 91 Figure 7: Methodology of the spatial wind farm design and internal grid integration. DEM: digital elevation model [16], CLC: CORINE Land Cover 2018 [21], WS: wind speed data [35] with hourly resolution for a year. Spatial wind-oriented random layout generation The initial layout is generated using a wind-oriented heuristic method to generate the initial layout of a wind farm for a given feasible area. After extracting the wind data, the dominant wind direction 𝜃𝜔 is determined from the site-specific wind rose. The first turbine is positioned upwind in alignment with 𝜃𝜔, and the remaining turbines 𝑁𝑡−1 are placed pseudorandomly while constrained by a minimum spacing criterion as: 𝑑𝑖𝑗 𝑑𝑜𝑤𝑛𝑤𝑖𝑛𝑑 ≥4𝐷 𝑎𝑛𝑑 𝑑𝑖𝑗 𝑐𝑟𝑜𝑠𝑠𝑤𝑖𝑛𝑑 ≥4, WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 38 of 91 where D is the rotor diameter and 𝑑𝑖𝑗 is the distance between turbines 𝑖 and 𝑗 in the respective directions. In this study, a minimum turbine spacing of 4 rotor diameters (4D) was adopted based on a balance between spatial constraints and acceptable wake interaction. Although previous optimisation studies [36] suggest that optimal efficiency is typically achieved at larger spacings (7D–11D), using a 4D minimum reflects common industry practice in land-constrained settings and enables the evaluation of denser layout scenarios and therefore is the value using for our analysis [37], [38]. To ensure suitability for different wind conditions and terrain, the model automatically selects a suitable wind turbine type from a list of 10 turbines with varying power ratings, selected from the Wind Power Dataset [39]. This selection is based on the potential AEP, the required initial investment, and the site’s mean wind speed site 𝑣𝑠𝑖𝑡𝑒. Each turbine has two potential hub heights, which are selected according to the local elevation of its siting position. Finally, the extrapolation of the wind speed at hub height 𝑣𝑖(ℎ𝑖) is calculated using the logarithmic wind profile as: 𝑣𝑖(ℎ𝑖)=𝑣𝑟𝑒𝑓 ⋅ln(ℎ𝑘 𝑧0,𝑘) ln(ℎ𝑟𝑒𝑓 𝑧0,𝑘 ), where 𝑧0,𝑘 is the site-specific surface roughness, and 𝑣𝑟𝑒𝑓 is the wind speed at the reference height ℎ𝑟𝑒𝑓. Wake effect modelling and calculation of AEP Estimating the wake deficit is crucial in quantifying the reduction in power output caused by the spatial arrangement of wind turbines—a phenomenon known as the wake effect. In complex terrain, variations in elevation can intensify the wake deficit, making precise calculation even more critical. The effective wind speed for each turbine 𝑣𝑒𝑓𝑓𝑖 is therefore recalculated by accounting for the upstream deficits Δ𝑉𝑗→𝑖 caused by other turbines, as demonstrated in the equation below: 𝑣𝑒𝑓𝑓𝑖=𝑣𝑖(ℎ𝑖)− ∑ Δ𝑣𝑗→𝑖 𝑗∈𝑢(𝑖), where 𝑢(𝑖) is the set of upstream turbines affecting turbine 𝑖. Upstream deficits Δ𝑣𝑗→𝑖 are calculated using the 3D Jensen wake model, which assumes linear wake expansion and momentum deficit [40]. The Jensen WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 39 of 91 wake model, known as the park model, is a widely used model for simulating wind turbine wake effects. Initially formulated for flat terrain, it assumes a linearly expanding, top-hat shape wake with uniform velocity deficit inside the wake cone. This has been expanded to complex terrain, in which the wake follows the symmetry of the terrain at a constant height above the ground. This allows the wake to be aligned with the local topography and wind direction. The velocity Δ𝑣𝑗→𝑖 at a point 𝑖 caused by an upstream turbine 𝑗 is computed as: Δ𝑣𝑗→𝑖 =𝑣∞ [ 2𝑎𝑗 (1+𝑘⋅𝑆𝑗𝑖 𝑅𝑗)2 ] , where 𝑣∞ is the free-stream wind speed, 𝑎𝑗 is the axial induction factor of turbine 𝑗, 𝑆𝑗𝑖 is the downwind distance from turbine 𝑗 to point 𝑖, 𝑅𝑗 is the rotor radius of turbine 𝑗, and 𝑘 is the wake decay constant, typically ranging from 0.04 to 0.075 [40], dependent on the ambient turbulence intensity. Additionally, when multiple wakes overlap, the total velocity deficit at turbine 𝑖 is calculated using the quadratic sum of individual deficits. The resulting values of effective wind speed are used to calculate the total power output at each time step and the AEP as: 𝐴𝐸𝑌 =∑∑𝑓(𝑣,𝜃)⋅𝑃𝑖(𝑣𝑖𝑒𝑓𝑓(𝜃)) 𝜃,𝑣 𝑁𝑡 𝑖=1 , where 𝑓(𝑣,𝜃) is the joint probability distribution of wind speed and its direction. Internal connection costs To calculate internal cable connection costs, we first compute internal cabling design using EDWIN from the TOPFARM toolkit [41]. The layout is generated using a heuristic algorithm that leverages Delaunay triangulation to form a graph and interactively connect turbines in a tree structure toward a local substation node. The algorithm seeks to minimise the total cost by considering factors such as cable length, type, and size, as well as the number of junctions, turning angles, and substation connections. The substation node is located at the centroid of the polygon, and each edge in WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 40 of 91 the tree is assigned to a conductor cross-section. The cross-section and prices are adjusted based on values from the literature, as shown in Table 4 The cable cost is calculated using the resulting cable lengths 𝑙𝑖𝑗 and the corresponding conductor sizes 𝐴𝑖𝑗, which are then used for the calculation of internal cabling costs, according to the equation: 𝐶𝑐𝑎𝑏𝑙𝑒 =∑𝑙𝑖𝑗 ⋅𝑐𝑖𝑗(𝐴𝑖𝑗), where 𝑐𝑖𝑗(𝐴𝑖𝑗) is the cost function dependent on the conductor cross-section size. An illustrative example is shown in Section 4.2. Table 4: Conductors used in the internal topology design [42], [43], [44], [45]. Cross-Section (mm2) Material Rated Current (A) Resistance (Ω/km) CAPEX (k€/km) 150 Aluminum 270 0.206 133 240 Aluminum 360 0.125 196 400 Aluminum 490 0.078 294 800 Aluminum 700 0.040 490 1000 Aluminum 810 0.032 560 Multi-objective evolutionary optimisation All the previously mentioned steps are repeated 25 times for each polygon to identify the optimal wind farm layout in this final step, as shown in Figure 7. This number was selected as an upper limit. In most cases, the optimal layout was identified well below the 25th candidate. To determine the optimal wind farm layout, we apply a metaheuristic multi-objective optimisation, which aims to: • Maximise AEP: max𝑓1=𝐴𝐸𝑃 • Minimise internal cable cost: min𝑓2=𝐶𝑐𝑎𝑏𝑙𝑒 The multi-objective genetic algorithm (GA) is based on an evolutionary machine learning algorithm, which is implemented in the DEAP library of the Python interface [46]. The result of this stage is the site-specific optimal layout of the wind farm, including its characteristics (exact location, type of wind turbines, specific hub height for each turbine, estimated AEP, total internal cabling costs, and substation location). The following section describes the methodology of the external grid connection of the wind farm. WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 41 of 91 2.3.2 External grid connection model We have implemented an innovative algorithm for identifying the shortest path between two points, which, unlike the approach used in WP2, takes into account land cover and terrain constraints. The WP2 model was limited to straight-line connections between points, without considering deviations required by land use or environmental factors, making it less suitable for realistic routing in our context. A spatially explicit Dijkstra’s algorithm identifies the most cost-effective route between each wind farm centroid and the nearest mediumor highvoltage (MV/HV) substation. The algorithm identifies the least-cost path using weights that reflect the relative constructability and ecological impact. It follows the raster-based cost used in spatial planning, as presented in Table 5. The model incorporates slope and elevation in routing decisions using DEM [16]. Furthermore, the model includes structure (tangent, running angle, angled deadend, and non-angled deadend) costs and voltage-specific conductors adapted from real-world transmission planning guidelines [47], [48]. Table 5: Weights for wind farm connection cost modelling [49], [50]. Weights CLC code range Land Cover Type Description 10 6 – 10 Industrial, infrastructure, roads Low cost if corridors exist 20 11 – 15 Cultivated farmland Moderate cost for trenching and accessibility 25 22 – 25 Semi-natural areas, grassland Lower ecological impact 35 26 – 29 Shrubland Slightly higher ecological impact than grassland 45 16 – 21 Forest Higher cost due to clearing and restricted access 100 30 – 34 Wetland High cost due to ecological sensitivity 150 1 – 5 Urban areas Very expensive due to construction limitations 1000 35 – 44 Water bodies Considered impassable The cable types are dynamically assigned based on power capacity and voltage level. Section 4.2 shows an example of the grid connection model result. As mentioned, the model calculates the connection costs to the WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 48 of 91 extending this to include Ia-VI with a 2000 m buffer at the highest setting. All CDDA data is represented as a 100 m resolution raster in our modelling. Secondly, we also restrict the Natura 2000 protected sites, which are designated under the Birds and the Habitats Directives [61], [62], in the medium and high levels, with the latter additionally including a 2000 m buffer. The low case does not see these areas restricted to align with the WIMBY interactive map. The Natura 2000 dataset is ingested into our modelling as a 100 m resolution raster. Thirdly, we leverage the insights on the number of bird and bat species that are vulnerable to collision with wind turbines developed in WIMBY D1.6. For birds, species richness rasters, which depict highly vulnerable and high latent risk, as shown in Figures 10a and b of WIMBY D1.6, are combined to produce a map of the number of bird species deemed highly vulnerable in each 10 km resolution grid cell. For bats, the status of 38 European bat species was obtained from the European Red List of Threatened species [63], with 10 species being classified as threatened (3 as endangered and 7 as vulnerable). Maps of threatened (and therefore vulnerable) species richness were then obtained from occurrence probability layers from SiMoussi [64] at a 10 km resolution. WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 49 of 91 Figure 10: Geospatial overview of eligible areas for wind onshore deployment based on the three environmental scenarios. Next, we sum the species richness rasters for birds and bats together to create a final dataset that contains the total number of species vulnerable to collisions with wind turbines within each grid cell. To convert this map into a set of exclusions, we take a quantile approach to identify three vulnerability thresholds. For each country, we take all grid cells in that country and compute the top 25, 50 and 75% quantiles. These thresholds are then used to mask all cells above that number from wind deployment to represent progressively more restrictive cases from low to high. For instance, low is to be interpreted as removing land areas (grid cells) where the upper quartile of the number of vulnerable species in each country is found. Thus, it is a more permissive case than medium (which blocks the upper 50%) or high (which blocks the upper 75%). WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 50 of 91 The three levels of our environmental exclusion dimension are shown in Figure 10, and Table 8 provides a summary of the data and assumptions involved. Table 8: Environmental exclusions applied in each scenario. Name Description Buffer Dataset IUCN protected areas Low: Ia, Ib, II, III, IV Medium: Ia, Ib, II, III, IV High: Ia, Ib, II, III, IV, V, VI High: 2000 m Common Database on Designated Areas (CDDA) Natura 2000 Low: not included Medium: all sites included High: all sites included High: 2000 m Natura 2000 Number of bird and bat species vulnerable to collision with wind turbines Low: top 25% per country Medium: top 50% per country high: top 75% per country WIMBY D1.6 dataset 3.2.3 Social exclusions For social exclusion, we consider all impacts of onshore and offshore wind from a social perspective and define buffer distances to represent different levels of opposition. Within this dimension, we consider buffers around buildings and residential areas, identified in CORINE Land Cover Polygons 2018 [21] with categories: Continuous urban fabric (1.1.1), Discontinuous urban fabric (1.1.2), Industrial or commercial units (1.2.1), Construction sites (1.3.3), Green urban areas (1.4.1) and Sport and leisure facilities (1.4.2); visual impact on the landscape, defined in WIMBY D2.6 [65], and coastlines buffers. Table 9: Social exclusions applied in each scenario. Category Description Buffer Dataset (incl. code) Onshore exclusion WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 51 of 91 Settlement Excluded area around residential areas due to regulations. Low: 200 m Medium: 1,500 m High: 3,000 m CORINE Land Cover Polygons, 2018 Noise Excluded area around residential areas due to noise. Low: 400 m Medium: 600 m High: 1,500 m CORINE Land Cover Polygons, 2018 Shadow Flicker Excluded area around residential areas due to shadow flicker. Low: 84 m Medium: 1,250 m High: 2,500 m CORINE Land Cover Polygons, 2018 Landscape visual impact Excluded areas based on scenicness categories (ternary map) Low: No exclusion Medium: Category labelled 3 High: Categories labelled 2 and 3 Landscape visual impact map - Ternary category map Offshore exclusions Coastline Excluded area based on a buffer around the coastline Low: 6 nms Medium: 12 nms High: 12 nms [51], [59] Table 9 lists all social exclusion categories, along with the corresponding buffer distances and dataset sources for the low, medium, and high scenarios. In this case, only one category is relevant for offshore wind, while all the others are used to exclude onshore wind. Figure 11 summarises the resulting eligible areas according to the different scenarios for onshore wind deployment. The settlement category is one of the most relevant for onshore wind, as it shows the largest excluded areas in the medium and high scenarios due to the large buffers (1.5 km and 3 km). In this category, we defined the low and medium buffers around settlements based on the results of WIMBY D2.10 [23], where the distances correspond to the minimum and maximum values between the ten WIMBY countries studied (minimum distance to villages in Italy and maximum distance corresponding to 10 times the turbine tip height) in this report. For the high scenario, we considered the upper limit of 3 km from [37] for the distance to settlements criterion. For the noise category, the buffers are defined based on the existing European regulations on wind turbines and noise, summarised in [66]. For the low scenario, a buffer of 400 m is considered, which is the minimum setback distance required by noise regulations. A similar buffer value is used WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 52 of 91 in other works, such as [13] for Germany, [7] for Portugal, and [14], where this value is theoretically calculated to maintain a noise level of 40 dB. For the medium scenario, we set a buffer value of 500 m, and for the high scenario, a value of 2000 m, corresponding to the modal and maximum values, respectively, in [66]. For the shadow flicker category, we have defined the three scenarios in conjunction with D2.4 [67]. In the low scenario, the buffer is set at 84 m, corresponding to one hub height. In the medium scenario, the buffer is set at 1250 m, corresponding to the average radius for having a threshold of 30 hours of shadow flicker per year over flat (50 turbines in the Netherlands have a radius of 1235 m) and mixed (50 turbines in Spain have a radius of 1221 m) terrain. This buffer is assumed for the width (east to west) and the height (north to south) of the shadows, although the latter can be smaller (half of the radius). For the high scenario, we define a buffer of 2.5 km to mitigate the effects of shadow flicker completely. The landscape visual impact category takes into account the scenicness map developed in WIMBY D2.6 [65], which uses a machine learning model to predict the landscape scenicness across Europe. The resulting map from this model provides a binary and ternary classification of the landscape visual impact of onshore wind farms at a resolution of 1km×1km. To define the three scenarios in this category, we use the ternary classification prediction map with categories: unscenic to medium, scenic, and highly scenic. Thus, while no pixels are excluded for the low scenario, for the medium scenario, only pixels labelled as highly scenic are excluded, and for the high scenario, both scenic and highly scenic are excluded. The coastline category is only relevant for offshore wind, where values are defined based on [51], [59]. For the low scenario, the buffer distance around coastlines is set to 6 nautical miles (11,112 m), and for the medium and high scenarios, it is set to 12 nautical miles (22,224 m). WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 53 of 91 Figure 11: Geospatial overview of eligible areas for wind onshore deployment based on the three social scenarios. 3.2.4 Land availability in the spatial scenario combinations The resulting land availability from the nine combinations of the social and environmental dimensions set out above, together with the technical dimension which is the same in all cases and are shown in Figure 12. WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 54 of 91 Figure 12: Overview of the resulting land availability and how large a share of the total area is available for onshore wind deployment for the nine scenarios. 3.3 Macro-scale scenario development 3.3.1 Whole energy system scenarios JRC-EU-TIMES aligns with the land availability scenarios described in Section 3.2 in addition to the Climate change mitigation scenario (CLI) developed on the side of JRC-EU-TIMES. The CLI scenario aims to achieve net-zero GHG emissions in the EU and Switzerland by 2050. The scope of the GHG emissions WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 55 of 91 reduction target includes Energy (UNFCCC classification 1A-1B), Industrial Processes (2A-2H), Agriculture (3A-3J), LULUCF (4A-4H), Waste (5A-5E) and 50% of the emissions from international aviation and maritime. Details of these targets are summarized and listed in Table 10. The transition to net zero is to be achieved in this scenario solely with domestic mitigation measures in the EU. This scenario implements policies in the EU that are in the legislation up to 1.1.2024 or have been decided to be implemented in the near term. It assumes a continuation of the current energy supply and consumption trends and is used as a benchmark for the other two scenarios. Table 10: The major EU directives included in the CLI scenario. Scenario Directives CLI (net-zero target scenario) •EED energy efficiency (EU2023/1791) •EPBD buildings performance standards (EU2018/844) •ETS (all revisions up to EU2023/959) •EU RED III renewable targets (up to EU2023/2413) •GHG effort sharing (up to EU2023/857) •Vehicle emissions standards (EU2019/631, EU2023/851) •Heavy vehicle emissions standards (EU2019/1242) •Coal phase out 2030 in DE, DK, FI, GR, HU, IE, IT, NL, PT, SI, SK, ES •Intra-EEA aviation in EU-ETS •NTC electricity capacities as in ENTSO-E TYNDP 2022 plan •Reduction of nuclear share in France •New nuclear plants those under construction/advanced planning •GHG emissions from 1990: -55% in 2030, -90% in 2040 •Net-Zero GHG emissions in 2050 at the EU-level •Individual net-zero GHG emissions targets of the member states •GHG emissions reduction scope as in the EU Climate Law - includes LULUCF and 50% of the international transport •Refuel aviation SAF mandates •EU-ETS-2 from 2030 (although incl. in 2023 revision of EU-ETS) •+ 8GW new nuclear power (BG, CZ, RO, SI, SK, FI, FR) With the basis of CLI scenario, nine further scenarios are layered on top of the CLI scenario that are based on the three exclusion dimensions with three degrees (low, medium and high). Table 11: Scenario name alignment between the two macro-models JRC-EU-TIMES Scenario abbreviations Corresponding highRES-Europe scenario titles CLILL soc:low env:low WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 56 of 91 CLILM soc:low env:medium CLILH soc:low env:high CLIML soc:medium env:low CLIMM soc:medium env:medium CLIMH soc:medium env:high CLIHL soc:high env:low CLIHM soc:high env:medium CLIHH soc:high env:high In quantifying the nine scenarios that are shown in Table 11 above with the JRC EU TIMES model, we therefore assumed that the EU Green Deal policy package is always active: this means that the nine wind deployment scenarios in JRC-EU-TIMES consider the identical emissions reductions targets, renewable deployment targets, energy efficiency targets, sustainable fuels blending targets, etc. that are also considered in the Directives forming the EU Green Deal. Therefore, the scenario exploration with JRC-EU-TIMES identifies which options can substitute or complement wind when the EU policy is implemented under the presence of varying levels of societal and environmental concerns. 3.3.1 Additional boundary conditions applied to highRES-Europe In addition to the boundary conditions derived from the model coupling described above, a number of important assumptions are made in the highRES-Europe optimisation. Firstly, following Millinger et al. [68], we assume that negative emission technologies, i.e. bioenergy with carbon capture and storage (BECCS) in this case, can only provide a limited amount of compensation for concurrent fossil fuel emissions. This assumption is taken here to be a maximum sequestration of 5 MtCO2 beyond what is needed to achieve the emissions targets coming from the whole energy system model for the electricity system (which are ~ -80 MtCO2). This choice is made to prioritise emissions reductions rather than offsetting via BECCS and also to reflect the substantial array of non-modelled factors surrounding speculative technology options like BECCS, such as risks and uncertainties around scale up and wider social, environmental and economic impacts [69], [70]. Secondly, biomass potentials, which provide fuel for both BECCS and biomass power plants, are taken from the medium scenario of JRC ENSPRESO [71]. Here we exclude: i) energy crops to minimise competition with WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 57 of 91 food production, ii) imports to ensure the highest standards for feedstock sustainability and iii) primary forest residues to mitigate concerns around the sustainability of that supply chain. This results in a total biomass potential for the 28 countries modelled in highRES-Europe of 1000 TWh/yr. 3.4 Micro-scale scenario development To identify the micro-scale influence of wind turbine deployment in Europe, we used three scenarios reflecting varying levels of social and environmental constraints, as described in Section 3.2. These scenarios defined the feasible areas for wind farm deployment. Within these areas, potential wind farms were identified using our wind farm layout design and grid connection cost model. For this deliverable, we have chosen Styria, Austria, as our case study from among the four pilot regions analysed in WIMBY due to the availability of multi-criteria satisfaction analysis (MUSA) indicators. More details on the applied MUSA indicators and methodology will be presented in WIMBY Deliverable 4.6. These indicators will be provided by WP4.1 and WP4.2 partners. These indicators are derived from surveys conducted within the study region and will inform the MCDA analysis that will be developed in Deliverable 4.6. Following the selection of the region, we obtained nine potential wind farm deployment scenarios from Section 3.2.4 and selected three representative ones for further analysis, as presented in Table 12. Table 12: Scenarios considered in micro-level analysis. Scenario Environmental level Social level Technical Area available in Km2 - Styria High-high High High Yes 110.63 Med-med Medium Medium Yes 2209.58 Low-low Low Low Yes 5848.21 The potential deployment areas (polygons) vary across the different scenarios. To ensure consistency and comparability, the case study was narrowed down to a specific district. Styria comprises 13 political districts, as presented in Figure 13. Each district was evaluated for its presence in all scenarios, since some lack areas that are feasible under high environmental and social constraint levels. WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 64 of 91 Figure 19: Scenario variations of renewable electricity deployment for total Europe deviating from the mean of all scenarios. The black dotted line is the arithmetic mean of the values from all 9 scenarios from JRC-EU-TIMES. As highRES-Europe demonstrates in detail the electric system designs in 2050, Figure 19 above aims to show the trajectory of the variation in installed electric capacities of renewable technologies by considering the scenarios constraining wind capacity expansion based on social and environmental criteria. In particular, the figure shows the ways in which other renewables compensate for the reduction in wind installation to meet renewable targets and emissions targets (ETS) in the electricity sector by supplying the demand. Following the CLIHH case indicated by the blue line, we see the drastic compensation of electricity supply from solar and hydroelectric power. Though the compensation of the electricity supply reduction from wind via solar electricity scales more than hydro-electricity. While wind contributes to meeting electrification-driven demand, its potential appears to be overestimated. In contrast, the potential of hydro, as a near-perfect substitute, is already fully exploited. With the reduction of around more than 200 GW from wind in 2050 for the CLI_HH case, an addition of c.a. 300 GW of solar installations and merely 4 GW of hydro-electricity necessary. Biomass WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 65 of 91 and waste with CCS installations is minimally influenced by the reduction trajectory of wind and surges in capacity in 2040. In the near-term period until 2030 the wind deployment remains relatively the same across all scenarios, with the exception of the very high constraint case. This is due directly to the capacity potential constraints introduced for 2050 from highRES. All the constraints are active in the optimization. The potential for the CLI_HH case is less than half of the potential given for the CLI_MM case. Due to the model’s nature of perfect foresight and implemented extrapolation rules, the JRC-EU-TIMES pre-emptively reduces its capacity installation in the near term. This highlights the importance of wind deployment in meeting the climate and renewable energy deployment targets in the next 10-15 years. Figure 19 clearly shows that the trade-off in renewable electricity is mainly between solar and wind, as bioenergy and hydropower have limited potential for expansion due to resource constraints (bioenergy) or already high exploitation rate of sustainable potential (hydropower). The JRC-EU-TIMES model has a perfect foresight optimisation with low intraannual resolution, compared to the highRES-Europe model which is a snapshot model but with high spatial and temporal detail. Besides, JRC-EUTIMES sees lead-in times for the infrastructure deployment, grids or energy supply assets including wind. This is particularly important for the deployment for the wind offshore, as good capacity factors are achieved in deep waters far from the coast with the need for extensive and costly grid connections. In this regard, and as discussed also in the next section, the results from the JRC-EU-TIMES are conservative in the deployment of wind offshore electricity. 4.1.3 National-level cumulative new wind capacities trajectories and near-term challenges Figure 20 shows the total installed capacity per country for today and in 2035 (CLI_MM scenario case), as well as the cumulative new installations in wind for 2025 and 2035 in all scenarios examined. The numbers include both offshore and onshore wind. WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 66 of 91 In the near-term horizon until 2035, and for the CLI_MM scenario, Europe is projected to add about 480 GW of wind, mostly onshore, while the offshore installations are ramping up. Germany is the onshore leader, due to the supportive legislation in recent years and continuing strong political backing and reform momentum; at the same time, offshore wind capacity in Germany sees a rapid expansion. The United Kingdom can be characterised as an offshore powerhouse, supported by supply-chain funding, the upcoming Crown Estate investments and the next offshore auction targets; however, the UK will need to proceed faster in the near term with the grid upgrades to support the expansion of offshore capacity by 2035. The Nordic countries (Sweden, Finland and Denmark) continue strong trajectories, leveraging supportive policies and community backing. Finally, a set of countries like France and Poland can be characterised as emerging markets, signalling the broadening of wind leadership beyond traditional players. Based on Figure 20, Germany, United Kingdom, Ireland and Spain becomes the “first movers” in its expansion in wind installations. The potential restrictions in the scenarios play a bigger role in the early years, around the 2025 milestone years, as capacities vary more among the scenarios than in 2030. Germany remains the main the highest in wind capacity (ca. 145 GW) in 2035. On the other hand, France and Italy increase their new capacities in 2035 from ca. 1 GW to 55 and 48 respectively. To realise the above geographical distribution of wind expansion in Europe between 2020 and 2035, concrete and persistent challenges need to be tackled. Even in countries which are first movers in wind expansion, like Germany and the UK, permitting for example remains an issue due to complex, decentralised processes and legal appeals. Supply chain constraints, including turbines, raw materials, vessels etc. need to be overcome too across Europe. Grid infrastructure upgrades will be needed to be completed in a rather short time, especially for offshore generation to keep pace with the new capacity ambitions of the UK and the Nordic countries. Finally, volatile auction designs and financing issues, seen for example in the UK’s failed AR5 round, underscore the need for stable, investment-friendly frameworks to de-risk deployment and accelerate wind uptake in the next 10 to 15 years across the European continent. WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 67 of 91 (a) (b) (c) Figure 20: (a) Installed wind capacity in 2020 and 2035; (b) cumulative wind capacity installation for 2025; (c) cumulative wind capacity for 2030 on a national European level. The ranking of the countries is based on the largest to smallest of wind capacity for scenario CLIHH. The figures (b) and (c) serve to observe the ranking. © GeoNames, Microsoft, Open Places, OpenStreetMap, TomTom Powered by Bing 3 1 0 26 2 16 7 0 4 11 1 6 5 3 9 4 26 0 60 Installed wind capacity in GW in 2020 for the CLI_MM case 0 159 GW © GeoNames, Microsoft, Open Places, OpenStreetMap, TomTom Powered by Bing 9 1 50 20 76 4 42 5 53 5 19 29 10 48 0 0 159 Installed wind capacity in GW in 2035 for the CLI_MM case 0 159 GW 14 WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 68 of 91 4.1.4 Cost and design implications for Europe’s electricity system in 2050 In this section, we present an overview of the highRES-Europe modelling results, i.e. the design of the continental power system covering 28 European countries in 2050. Here, our aim is to showcase the implications of varying levels of social and environmental protections/restrictions on power system design and total annualised system costs. Figure 21: Spatial (NUTS2) distribution of onshore wind capacities for the nine scenarios from highRES-Europe modelling. The colorbar is capped at 15 GW to keep the lower end of the scale visible; some NUTS2 areas have installed capacity in excess of this limit. Firstly, in Figure 21, we show the NUTS2 level capacity deployment of onshore wind across the nine land availability scenarios with their different levels of WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 69 of 91 prioritisation around social and environmental protections. It is clear that the deployment pattern changes substantially, both across Europe and within countries, from the low-low case to the high-high case. In the former, and a number of the other lower restriction cases, wind siting is concentrated in favourable (i.e. windy) NUTS2 regions. As one then moves to the higher restriction scenarios, and particularly high-high, installed wind capacity is both markedly less in absolute terms, but also more evenly distributed over a greater number of NUTS2 areas. Figure 22: Installed electricity system capacities for the low-low (social-environmental) scenario (left) as well as the relative difference for the remaining eight scenarios, compared to the low-low scenario. The upper panel of the figure shows electricity generation technologies while the lower panel shows energy storage. All results from highRES-Europe modelling WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 70 of 91 Figure 22 plots the aggregated installed electricity system capacities, with generation in the top panels and storage in the bottom panels, of all 28 countries modelled in highRES-Europe in the low-low scenario (left panels). Here, we see a combined system dominated by variable renewables, with total wind power capacity of ~1300 GW onshore and ~300 GW offshore in the low-low case. The right panels then show that as the restriction/protection levels increase, this ratio changes such that by high-high there is just ~200 GW of onshore capacity and ~650 GW of offshore, i.e. the model responds in part to constraints on onshore by deploying more offshore wind. Figure 23: Comparison of installed onshore wind power capacity in the high-high scenario from highRES-Europe and 2024 levels, based on data from ENTSO-E [73] and U.K. Department for Energy Security and Net Zero [74]. For onshore, the deployed volume in the high-high scenario should be compared with today’s cumulative onshore capacity across these 28 countries of 229 GW [73], [74], indicating the pessimistic outlook this case has for this technology. The difference between today (2024) and the highhigh scenario is particularly striking for Germany, which sees a reduction of 55 GW of onshore wind, as shown in Figure 23. Similar results are obtained for other large European countries, such as France, Italy, and the United Kingdom, although less dramatic than Germany. Some smaller countries (in terms of population) see an increase, even in the high-high scenario, such as Finland, Norway and Ireland. This is likely a combination of relatively large land area, low population density (making the high social scenario less impactful) and a lower existing capacity in 2024. Finally, it is also noteworthy that around 200 GW more battery capacity is installed in the high-high compared to the low-low cases. This is likely to be at least partially explained by the increase in installed solar PV between these two scenarios, given the typical synergy between batteries and solar. WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 71 of 91 Figure 24: System-wide annual electricity generation for the low-low scenario (left), as well as the relative difference to the low-low scenario for the remaining eight scenarios (right) from highRES-Europe. Finally, at the European level, in Figure 24, we show annual electricity generation in a similar manner to Figure 22, i.e. for the low-low case and then the change with respect to that across the other eight scenarios. This chart underscores the important role played by onshore wind in the lower restriction cases, with it providing ~2400 TWh or a third of electricity generation per year for the entire European system. It also highlights the pivotal role played by offshore wind in replacing electricity supplied by onshore wind as the prioritisation of social and environmental factors grows across the scenarios. While at the capacity level, offshore appears similar to solar PV, given its typically much higher capacity factor, it plays a much more significant role in generation terms. Ultimately, the vast majority of the ~2000 TWh of onshore generation lost in the high-high case is replaced by offshore wind, solar PV and nuclear. WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 72 of 91 Figure 25: Share of annual generation across the 28 modelled countries for the diagonal scenarios of low-low (L-L), medium-medium (M-M) and high-high (H-H) from highRES-Europe modelling Moving to the national level, Figure 25 shows the share of annual electricity generation by country across low-low, medium-medium and high-high scenarios. These panels demonstrate how, as the role of onshore wind is progressively reduced due to increasing social and environmental restrictions, national electricity systems are reorientated more toward offshore wind (where possible) and solar PV. Countries like France, the Czech Republic and Slovakia switch to deploying new nuclear power, despite it being a more costly choice than variable renewables. Nevertheless, most countries remain heavily reliant on a mix of wind and solar PV to meet their electricity generation needs, with small amounts of balancing power coming from biomass and natural gas (note that storage plays a key role in providing flexibility and is not shown in this figure). WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 73 of 91 Figure 26: Total annualised system cost increase relative to the low-low scenario for the whole of Europe (all 28 countries modelled in highRES-Europe combined). Next, we show the total annual system cost implications for the nine land availability scenarios relative to low-low in Figure 26. Here, we see that for restrictions involving a combination of low and medium, the system cost increases are at most ~4%. These increases are driven by the continent's electricity supply being re-orientated toward less reliance on onshore wind, one of the cheapest forms of electricity generation in our modelling, as land available for deployment progressively becomes less across the scenarios. Bringing the high prioritisation level of either dimension into play leads to cost increases of ~5% or more as the available space for onshore wind is further restricted. Indeed, the high-high case results in a ~15% increase in the total annualised cost of the system, equivalent to an extra ~104 €bn/yr (in 2024 euros) in expenditure on Europe’s electricity system. Again, this is brought about by the high levels of protection assigned to social and environmental aspects in this potential future scenario, substantially restricting the land available for wind, particularly onshore wind. As a result, the optimal system moves towards a more expensive design that relies more on offshore wind, solar PV, batteries and particularly costly options like new nuclear. WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 80 of 91 for real-world constraints such as land use restrictions, terrain characteristics, and infrastructure availability. The core strength of the model lies in its ability to balance multiple objectives: maximising the AEP, minimising internal cabling costs, and reducing external grid connection expenses. This is achieved by evaluating multiple scenarios that consider environmental and social constraints. Turbine layouts are optimised, while cabling follows a cost-efficient design based on Delaunay triangulation. External connection paths are further refined to avoid areas with high development constraints, demonstrating how routing is sensitive to land cover cost. The scenarios with larger land availability, such as low-low, enable greater energy output by accommodating more turbines. However, this comes at a significant increase in total infrastructure cost—highlighting that more space does not automatically translate into better cost-performance. For instance, although the low-low scenario yields 64.5% more energy than high-high, its total connection cost is ~80% higher. This illustrates the tradeoffs between land use type, energy potential, and capital expenditure. These findings emphasise the importance of integrated spatial and technoeconomic modelling in wind energy planning. Rather than relying solely on wind potential or land availability, optimal site selection must consider a combination of energy yield, infrastructure costs, and land development constraints. An MCDA of different wind farms aims to showcase the competition among technical, economic, environmental, and social criteria, using models and results from the micro-level modelling other work packages in WIMBY. The analysis will be presented in the final deliverable (D4.6). WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 81 of 91 REFERENCES [1] International Energy Agency (IEA), ‘Europe – Countries & Regions’, Europe. Accessed: May 19, 2025. [Online]. Available: https://www.iea.org/regions/europe/electricity [2] International Energy Agency (IEA), ‘Denmark - Countries & Regions’, Denmark. Accessed: May 19, 2025. [Online]. Available: https://www.iea.org/countries/denmark/electricity [3] International Energy Agency (IEA), ‘United Kingdom - Countries & Regions’, United Kingdom. Accessed: May 23, 2025. [Online]. Available: https://www.iea.org/countries/united-kingdom/electricity [4] International Energy Agency (IEA), ‘Germany - Countries & Regions’, Germany. Accessed: May 23, 2025. 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WIMBY | D4.5 | V4.0 | P This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains Page 89 of 91 Figure 31: Geospatial overview of eligible areas for wind offshore deployment based on the three environmental scenarios. Figure 32: Geospatial overview of eligible areas for wind offshore deployment based on the three social scenarios.