Quantifying the trade-offs between renewable energy visibility and system costs

Abstract

Visual landscape impacts on scenic and populated places are among significant factors affecting local acceptance of large-scale renewable energy projects. Through the combination of large-scale reverse viewshed and techno-economic energy system analyses, we assess their potential impacts for nationwide energy systems. In our case study of Germany, moderate consideration of visual impact by placing renewables out of sight of the most scenic and densely populated areas does not have a significant impact on future energy system costs and design. In contrast, in scenarios assuming high sensitivity to visual impacts, annual energy system costs would increase by up to 38% in 2045. The energy system's resilience would also be compromised due to the increasing reliance on green hydrogen imports and the uncertain mass adoption of rooftop photovoltaics. Our analytical framework facilitates careful planning that considers the visual impact of renewable energy infrastructure, thus enabling socially acceptable deployment while understanding the implications for system costs and transformation pathways.

Fig. 1. Methodology of reverse viewshed analysis and exemplary use of the generated reverse viewshed maps as exclusion zones in land eligibility assessments.

a Reverse-viewshed analysis is performed from a selected viewpoint to map the areas where wind turbines or open-field photovoltaics (PV), if constructed in the areas, would be visible to people standing at the selected viewpoint. This analysis requires a digital elevation model (DEM) of the area and location of viewpoints that are to be protected from the visual impacts of renewable infrastructures. The EU-DEM v1.1 with 25-m resolution is used for the analysis. The viewpoints utilized are the centroids of a 1-km² grid of Germany with underlying metadata of scenicness and population density level. The visibility threshold distance was set at 11 km for wind turbines and 7.5 km for open-field PV. b Example of integrating reverse viewshed maps from viewpoints with high scenicness level (level 9), and high population density (≥ 5000 people per km²) into the land eligibility assessment of onshore wind turbines in the district of Aachen, Germany. The green areas represent the eligible areas for the siting of onshore wind turbines based on legal, geographical, technical, environmental, and additional visibility restrictions.

Fig. 2. Reverse-viewshed maps and the remaining renewable capacity potential at each visibility scenario.

Reverse-viewshed maps show siting areas for wind turbines and open-field photovoltaics (PV) that are visible from different scenicness and population density thresholds. Each map is utilized as an exclusion zone in the capacity potential calculation, and the remaining renewable energy potential for each scenario is displayed in the subsequent line graphs. The capacity potential, depicted as line graphs, are calculated after taking into account other legal, geographical, technical, environmental, and additional reverse viewshed constraints. The secondary y-axis in the line plots shows the percentage of the population protected from the visual impacts of large-scale renewable infrastructures. Source data are provided as a Source Data file.

Fig. 3. Cost-potential graphs of onshore wind (a,b) and open-field photovoltaics (PV) (c,d) not visible from different scenicness and population density thresholds.

The area in light gray represents annual electricity generation in 2023. The areas in light green and green represent the electricity generation targets according to the German Renewable Energy Sources Act (EEG, 2023) for each technology by 2030 and 2040, respectively. Source data are provided as a Source Data file.

Fig. 4. Relative cost deviation by sectors and relative deviation of electricity supply by sources when large-scale renewable energy plants are not visible from scenicness levels ≥ 5 and population density ≥ 300 people per km², compared to the base scenario.

a, b The cost deviation is shown for each economic sector and important energy-related sub-sectors. The energy sector accounts for the domestic energy supply. The infrastructure sector accounts for grid costs. Renewable and conventional fuels represent costs for imported fuels. Building and transport sectors are not as strongly affected by the various visibility scenarios as the energy sector. In these two scenarios with the strictest visibility restriction, the cost from the energy sector increases by 38% in 2045 compared to the base scenario (equivalent to €23.6 billion). c, d Reduction in electricity supplies from onshore wind and open-field photovoltaics (PV) are substituted by rooftop PV and offshore wind. The blue line shows the need to increase hydrogen imports to meet demand in these two scenarios with the strictest visibility restrictions. Source data are provided as a Source Data file.

Fig. 5. Annual system costs in 2045 for different visibility scenarios.

The green color indicates scenarios where minimizing the visibility of renewable infrastructure exclusively from the most scenic or densely populated areas does not significantly affect system costs. By contrast, the beige color signifies scenarios where increasing visibility restrictions lead to a rise in system costs. The red color indicates the most stringent visibility restrictions considered, which results in cost escalation, exhaustion of renewable energy potential, and increases in hydrogen imports and fossil fuel reliance. As all sector's costs are taken into account, these system costs include many parts, such as for the building stock or transport options, which are not as strongly affected by the various scenarios as the energy sector. Source data are provided as a Source Data file.

Fig. 6

Overview of the models and data used in this study.