Recommendations for Parameters¶
Exclusion parameters¶
An overview of possible exclusion criterias found in selected literature can be downloaded here.
You can use this template to document the exclusion parameters you want to apply including their data sources.
Deployment Densities¶
You can calculate the deployment density of existing solar and wind farms in your study area and take the tables below as an orientation.
Solar PV¶
| Deployment density (MW/km²) | Source | Quote from source / comment |
|---|---|---|
| 35 | Benalcazar et al. (2024) | "Conservative assumption for practical implementation and corresponds to the 95th percentile of the global average land-use efficiency of utility-scale photovoltaic systems [63,88]." |
| 50 | Benalcazar et al. (2024) | "Reflects the average land-use efficiency of existing and ongoing utility-scale PV installations in Poland [90–92] and is close to the global average land use reported in the literature [63]." |
| 33 | Sterl et al. (2022) | — |
| 30 | Wang et al. (2022) | "Based on interview with solar project developers." |
Wind onshore¶
Defining the deployment density of wind farms is quite difficult. There are different methods to calculate the land taken up by a wind farm. (Enevoldsen & Jacobson, 2021) provides a very good overview on deployment densities of wind farms around the world using a sophisticated method. Use the table below as an orientation.
| Deployment density (MW/km²) | Source | Quote from source / comment |
|---|---|---|
| 8.61 | Von Krauland & Jacobson (2024) | Based on (Enevoldsen & Jacobson, 2021) |
| 11.8 | Sterl et al. (2022) | “spatial footprint of solar PV and wind power plants was taken to be 33 MW/km² for solar PV [23,24] and 11.8 MW/km² for wind power [25].” |
| 6.2–46.9 mean: 19.8 median: 13.9 |
(Enevoldsen & Jacobson, 2021) | European onshore wind farms |
| mean: 20.5 | Enevoldsen & Jacobson (2021) | Onshore wind farms in CHN, AUS, USA, CHL |
References
- Benalcazar, P., Komorowska, A., & Kamiński, J. (2024). A GIS-based method for assessing the economics of utility-scale photovoltaic systems. Applied Energy, 353, 122044. https://doi.org/10.1016/j.apenergy.2023.122044
- Enevoldsen, P., & Jacobson, M. Z. (2021). Data investigation of installed and output power densities of onshore and offshore wind turbines worldwide. Energy for Sustainable Development, 60, 40–51. https://doi.org/10.1016/j.esd.2020.11.004
- Sterl, S., Hussain, B., Miketa, A., Li, Y., Merven, B., Ben Ticha, M. B., Elabbas, M. A. E., Thiery, W., & Russo, D. (2022). An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power. Scientific Data, 9(1), 664. https://doi.org/10.1038/s41597-022-01786-5
- Von Krauland, A.-K., & Jacobson, M. Z. (2024). India onshore wind energy atlas accounting for altitude and land use restrictions and co-located solar. Cell Reports Sustainability, 1(5), 100083. https://doi.org/10.1016/j.crsus.2024.100083
- Wang, Y., Chao, Q., Zhao, L., & Chang, R. (2022). Assessment of wind and photovoltaic power potential in China. Carbon Neutrality, 1(1), 15. https://doi.org/10.1007/s43979-022-00020-w
Distances to high-voltage power lines¶
Solar PV (Zink, 2024a)¶
| Region | Median | P95 | Sample |
|---|---|---|---|
| EU | 1.96 km | 11.03 km | 23658 |
| Africa | 0.9 km | 2.5 km | 348 |
Wind onshore (Zink, 2024b)¶
| Region | Median | P95 | Sample |
|---|---|---|---|
| EU | 2.9 km | 14.1 km | 22883 |
| Africa | 2.3 km | 8 km | 154 |
References
- Zink, C. (2024a). GIS-based Evaluation of Suitability and Implementation Prospects for Solar PV Energy in Africa.
- Zink, C. (2024b). Proposing a Methodology to Evaluate the Feasability and Implementation Potiential of Wind Energy in Africa Using GIS Data. https://doi.org/10.1109/PowerAfrica61624.2024.10759357
Technology parameters¶
Solar PV¶
Information on optimal tilt: solarpaneltilt.com