Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Seasonal optimal mix of wind and solar power in a future, highly renewable Europe
2010402 citationsDominik Heide, Lueder von Bremen et al.Renewable Energyprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Stefan Bofinger
Since
Specialization
Citations
This map shows the geographic impact of Stefan Bofinger's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Stefan Bofinger with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stefan Bofinger more than expected).
This network shows the impact of papers produced by Stefan Bofinger. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Stefan Bofinger. The network helps show where Stefan Bofinger may publish in the future.
Co-authorship network of co-authors of Stefan Bofinger
This figure shows the co-authorship network connecting the top 25 collaborators of Stefan Bofinger.
A scholar is included among the top collaborators of Stefan Bofinger based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Stefan Bofinger. Stefan Bofinger is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Bofinger, Stefan, et al.. (2017). LCOE estimation in aggregated wind/PV study.1 indexed citations
4.
Bofinger, Stefan, et al.. (2016). Wind and solar PV resource aggregation study for South Africa: Public presentation of results.2 indexed citations
5.
Bofinger, Stefan, et al.. (2015). Smoothing out the volatility of South Africa's wind and solar photovoltaic energy resources : energy. 8(2). 28–29.1 indexed citations
6.
Bischof-Niemz, Tobias, et al.. (2015). Smoothing out the volatility of South Africa's wind and solar photovoltaic energy resources.2 indexed citations
Nieuwenhout, Frans, J.C. Jansen, Luis Olmos, et al.. (2010). Market and regulatory incentives for cost efficient integration of DG in the electricity system. Cadmus - EUI Research Repository (European University Institute).6 indexed citations
11.
Heide, Dominik, et al.. (2010). Seasonal optimal mix of wind and solar power in a future, highly renewable Europe. Renewable Energy. 35(11). 2483–2489.402 indexed citations breakdown →
12.
Bofinger, Stefan, et al.. (2008). Value of PV Energy in Germany - Benefit from the Substitution of Conventional Power Plants and Local Power Generation. HAL (Le Centre pour la Communication Scientifique Directe).11 indexed citations
13.
Lorenz, Elke, Jethro Betcke, Anja Drews, et al.. (2007). Intelligent Performance Check of PV System Operation Based on Satellite Data (PVSAT-2), Final Technical Report. Utrecht University Repository (Utrecht University).3 indexed citations
Beyer, Hans Georg, et al.. (2001). Identifikation und anwendung eines modells der strom / spannungs kennlinie von solarmodulen aus amorphe silizium. Strathprints: The University of Strathclyde institutional repository (University of Strathclyde).2 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.