Shade, dirt, or aging considerably reduce the yield of large photovoltaic facilities. Karlsruhe Institute of Technology (KIT) and partners from science and industry have now launched the Solar Park 2.0 project to reduce these losses.
Even small objects, such as dust and leaves, can block sunlight from reaching solar cells, and understanding how the loss of incoming radiation affects power output is essential for optimizing photovoltaic technology. In the Journal of Renewable and Sustainable Energy, researchers explore how different shade conditions impact performance of single solar cells and two-cell systems connected in series and parallel. They found that the decrease in output current of a single cell or two cells connected in parallel was nearly identical to the ratio of shade to sunlight. However, for two cells running in series, there was excess power loss.
New investigations have produced a simpler model to elegantly explain previously observed behaviors for free carrier generation in organic solar cells. The model relies on well-established scientific descriptors, Marcus theory and entropy. Previous descriptions proposed new physical phenomena, but a new, simplified model provides a unified platform for understanding processes in both solution and solid-phase systems for organic photochemical conversion.
Halide perovskite can make solar cells a thousand times thinner than today’s silicon solar cells. A new approach allows scientists to watch changes in the material’s structure and functional properties while the material solidifies into a thin film from solution. This gives new insight into how the material’s structure and functionality are related, aiding in future solar cell design.
As photovoltaic technology continues to progress, PV devices’ applications in harvesting energy from indoor ambient light have become more realistic. Some combinations of PV material and light source can be more efficient in converting power than the same material under solar illumination, and a better understanding of these relationships is needed to fully characterize the behavior of solar cells under very low illumination conditions.
Case Western Reserve University computer scientists and energy technology experts are teaming up to leverage the diagnostic power of artificial intelligence (AI) to make solar-power plants more efficient.
To meet the Paris Agreement’s goal of preventing Earth’s average temperature from rising more than 2 degrees Celsius above preindustrial level, one of the best options for the energy economy will involve a shift to 100% renewable energy using solar energy and other clean energy sources. In the Journal of Renewable and Sustainable Energy, researchers describe a model developed to predict what is necessary for the solar industry to meet Paris Agreement targets.
Perovskite materials are increasingly popular as the active layer in solar cells, but internal forces in these materials cause distortions in their crystal structures, reducing symmetry and contributing to their intrinsic instability. Researchers at Soochow University examined the mechanisms at play, as well as several degradation factors that influence the performance of perovskite photovoltaics. In APL Materials, they clarified the factors influencing the degradation and they summarized some feasible approaches for durable perovskite photovoltaics.
Utility-scale photovoltaics are the largest sector of the overall solar market within the U.S. and the fastest-growing form of renewable power generation, and this fleet of utility-scale photovoltaic projects is relatively young and hasn’t been operating long enough to establish a lengthy history of operational field service. In the Journal of Renewable and Sustainable Energy, researchers assess the performance of 411 utility-scale photovoltaic projects built within the U.S. from 2007 through 2016.
As we look back at a decade of discovery, we highlight 10 achievements by scientists at Berkeley Lab and the Joint Center for Artificial Photosynthesis that bring us closer to a solar fuels future.
Iowa State engineers are working with collaborators to develop machine learning theories and software tools that can quickly and cheaply design better solar cells. Those theories and tools could also be applied to the rapid design of all kinds of new technologies.