For the past few months, we have been running a closed beta test, offering four years of data. Thanks to all our users that took part in testing! Since this ran smoothly, we are happy to announce that as of today, the Ninja is able to serve 17 years of weather and renewables data – from 2000 through to 2016.
This expanded data set is now available to all registered users.
You can now choose which year to download through the web interface. If you’re using the API, you can still select a more precise range of dates. In either case, requests can be for a maximum of one year at a time. If you want to download longer timespans, you will need to run an individual simulation for each year – we found the server simply cannot cope with processing >100 GB of weather data in a single request!
We hope this enables you to move forwards with answering new and interesting research questions!
Speaking of research, we have been collaborating with two international teams on studies using the Renewables.ninja. With Fabrizio Fattori and Norma Anglani of Università degli Studi di Pavia (Italy) we published a paper showing that high concentrations of solar PV will have significant effects on the power system of northern Italy, and how electricity storage can help to reduce peak demand, ramping rates and over-generation.
F. Fattori, N. Anglani, I. Staffell and S. Pfenninger. High solar photovoltaic penetration in the absence of substantial wind capacity: Storage requirements and effects on capacity adequacy. Energy (2017). doi: 10.1016/j.energy.2017.07.007
Then with Christian Grams, now of Karlsruhe Institute of Technology (Germany), Remo Beerli and Heini Wernli (ETH Zürich) we have published a paper in Nature Climate Change showing how a new understanding of continental-scale weather regimes and greater cooperation between countries could dramatically smooth the output of wind farms across Europe:
C.M. Grams, R. Beerli, S. Pfenninger, I. Staffell and H. Wernli, 2017. Balancing Europe’s wind power output through spatial deployment informed by weather regimes. Nature Climate Change (2017), 7, 557–562. doi: 10.1038/nclimate3338
Finally, we have upgraded the wind model to include 121 wind turbine models. You can type into the textbox on the web interface to search for available models. We have selected the 10 most widely used models (both in existing and planned wind farms) to sit at the top of this box as the default input.
Here are some of the most widely used turbine models in our database. Highlighted rows show the turbine models that appear at the top of the UI selector.
|Top current turbine models||Top future turbine models|
|Model||Existing Farms||Capacity (GW)||Model||Planned Farms||Capacity (GW)|
|Vestas V90 2000||912||12.4||Siemens SWT.3.6 107||36||11.2|
|GE 1.5 sle||467||20.0||Vestas V112 3000||36||2.6|
|Vestas V80 2000||585||9.9||Gamesa G128 4500||16||5.6|
|Enercon E82 2000||588||8.1||Vestas V90 3000||26||2.7|
|Vestas V90 1800||410||9.4||REpower 5M||10||4.8|
|Vestas V66 1650||316||11.4||Vestas V164 7000||11||3.9|
|Siemens SWT.2.3 93||171||17.3||Enercon E82 2000||17||1.9|
|Gamesa G90 2000||335||8.8||Alstom Eco 110||8||3.3|
|Vestas V90 3000||279||10.3||Vestas V90 2000||18||1.4|
|NEG Micon NM48 750||588||4.7||Enercon E126 6500||6||2.5|
|REpower MM92 2000||408||6.2||Vestas V80 2000||12||1.1|
|Acciona AW77 1500||377||6.4||Acciona AW77 1500||8||1.6|
|Vestas V47 660||621||3.6||Vestas V100 1800||27||0.5|
|Enercon E70 2300||361||6.1||Enercon E101 3000||19||0.7|
|Vestas V52 850||539||4.0||REpower MM92 2000||17||0.8|
Disclaimer: we cannot guarantee the completeness or accuracy of this table and it should not be used for any commercial decisions.
I. Staffell and S. Pfenninger (2016). Using Bias-Corrected Reanalysis to Simulate Current and Future Wind Power Output. Energy 114, pp. 1224-1239. doi: 10.1016/j.energy.2016.08.068
As always, if you have any issues, do contact us.