The largest critique of renewable energy sources focuses on the fact that their generation output varies significantly with time (and space). Inspired by the fantastic lectures on energy system modelling by Tom Brown, I thought I’d take a look at the solar and wind data for Poland and do a little exercise visualizing them using the Plotly framework.

The idea was to compare the variability of wind and solar with the fluctuations of the electricity demand curve. I used data from and

  • Capacity factor of solar generation in Poland in 2016
  • Capacity factor of onshore wind generation in Poland in 2016
  • Capacity factor of offshore wind generation in sample location in 2016 (matching planned location for PGE Baltica 3 plant) for Vestas V164 turbine
  • Electricity demand curve for 2016, scaled to 0-1 through dividing by max load in 2016 to show variability

I summed the capacity factors for solar, onshore and offshore wind to see if there are times, when no RES source can actually produce electricity to cover the demand. This is obviously not a realistic optimization scenario and does not account for the different locations, different wind turbine and solar panel models, varied capacities. It does however show if renewables are in fact as variable as they are portrayed.

Looking at the histogram of the summed capacity factors, you can see that the summed capacity factors are clustered around the 0.5-0.8 range (mean is 0.76) with a long tail towards the right. Values above 1 mean that we can potentially overproduce energy and store it for later (obviously depending on the capacity). Is is extremely rare to not have any production at all – only during 33 hours (from a yearly total of 8760 hours) the summed capacity factor fell below 0.05. The red color shows cases when RES generation covers less than 50% of the load, which only happens 26% of the time.

The chart below shows the sum of the RES capacity factors and the demand profile for Poland in 2016. You can see that a lot of the time (marked as green), renewables are able to produce more than the load (depending on the capacity). There are times when renewable generation cannot cover even half of the demand – marked as red. Those cases would require significant storage capacities, but they are not as frequent as I was expecting. In particular, it doesn’t feel like these times would warrant a large outstanding fossil-fuel fleet to cover the base load (like the Polish energy strategy assumes).

A deep dive into a specific week in the winter and summer of 2016 shows the structure of renewable generation from different sources – onshore/offshore wind and solar. Offshore wind is definitely the core with average yearly capacity factors reaching 60% for very large turbines (like the GE Haliade X). In summer, solar generation can be used to fill the gaps on days with less wind – the production from solar is much more predictable than wind and basically follows a daily pattern, nicely correlated with the demand profile. The difficulty lies in the evening time, when demand is high and solar production drops to zero. Fortunately those moments are often compensated by wind speeds increasing towards the night.


I did not include hydro, import, biomass and fossil fuels, the idea was to see if you can cover the load with just solar and wind. But with good import possibilities, some stable hydro and biomass, there should be no need to use fossil fuels in the energy mix.

Since this was mainly a Plotly excercise, you can download the jupyter notebook and the input files from my Github repo. It’s not pretty and lacks commenting, but can be used as an inspiration especially if you’re new to Plotly styling.