Motivation: wanted to show the impact of daily and seasonal intermittency of renewables and space heating demand, using Belgium as main example.

While wind and solar energy are renewable, their energy output fluctuates - or to use a fancy word their energy output is intermittent. Also space heating and cooling fluctuate. The next five parts visualize several types of intermittency:

• First we investigate seasonal fluctuations of solar energy at different latitudes. We fortunately observe that latitudes of the biggest carbon emitters are not severely impacted by seasonal fluctuations.
• Next we look at solar hourly and daily fluctuations for Brussels in Belgium
• In the third part we show daily fluctuations of solar in Belgium.
• We redo the analysis for wind power in Belgium.
• To conclude, we compare solar and wind seasonal fluctuations with space heating and cooling demand.

Methods:

• To estimate solar and wind energy output we use a power of 1 kilo-Watt peak. The corresponding values in kWh can be divided by 24 to obtain the capacity factor.
• We use data from 2005 till 2015, covering a period of 11 years.
• Most data plots on this page are for Brussels [5] or Belgium [8]. Plots for other locations are available in this website.
• Figures 2,3,4 use box-plots to visualize the statistics. A brief intro to reading a box-plot is available in the manual [1]

1. Solar seasonal fluctuations for different latitudes

To start we consider latitudes of approximately 30, 40, 50 and 60 degrees, respectively Ouarzazate in Morocco, Madrid in Spain, Brussels in Belgium and Bergen in Norway. Ouarzazate is the location of the Noor concentrated solar heat plants [2], with molten salt storage to enable providing power in the evening as well. Here we use photo-voltaic data from the PVGIS 5.1 tool [4], and compute the average daily energy, averaged per month or per year. Table 1 shows the statistical properties for the four selected location. We observe that the average of Belgium is about half the average solar output of Ouarzazate. So is it sufficient to double the amount of solar plants at Brussels to obtain an equivalent solar output at Ouarzazate. Unfortunately this is not the case since the standard deviation of the monthly averages increases increases for more northern latitudes. This is also obvious when we consider the difference between the maximum monthly average with the minimum monthly average. We also see that the difference of the January monthly daily average and the yearly daily average is bigger for northern locations, and also for the July average.

Table 1: Daily solar statistics, the locations names link to seasonal fluctuations for different solar panel orientations using PVGIS 5.2

kWh/day per kWpeak	Average (avg)	Standard deviation	Max - Min	Jan - avg	Jul - avg
Ouarzazate - Morocco	4.96	0.287	0.780	-0.010	-0.260
Madrid - Spain	4.31	0.752	2.01	-0.973	0.977
Brussels - Belgium	2.60	1.13	2.93	-1.47	1.153
Bergen - Norway	1.94	1.452	3.98	-1.863	1.477

Figure 1 shows this graphically, and we immediately observe that July daily solar averages are closer to each other than those in winter. This also means that simply doubling solar power in Belgium will still lag in winter and will produce more energy in summer than in Ouarzazata, and this effects gets worst for Bergen.

Figure 1. Daily solar energy for different latitudes, averaged per year (dashed lines) and per month (markers).

Fortunately large parts of the world experience only limited seasonal fluctuations. Let us consider the top 3 carbon emitters: China, the USA and India, which combined emitted half of the world man-made CO₂ in 2017 [3]. Table 2 shows that most parts of these countries are below a latitude of 40 degrees, similar to Madrid.

Table 2: Latitude of biggest carbon emitters

Country	Lower latitude	Upper latitude
China	18°	45°, with most cities lower than 40°
USA	25°	47°, with most cities lower than 40°
India	9°	34°

Note that these are all large countries with a high population count, so it is normal that they make up for a significant part of carbon emissions. So it is good to observe that seasonal solar fluctuations are low for these countries.

2. Hourly and daily intermittency of solar for Brussels

For seasonal fluctuations we looked at daily averages, but this hides day to day and hourly fluctuations. Therefore we re-use the PVGIS 5.1 data for Brussels [5] for Figure 2, which illustrates intermittency with following plots:

• On the left side average hourly output power is shown. We see that on average most power is generated around noon. Since in winter days days are shorter and the sun is lower the average power is lower than for summer months.
• On the right side a box plot summarizes monthly solar energy output. We observe that 25% of the days more energy is generated in December than in September. But also the opposite is true, 75% of the days in September produce more solar energy than 75% of days in December. Hence we observe lower averages in winter than in summer.

Figure 2. Brussels hourly and daily solar energy intermittency.

3. Daily intermittency of solar for Belgium

For solar we redo the analysis for Belgium, using a bias corrected data-set for countries in Europe from Renewables.ninja [6]. While this model produces different energy values, the trend is similar as in the right part of Figure 2. Specific differences with the Brussels PVGIS data-set are that in winter less energy is produced, and in summer more.

Figure 3. Belgium daily solar energy.

4. Daily intermittency of wind for Belgium

The Renewables.ninja data-set also models wind energy, both for the wind fleet of 2016 as for future extensions. Here we use the model data for the 2016 wind fleet for Belgium [7]. Data plots for future extensions are available at [8]. We see that per kWpeak more energy is produced by wind, more than a factor 2. Fortunately wind produces more energy in the winter period, where solar power is reduced. Nevertheless day to day fluctuations are significant, so that wind energy production can be low for a significant number of days in winter when only a limited amount of solar power is present. Hence we can conclude that either backup power or energy storage will be essential for Belgium.

Figure 4. Belgium daily wind energy for 2016 wind fleet.

5. Intermittency of heating and cooling energy demand, compared with monthly solar and wind energy production for Belgium

Not only solar and wind fluctuate, also energy demand fluctuates. This is most visible for space heating and cooling. We normalize average monthly solar, wind, heating and cooling degree days to 100% for Belgium. For cooling degree days we see a sharp peak around July, but due to the limited amount of cooling degree days only limited energy is required for cooling. More significant is heating power, for which heating degree days are an indicator. We observe that demand in winter is more than the percentages provided by wind and solar. For solar there is a supply demand problem that can only be overcome if cheap seasonal storage is available. Wind looks more suitable to power heating pumps, provided it can meet demand and its daily fluctuations are covered by storage or back up power. Figure 5 shows the seasonal fluctuations renewable energy production and space heating/cooling demand.

Figure 5. Belgium average monthly percentage of solar and wind energy // heating degree and cooling degree days.

6. Conclusion

While the seasonal intermittency is limited in most parts of the world, say in the region between latitudes of -40° and 40°, it significantly impacts solar energy in countries outside this region. Fortunately wind is in general stronger during winter periods, which we illustrated for Belgium. Also energy demand can fluctuate significantly over season. For regions closer to the equator space cooling demand goes up in summer, while for locations more towards the poles space heating demands rises in winter. To deal with day to day fluctuations backup power and/or energy storage will be essential.

Acknowledgement: many thanks to:

• the PVGIS team for open access to PVGIS 5.1 data from 2005 to 2015. Source - "Photovoltaic Geographic Information System (PVGIS)", European Commission, Joint Research Centre (JRC). Online tool available at re.jrc.ec.europa.eu/pvgis.html - PVGIS Ⓒ European Communities, 2000-2020.
• the Renewables.ninja team for open access to the v1.1 data-set of Europe, covering 1980-2016 data for weather, solar and wind on a country basis. Website: www.renewables.ninja. Version 1.1 of the original data-set is licensed under CC BY-NC 4.0.

References:

History:

July 2023 July 2020