Below is a simple guide to lead you to better interpret electricity generation data published from various sources. From time to time, we see people using data from different sources as a counter argument to other people’s claims, without knowing the difference of methodology of these sources. This guide aims to help you see through the noisiness and confusion set up by many Energiewende skeptics.
Variations of the Data
The federal working group on energy balance publishes its prediction of the annual energy statistics just before Christmas every year. The data, of course, is thus subject to change. For example, only until next September when the renewable surcharge billing is published will people be able to know the exact amount of electricity generated by renewable sources in the previous year.
Gross and Net Electricity Generation
If you check the data from Fraunhofer, the electricity generation share of renewables that year was 33.4%. But a federal report shows the same number to be 29%.
The main reason for this difference is because Fraunhofer publishes the net electricity generation data, while the federal working group on energy balance publishes the gross electricity generation data. Conventional power sources consume relatively more electricity than renewables to maintain operational, so the share of renewables in net electricity generation will always be slightly higher than in gross electricity generation. This difference is similar to the difference between primary and final energy consumption (but not the same).
Since what is useful is the net electricity generation, the data published by Fraunhofer gives more insight on how green Germany’s power system really is. However, net electricity generation is harder to calculate, and might contain higher error than gross electricity generation. As more precise information released, the data of 2016 from Fraunhofer was modified again and again in year 2017. This manner has been and will be repeated over and over.
Effects of Cross Border Electricity Markets to Domestic Electricity Consumption
One of my previous article was about cross border electricity markets in Europe. In that article I discussed the overall effect of cross border electricity market when compared to an autarky scenario: more coal power and less fossil gas power for the continent as a whole, which is an adversary from environmental perspectives. But the effects of cross border electricity trade on an individual nation was not made specified. This is an important insight if you want to investigate domestic electricity consumption.
For electricity exporting nations such as Germany, electricity export occurs usually when there is abundant renewable output on the grid. During these events, operators of flexible conventional power plants such as fossil gas plants and some state of art hard coal plants minimize power output or even shut them down. Meanwhile, the wholesale prices of neighboring nations determines what kind of power sources can be exported with an economic benefit. Renewables and brown coal power plants are the ones that will benefit the most during a cross border trade, thus the export portfolio will be mainly composed of these two electricity sources.
As I have suggested in my previous article, in an autarky scenario nations with high VRE could reduce its coal power output to the most, so cross border trading is certain to have higher carbon intensity for these nations. A vivid depiction is shown below: brown coal electricity generation could be 37% less in 2016 under an autarky scenario, if all the additional residual load flexibility in such scenario was performed by brown coal power plants. Although an autarky scenario, technically feasible but poli-economically impossible, will never exist in Germany, this kind of analysis still give us a hint on how important residual load flexibility is.
But what would cross border electricity trade do to domestic electricity consumption? We expect that, if a nation’s export portfolio has a higher share of coal than net electricity generation portfolio, then subtracting that data away should yield a cleaner portfolio. Since Germany exported about 50 TWh of electricity and had 42.9% of coal in net electricity generation in 2016, the domestic net electricity consumption portfolio would be cleaner than total net electricity consumption portfolio if it had exported more than 20 TWh of coal that year. Of course one has to dig into the real time data and elaborate the detailed work to tell whether that was true.
For electricity importing nations, things are even more complicated. A nation with few VRE installations such as France, electricity import from abroad during peak hours is competing with domestic fossil gas power plants as the marginal supply. Since fossil gas power stations emit half the amount of CO2 than coal power plants, if the import portfolio has less than 50% of coal it will very likely be cleaner than domestic fossil gas power plants.
On the other hand, a nation with more VRE installations might also have excess VRE output when importing electricity. Then the import electricity might begin to compete with hard coal or even brown coal power plants for marginal supply, so the import portfolio will almost certainly make the power system cleaner. This is usually the case when cross border electricity trade occurs between countries such as Germany and Denmark.
Last but not the least, one must always remember that power plants inside a nation can simultaneously import and export electricity to nearby nations. For example, France (when its nuclear fleet is in ideal condition) usually exports electricity to southern Europe while importing electricity from Germany. Different power stations in a nation have different marginal costs, so some might prefer to export electricity even when the nation as a whole prefers to import electricity, or vice versa. Therefore any sophisticated analysis of real time domestic electricity consumption portfolio requires a deeper look in the real time commercial flow contracts, which explains why it is difficult to find such analysis on the internet.
