A full year ago the Flowbased capacity calculation methodology went live in the CORE region. To mark this milestone and celebrate the anniversary, this post will look at the process stability and price convergence of Flowbased.
Minimizing the price spread, or in other words maximizing the price convergence, is one of the goals of a more efficient capacity calculation in the region. At the time that full price convergence (e.g. a price spread of zero) is achieved, the market did not hit any constraint in the capacity domain.
The go live of FB CORE at business day 2022-06-09 coincided with an ongoing energy crisis in Europe, one of the worst in recent memory. I thought it would be interesting to take a look at how the price spread in the region held up in those times.

Definitions and data remarks#

Lets start off with a definition: the “hourly price spread” is defined as the difference between the maximum and minimum price within one hour within a certain region on the Day Ahead market.
In this post I focus on the CORE region, however I have excluded Poland from all analysis. The reason for this is that Poland has been applying heavy additional restrictions on its import and export of electricity in the past months. The polish TSO PSE says they must conserve coal fuel due to fuel shortages and need these restrictions for operational security. This has resulted in vastly different prices compared to the rest of the region. As you can imagine this is not related to flowbased. For more data on this please view my dashboard on it.

Excluding Poland, the CORE region left is thus defined as the bidding zones of: Austria (AT), Belgium (BE), Czech Republic (CZ), Germany/Luxemburg (DE_LU), France (FR), Croatia (HR), Hungary (HU), Netherlands (NL), Romenia (RO), Slovenia (SI) and Slovakia (SK).
All data is based on the day ahead prices published on the ENTSOE transparency platform. I have used data from business day 2022-06-09 up to 2023-05-31. The dataset constitutes 8568 timestamps, which means there are no hours missing in that period.

Process Stability#

The Flowbased process transparently publishes whether the calculations were successful for all hours of a given business day. When a calculation fails there are two backup options to deal with it: Spanning and Default Parameters.
Spanning means that a mathematical average is chosen between the results of the hours before and after the failure. This then bridges the data gap. This is only applicable if that average is feasible and the gap is not too many consecutive hours.
Default parameters are a set of minimum capacity values that are safe to be released to the market for trading, in terms of net security. These are a kind of fallback to apply when all other efforts failed.
Since go live, only 13 hours failed in their final calculation. Of those 13, eight resulted in default parameters and 5 were spanning.
That is 0.15% of the total number of hours. A very good result for process stability after its first year!

DA Prices#

The DA prices in this period were rather extreme, both in their height and volatility. In figure 1 below the prices for CORE are plotted. This is from my generic live dashboard which does include Poland.

Figure 1: DA prices for CORE region, including Poland (live dashboard embed, interactive)

The figure is very busy, thus lets zoom out a bit. In figure 2 below, the mean of the whole region (excluding Poland) is shown per month. Especially in July, August and September of 2022 and again half way through December and early January, the prices exploded. This is explained by the European gas crisis, something that has already been extensively covered by others.

Figure 2: Average DA price per month in CORE region, excluding Poland (figure is interactive)

Still, sometimes the prices also plummeted in some zones. This is most likely due to high infeed of cheap renewables. In 3.3% of all timestamps (282 hours) at least one zone had a price below 1 EUR/MWh and in 2.3% of all timestamps (200 hours) it was even negative in at least one zone!
Luckily, the prices have dropped due to the easing of the gas crisis. Nonetheless, historically the prices keep being high. As a reminder, in the first five months of 2021 the average prices were 56, 49, 52, 60 and 57 EUR/MWh respectively, compared to 136, 142, 110, 103 and 83 EUR/MWh respectively for the first five months of 2023.

Price Spreads#

Prices in and of itself are interesting, however they are not really relevant to flowbased. What is more relevant, is the hourly price spread. As explained earlier, price spread is defined as the delta between the max and min price within an hour, within a region.
First lets take the monthly average again. This time, for absolute price spreads in EUR/MWh. This is displayed in figure 4 below.

Figure 4: Average DA price spread per month in CORE region, excluding Poland (figure is interactive)
The month of July has the largest absolute spread on average, while the months after it are in the 70-90 EUR/MWh spread range. The last three months are even in the 20-30 range. This is interesting since the mean of the overall prices swung much more. This can probably partly be explained by renewable infeed. July is generally more sunny and therefore cheap solar power is available in large abundance. However the installed generation capacity of renewables is not equally distributed across all zones. For example, the Netherlands, which has much more installed solar generation capacity than other CORE countries. The grid becomes strained trying to move all that solar energy to other zones, thus increasing price spreads.
This theory is reinforced by Figure 5, which shows a heat map of the price spread. To keep the figure readable the extremes are cut off at 200 EUR/MWh of price spread.

Figure 5: Price spread in CORE region as heat map

Here we can see that in July 2022 (but also a bit later) the highest price spreads are in the middle of the day. These are typically the hours that solar energy is the most active. Also, in the last two months of 2022 the afternoon hours have higher price spread when the sun is picking up again. These do have a smaller magnitude due to lower prices overall compared to summer of ‘22.

Price Spreads Performance#

So how do these price spreads play a role in the bigger picture? In figure 6 below, the absolute hourly price spread is binned in categories. Each category shows the percentage of timestamps that fall in it, out of the total set.

Figure 6: Absolute hourly price spreads in CORE region excluding Poland, binned in categories.

Here we can see that in 26.98% of the hours there was full price convergence (bar A), so a price spread of 0. For a large area such as the CORE one, in crisis times, that’s quite impressive!
In almost half of the time, 49.35%, the price spread was less than 100 EUR/MWh (split out over bar B, C and D). Although that doesn’t sound that much in these times, it is quite context dependent. A 100 EUR/MWh spread between 10 and 110 is much more impactful than, for instance, between 700 and 800 EUR/MWh.

To better look at this indicator in this context, we introduce a new indicator C%. This indicator is then defined as the “hourly price spread as expressed as a percentage of the mean price in a determined hour”. Or more formally:

$$ C_{\%}(t) = \dfrac{\overline{P_{spread}(t)}}{\overline{P(t)}}\cdot 100[\%] $$ in which the $\overline{bar}$ denotes the average over all CORE zones excluding PL at timestamp $t$

Now lets revisit the above example for a simple situation of two zones. A 10-110 spread is then expressed as a C% of 167%, while for the 700-800 spread the C% comes down to a mere 13.3%.

Next, we will bin the resulting numbers over the whole dataset. The result is shown in figure 7 below.

Figure 7: Hourly price spreads in CORE region excluding Poland, relative to hourly mean price, binned in categories.

Full convergence (or $C\%=0$) is still 26.98% of the total time. However, now we can also see that in 28.59% of the total time the price spread is between 0 and 25% of the mean price in an hour! Combined this means that in a little over half of the time (55.57%) the price spreads are less than a quarter of the mean price. Within the context of the extreme market conditions, this seems a quite good result!

An even better benchmark would be to compare this with a non flowbased approach. Unfortunately, this is not possible because this data does not exist anymore in the CORE region. Comparing with another flowbased region is also not possible yet, the nordic region is implementing flowbased but this is not done yet. Perhaps in the future it would be interesting to see how flowbased performs in terms of price spreads, in this different region and context. But for that we may have to wait at least another year.

So within the data we can see now, the system seems to perform very well. It will be interesting to see how the full (or almost full) price convergence share holds up the longer flowbased CORE marketcoupling runs.

Bonus Annex#

As a bonus, to check whether our earlier assumption about price spreads going up in sunny hours holds, we can also look at the C% indicator, averaged per hour of the day. This is shown in figure 8 below.

Figure 8: Hourly price spreads relative to hourly mean price, binned per hour of the day

Here we can see a very large peak at hours 13 and 14, which typically is the peak solar hour of the day. The effect on hour 12 is less strong, but still visible. This indeed seems to confirm the theory that price spread is influenced by solar infeed.