Record prices on the electricity market: Now secure RE market values on the futures market?
- Nov 17, 2021 12:32 pm GMT
In the wake of the historically high electricity prices, the conventional marketing strategies for electricity from renewable energies (RE) are being scrutinized. Up to now, direct marketing of renewable energies has been synonymous with spot marketing, an industry standard that has hardly been questioned. Why should a plant operator expose themselves to the futures market risk when the EEG subsidy secures the market value on the spot market? Coupled with upcoming contract adjustments in connection with Redispatch 2.0, some direct marketers are now offering guaranteed market values for the future and hedging them on the futures market. Fundamental scenario swarm analyzes can be used to answer the circumstances under which this is a good idea.
Current market situation: market values via EEG funding for large-scale systems
Since July 2021 at the latest, the RE market values have exceeded the EEG funding for those systems that have been awarded contracts in the tenders since 2018. These plants currently receive a market premium of EUR 0 / MWh. Even with systems with higher EEG subsidy rates, the share of the revenue from direct marketing is becoming more and more relevant. Figure 1 shows this relationship.
Figure 1 shows the development of the monthly market values since 2019 in comparison to the range of mean funding values per round of tenders (“value to be applied”) for large-scale systems. The bandwidth ranges from 43 to 66 EUR / MWh. The market values exceeded this range by July 2021 at the latest. The increase in the relevance of market revenues is the main reason that many players are now considering hedging this high price level through their direct marketers on the futures market.
How high are future market values that can be hedged today on the futures market?
This depends on the one hand on the level of electricity prices on the futures market and on the other hand on the relative value of the feed-in profile of the individual systems. While the price level on the futures market can be easily hedged with standard products on the stock exchange or as a bilateral trading transaction, this cannot usually be done for the future value of the feed-in profile. With the valuation date November 2, 2021, the following mean values for onshore wind, offshore wind and solar can be given from Table 1: For this winter, market values fluctuate between EUR 126 / MWh and EUR 183 / MWh, depending on the technology and month.
In the next three quarters the value will drop to between 80 and 90 EUR / MWh. These futures market valuations will continue to decline until 2024 and will then be partially below the values to be applied in the EEG tender. This means: According to the current status, a date-based price fixing for the winter months remains a relevant option for revenue optimization even for older large-scale systems with values to be applied well above 100 EUR / MWh (10 ct / kWh). For the summer or the full year of 2022, this applies primarily to newer large-scale systems with lower EEG subsidy rates.
Does it make sense to fix these market values, which are very high in historical comparison?
Today nobody can say with certainty whether and when lower prices will appear again on the spot market - rather, the futures market price is the average estimate of the electricity traders: inside, what the spot market of the future will look like.
Log in to market value or wait for the spot market value: How do you make a decision?
It makes sense to orientate yourself on the most likely future scenario from today's point of view. The spot prices for the next few months and years are particularly dependent on further developments, among other things
- the demand for electricity (including the effects of economic developments in view of the Covid-19 pandemic),
- the weather (merit order effect of wind and solar feed-in, temperature-dependent heating power demand) and
- the commodity price development (gas, coal and CO 2 prices).
In the short term (i.e. monthly and quarterly) the availability of conventional power plants can also play a significant role. A fundamental electricity price analysis basically offers an aid to consistently depict the price influence of different development paths of these parameters. At the present time, however, it is not yet possible to say anything about the development of the weather for 2022, for example. If only a single base scenario is calculated, an average weather year as possible must therefore be assumed. Based on the history, this average weather year is assigned the highest probability of occurrence.
Similarly, there is uncertainty about the further development of the economy and the commodity markets. The settlement prices of the futures market provide an indicator here. These prices represent an up-to-date but risky estimate of future market prices. So here too there is uncertainty as to the extent to which the market-traded contracts carry an idiosyncratic risk and deviate from the fundamental value. In order to counter these uncertainties of the short to medium term market development, an analysis of fundamental scenario swarms for the classification of the futures market offers itself.
Decision-making aid 2.0: fundamental scenario swarm analysis briefly explained
In 2018, the futures market price was mostly below the spot market price. In 2021, however, the spot market price was significantly higher than the prices for the same year on the futures market. As can also be seen in Figure 2, the year 2022 was also not valued too highly on the futures market in the past; prices only increased extremely in the summer of this year. Scenario swarms can show how likely a certain spot price level is from today's perspective.
The scenario swarm analysis is based on a combination of fundamental and stochastic analysis models (Monte Carlo simulation), with which over 1,000 scenarios are calculated. In each run, the above-described input parameters for the development of electricity prices are taken into account anew.
Quantifying price risks
An example of this is the choice of weather development for the year 2022. Instead of an average weather year, a different weather development from a historical sample of over 30 years is modeled with each run. For example, the price risks of a particularly cold winter or a sunny summer can be quantified.
In addition, there are interdependencies between the input parameters that have to be mapped in the analysis models. One example of this are commodity prices, which, like the demand for electricity, are influenced by economic developments.
Figure 3 shows an example of how over 1,000 scenario variations will affect the marketing value for solar systems in 2022. From this “swarm” of scenarios, a frequency distribution and thus statements about quantile values (P values) and fluctuation ranges can be derived.
Probability of the marketing value for solar systems in 2022
Energy traders are currently focusing in particular on further gas price and weather developments. We therefore consider the influence of these parameters on the frequency distribution of possible marketing values for solar systems in 2022 as an example in Figure 3. The P-50 marketing value is just under 90 EUR / MWh, the probability distribution is asymmetrical, possible upward deviations are slightly larger than downward deviations . In principle, fixed marketing values of around and over 90 EUR / MWh are good offers. However, especially with solar systems, a monthly analysis is relevant, since a monthly optimization (high market values in winter, lower market values in summer) often further increases revenues, so an individual analysis depending on the value to be applied is advisable.
However, the results of the swarm modeling are not independent of the respective futures market level; like all price forecasts, they have a "half-life". The commodity price fluctuations, which are the basis for swarm scenarios, depict the volatility around the currently valid price level. As soon as the price level changes substantially, the modeled P-50 value and the distribution also change. A longer-term informative value is, however, basically possible; the results of the swarm modeling can continue to be used in the case of small to medium changes. For this purpose, the calculated 1,000 scenarios are reassessed based on the changed parameters.
A current example based on the defining “price driver natural gas price”. In Figure 4, the previously described reassessment of the 1,000 scenarios has been carried out using this crucial parameter.
The three different results from the same scenario swarm calculation are shown:
- The dashed gray distribution curve depicts all modeled scenarios.
- The blue distribution takes into account the results of all modeled electricity price scenarios that meet the conditions "below average ( cold ) temperature" and "particularly high gas prices" .
- In contrast, the red distribution shows possible electricity price developments in the case of above-average warm temperatures and particularly low gas prices .
How is Figure 4 to be interpreted?
The horizontal x-axis shows the deviation of the marketing value per scenario from the average expected marketing value in 2022 in EUR / MWh (= mean value of the gray distribution curve around the value 0). The range of fluctuation within the totality of all calculated scenarios is very large (= width of the gray distribution curve). If the year 2022 were characterized by cold weather and high gas prices, over 80 percent of the results would be above the expected mean value of all scenarios (see area of the blue distribution and mean value of the gray distribution).
On the other hand, warm weather and low gas prices would lead to lower prices than expected with a probability of around 80 percent. The left and right ends of the distribution curve are also not symmetrical. In the case of high gas prices and cold weather, the price tends to increase more than in the opposite case. From today's perspective, however, a deviation of more than +/- 40 EUR / MWh seems unlikely.
Conclusion: Use of scenario swarm analyzes for RE marketing
As can be seen in the example described above, the effect of individual parameters on the frequency distribution of expected electricity prices can be quantified using scenario swarm analyzes. On the one hand, this data supports the decision as to whether and under what circumstances the RE market values should be logged in today or the usual spot marketing is cheaper. In addition, scenario swarm analyzes can help you decide whether a switch to other direct marketing is advisable. In other direct marketing, system operators receive a certificate of origin, but no market premium even in the event of falling market values. Scenario swarm analyzes show how likely it is that the market values will fall below the applicable value again.
* This graphic is for illustrative purposes. Exact information and labels are available in the concrete scenario swarm analysis.
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