Power Price Scenario Swarms
Risk assessments for power and capture prices
How can you ensure that your decisions are crisis-proof? Numerous risk factors such as the weather, unsettled commodity markets and the overall economic development influence the success of your project. To give you more security in your projects, we support you with well-founded risk indicators.
What are power price scenario swarms?
For a scenario swarm, we calculate a power price scenario more than 1,000 times using our Power2Sim energy market model. Very specific unpredictable fundamental model parameters such as hourly wind speed fluctuate based on historical values and trends such as global warming. The results can be used to interpret the probability distribution of e.g. power prices, revenues or risks. In the process, the distributions from the scenario swarms dovetail with our market-tested standard electricity price scenarios, the Energy BrainReports.
- Receive probability distributions for baseload prices and capture prices in annual resolution, characterised by "P-values", for example the P90 price. These key figures can refer to one year (annual P-price) or an average over several years (period P-price).
- As with our standard power price scenarios, you choose the one that suits you best from up to four different scenarios: Central, Tensions, Relief and GoHydrogen. These scenarios form the basis for your scenario swarm calculations.
- With the data delivery, you receive a tool to assess your opportunities and risks per year or for a sequence of years.
- All assumptions, the detailed description of the methodology, the definitions of the key figures as well as the visualisation of the results are available as a PDF file.
- Two hours of Q & A included: Because we want our power price scenario swarms to be plausible and easy to understand for you.
Swarm-based power price scenarios
With the Energy BrainSwarms Y+3, you can assess the probability of future spot market prices for the liquid delivery years with the help of a frequency distribution. This is supplemented by associated "P values" (exceedance probabilities) for various price indices, such as baseload price, market value wind and capture price solar.
In procurement optimisation and the marketing of electricity quantities, you can determine more precisely whether a certain share of them is hedged on the futures market or runs into the spot market.
Weather fluctuations, economic crises with price crashes, temporary price spikes and the effects of climate change lead to high volatility. This harbours both opportunities and risks. As a result, power prices increasingly deviate from their foreseeable development or the long-term expected values.
The Energy BrainSwarms Y+30 power price scenario swarms allow you to assess power price risks up to the year 2060. You receive a frequency distribution of the expected spot market prices up to delivery year Y+30.
You operate a 10 MW wind turbine with EEG subsidy entitlement. For the next months and years, you will notice a high power price level on the forward market. This is higher than the subsidy rate for your plant. Now is the best time to optimise your revenues. You can remain in the EEG subsidy system with spot market value-based remuneration, but also switch to subsidy-free green power marketing and secure prices. The high price volatility on the electricity markets complicate assessments.
With our fundamental model Power2Sim, we run through various constellations of weather, economic and commodity price effects in over 1,000 scenario runs. The result is a probability distribution of the market value of wind for the period you have selected.
The probability distribution can be used to answer these questions: How likely is it that the market value of wind will fall below a certain price level in the coming months and years? Should I secure it today or wait?
An investor in renewable energies values his unsubsidised solar plant based on average capture prices. However, time and again, individual weather and market situations hold back the revenue. Volatile markets and extreme weather phenomena make investments more uncertain. What revenues can the investor expect even in unfavourable weather conditions?
In our power price scenario swarms, we also take parameters that are difficult to predict into account. These include, for example, weather, short-term trend deviations in demand and price shocks on the commodity markets. Each capture price is thus given a probability of occurrence. Our period P90 price, for example, indicates which revenue level will be reached with a probability of 90 percent in the assessment period.
The period P90 capture price allows the investor to estimate a "low case" revenue level. This allows him or her to value the asset robustly and safely.