A Day in the Life… How Short-Term Power Traders Operate
17 Oct 2022
Alexander Reinhold, CEO
Weather-based power markets, characterized by extreme volatility within short time horizons, require energy market participants to calibrate their trading strategies accordingly.
Therefore, both asset-backed and asset-less power traders continuously analyze market trends and patterns to derive trading decisions and execute orders in volatile short-term power markets.
Let’s explore the key differences between asset-backed and asset-less traders and learn what their typical workflows look like.
Asset-Backed Traders vs. Asset-Less Traders
Asset-backed traders (renewable assets in particular) are dependent on power production forecasts and production actuals when making volume decisions. They usually submit forecasted volumes in the Day-Ahead auction and use Intraday markets to correct forecast deviations.
In this way, whenever asset-backed traders end up producing less/more power than they sold in the Day-Ahead auction, most European regulators require them to balance out their position by buying/selling the outstanding volumes in Intraday markets.
Let’s suppose one sold 15 MWh in the Day-Ahead auction for the 10-11 product. However, according to the latest forecast, actual production will equal 12 MWh during that hour. Accordingly, 3 MWh shall be bought in Intraday markets before gate closure.
In turn, asset-less power traders (often referred to as proprietary or simply prop traders), buy and sell arbitrary volumes of power provided that their balance is equal to zero at gate closure.
Compared to asset-backed traders, asset-less traders can open a speculative position in the Day-Ahead auction with the aim to close it in the Intraday markets against a positive spread (please note that some transmission system operators do not allow asset-less traders to participate in the Day-Ahead auction).
Let’s say a trading company buys 10 MWh in the Day-Ahead auction at 100 €/MWh (MCP) for the 10-11 product. Then, they sell 10 MWh in Intraday markets at an average of 105 €/MWh — with a 5 €/MWh spread — to get 50 € of profit.
Let's zoom in on the typical workflows of both asset-backed and asset-less traders.
Short-Term Power Traders Workflows
Data processing is one of the three essential steps in the short-term power trading value chain and a key to identifying spread opportunities in Day-Ahead and Intraday markets.
Both asset-backed and asset-less traders predominantly analyze the same technical and fundamental data, relying on some sort of data processing pipelines and third-party data suppliers. Manual, computer-assisted and automated trading strategies are commonly exposed to the following data:
Virtual power plant (only relevant for asset-backed traders), depicting actual production and status of each solar and wind asset in a trader’s portfolio.
Asset production forecasts (only relevant for asset-backed traders), indicating continuous volume forecasts of a trader’s production portfolio. Usually, traders aggregate insights from at least two forecast providers to increase their accuracy.
Country-level solar/wind production and demand forecasts, commonly obtained from third-party data providers and ENTSO-E Transparency, an open-source energy data platform, managed by transmission system operators (TSOs).
Raw weather data such as cloud covers, pressure, temperature, wind speeds, etc.
Live order books, where all market orders are recorded.
TSO publications, indicating the market balance.
Urgent market messages (UMMs), announcing outages that may influence the price.
Power exchange websites/APIs (e.g. EPEX Spot, Nordpool) to obtain key indicators like traded volumes, market prices, etc.
The majority of all short-term power trading decisions are still made by traders on the basis of visually inspecting the available data and applying trade rules. These decisions are usually translated into direct orders manually or communicated to a third-party trading application by means of simple parameters.
Most short-term power traders are still reliant on manual data analysis when deriving trading decisions that may allow for delayed reactions to data updates, intuition-based decision making and tedious order execution.
However, given advancements in computing power and data science, short-term power markets will inevitably move towards higher degrees of automation, replacing manual data analysis and human decision making by fully automated trading engines with seamlessly integrated data processing pipelines and computer-assisted order execution.