Sunny prospects

Currently at Next Kraftwerke, solar power makes up almost 50 percent of the portfolio. That's around 3,000 megawatts (updated 6/2019). Using our Virtual Power Plant, we market this power in auctions on the day-ahead market and balance out any fluctuation with intraday trades. On intraday markets, power is continuously bought and sold for delivery on the same day, making these markets a useful tool to balance volatile PV power on short notice. In Germany, a position can be traded up to 5 minutes prior to delivery, making very quick adjustments to power volumes possible to keep balancing costs low. 

At Next Kraftwerke, real-time data from our PV assets across Europe are among the sources we use to create our forecasts. The data is delivered from the PV assets using our Next Box remote control unit or through other interfaces such as the data logging APIs in the asset’s inverter. Using these various interfaces, we receive data in real time and are continuously informed about the feed-in from all 1,100 assets in our Virtual Power Plant. However, this data on its own is not enough for an accurate forecast. To make the forecast as precise as possible, we supplement the real-time data with geographic data from the assets and information pertaining to previous days, such as older feed-in data from the PV assets. 

Finally, weather predictions from multiple sources – such as data on cloud development – are incorporated into the equation for our solar forecast. The variety of data from diverse sources and time periods improves the forecast, helping to create a precise feed-in pattern. Anyone familiar with this pattern can use it to make forecasts and predict how much power can be delivered at a certain time in the future.

To process such a large amount of complex, poorly-structured high-frequency data, we created an algorithm that is tailored exactly to our needs. We began several years ago with simple scripts. Today, we have a sophisticated algorithm that collects and processes data from many assets in several countries. The IT department, traders, and analysts work closely together to improve the algorithm using each department’s unique expertise. 

It only takes seconds to process and calculate the data, thanks to our IT team. Saving, processing, checking, and correcting the data when needed – the IT department makes sure that this complex process goes off without a hitch. This is important because it is the only way for our forecasts to stay ahead of the market and for our traders to balance out deviations from the forecast at a better price. 
 

Our evaluation of the data for the PV forecast is conducted in two steps. The data is first converted to a unified standard format so it can be accessed by the algorithm. Next, the algorithm checks if the data makes sense – perhaps an asset had gone offline, causing distorted measurements. This is known as a plausibility assessment. If something doesn’t add up, we take a closer look to see if the deviation has an explanation. 

In the next step, the formula sorts the assets into groups which creates a portfolio effect. If a particular asset does not perform as expected in a group, the other assets can cover the difference. This reduces forecasting errors because the profile of a group is less volatile than the profile of a single asset. Once the assets have been grouped, all other data such as weather indicators and the current feed-in of each asset is added to the mix and the algorithm creates the forecast.