began thinking about this issue about two years ago. Due to tremendous changes
in the energy market, operators of combined cycle power plants need to cycle
their assets much more frequently. The main reason for this trend is the ever-increasing
share of renewable sources in the energy mix. Because the feed-in of renewable
plants depends on weather conditions, their power output fluctuates. Controllable
generation technologies like combined cycle power plants need to compensate for
the gaps in order to secure a stable power supply based on demand. This leads
to new operating profiles with higher flexibility requirements and to increasingly
complex dispatch optimization.
Closing the gap
maximize the revenue of combined cycle power plants, energy traders are making
great efforts to exploit the predicted demand profiles, prices, and weather
reports. The right timing of dispatching offers significant economic advantages.
But to ensure this, traders always need to know the available physical
performance of the assets in their portfolio. The more accurately energy traders
knows how fast the plant can reach a specific power output at a specific moment,
the better they can maximize their revenues and also ensure grid stability.
Forecasting the start-up
on these considerations, we wanted to develop a digital solution that could forecast
the potential power output of an asset for a specific time depending on defined
parameters. The start-up performance of a combined cycle power plant depends primarily
on the thermal state of the thick-walled components and the turbine’s
capabilities under specific ambient conditions like air temperature, humidity,
and pressure. So the key to accurate forecasting is an algorithm that can calculate
expected start-up performance based on historical plant data and that also
avoids the need for complex physical models.
Developing a digital application
In the framework of our next47 accelerator program, we developed an application call “Smart Start,” a browser-accessible application with an intuitive user interface for dispatch scenario planning. The core of Smart Start is a highly sophisticated machine-learning algorithm that evaluates the entire operational history of the power plant. Key performance parameters like start-up time, power output, and fuel consumption can be determined by factoring in the predicted weather conditions for each desired scenario.
Delivering the individually predicted start-up curve
Smart Start delivers the individually predicted start-up curve based on the desired start-up moment as input and the historical plant performance data while avoiding long processing times. It uses data from the plants’ SPPA-T3000 control system via a cloud connection. For calculation purposes, the application automatically creates a connection to the data acquisition system and starts its calculation based on the current state of the power plant. A prototype of this digital application is already successfully running at the combined cycle power plant in Herdecke, Germany.
Unleashing the asset’s full potential
Start can be an important advantage for plant owners by increasing their responsiveness
to volatile energy markets. The improvements include enhancement of start-up
and power output prediction accuracy, increased revenues due to additional trading
opportunities, and lower imbalance costs. We see Smart Start as an important
tool for unleashing the full potential of an existing asset. Because it’s data-driven,
the algorithm can be integrated either in plant controls or directly in
dispatch optimization software.
us, it’s another prime example of how digitalization can help improve and maximize
revenues in the energy industry. What do you think? Your questions and comments