Energy companies are embracing digital twin technology, like one large heat power plant that engaged us to optimize its operations. The project was to reduce fuel costs and improve efficiency at both day-ahead and balancing wholesale energy markets. For this purpose, we decided to build a decision-making support system covering both production and financial functions. We developed a simulation model to rather accurately calculate all key technological parameters (pressure, temperature, fuel consumption, etc.) at each point of a technological process.
Our developers gradually checked the digital twin parameters against those of a real-life equipment and finally achieved the required accuracy (of max 2% deviation). After that, the customer started to use the twin for "what-if" analysis, i.e. to run different modes and evaluate their effect. The digital twin was then followed by an optimization model that allowed the customer to calculate the best-fit per-hour load for the entire plant with regard to day-ahead market operations — all with due consideration of weather and heat consumption forecasts, as well as regulatory restrictions. In addition, the model was configured to minimize losses caused by operations in the balancing market and reduce fuel consumption rate.