A cloud decision support system for the reduction of electrical power peaks utilizing digital twins and machine learning predictors

Abstract Transitioning from fossil fuels to sustainable energy sources, however necessary, will increase energy consumption, representing a significant challenge to grid capacity. One such area is the manufacturing of cast iron components for the automotive industry. Traditionally, these melting furnaces have been powered by fossil fuels, e.g., coke, producing a high carbon footprint. Replacing those furnaces with electric melting furnaces powered by sustainably sourced energy will reduce the CO 2 footprint but increase the electrical power peaks during the melting cycle. Inevitably, this transition will also affect the local grid and the communities where the manufacturing plants are located. This paper will present a proof-of-concept decision-support system powered by several predictive models on a cloud platform. The melting cycle in the melting furnace will produce a large electrical power peak during operation, which could coincide with other power peaks on the local grid. The aim is to shift the start of the melting cycle to maintain production output while minimizing the coinciding power peaks from a local grid standpoint. To achieve this, three predictions, or forecasts, are utilized. First is a prediction on future plant energy consumption, second is a prediction on local grid power consumption, and third is a prediction of future molten iron need. The energy consumption predictors utilize machine learning, while the molten iron need predictor utilizes a digital twin of the production environment. The three predictions are used to create an optimized melting schedule for the next eight hours, which is repeated every thirty minutes to react to changes in the predictors. Reducing peak power consumption brings benefits for individual companies in addition to the communities they operate in. By providing decision support for operators, they can make decisions based on data to reduce the load on the energy grid, for value both to industry and society. For future work, the proof of concept will be expanded to encompass more melting furnaces and additional production lines and allow the operators more control over the digital twin through the decision support system.