Real-time carbon accounting and forecasting for reduced emissions in grid-connected processes

Real-time carbon accounting is crucial for advancing policies that effectively meet sustainability objectives. This work introduces a carbon tracking tool specifically designed for the European electricity grid. The tool collects hourly data on electricity consumption and generation, cross-border power exchanges, and weather information to assess the real-time environmental effects of electricity use, employing locally-specific emission factors for the generation sources. It utilizes weather data from various stations across Europe to produce week-ahead forecasts of carbon intensity in the grid. Predictions are created using a random forest regressor, integrated within the optimal controller of an operational industrial batch process. This prediction-based optimizer seeks to reduce total emissions tied to the process schedule's electricity consumption by implementing a rolling horizon strategy. By leveraging enhanced energy flexibility, the controller provides significant opportunities for load shifting and emission reductions, though at a higher cost. An industrial batch process demonstration highlights the potential of this approach, showing environmental savings ranging from 4% in grids with low renewable energy to 32% in nations that incorporate a substantial proportion of renewable energy in their energy mix.