Energy-Aware Software: Intelligent Scheduling of Energy-Intensive tasks with Grid Congestion Data

Matthijs Vossen, Melle Koper, Roan Rosema, Scott Jochems.

Group 3.

This project addresses the integration of grid congestion awareness into software systems to enhance sustainability and grid efficiency. We highlight the importance of accurate load forecasting in preventing energy waste, avoiding renewable energy curtailment, and optimizing electricity use during surplus production periods. The solution extends the Carbon Aware SDK by incorporating real-time congestion data from the ENTSO-E platform, enabling software to intelligently schedule energy-intensive tasks during periods of excess energy generation. This approach aims to reduce energy waste and mitigate renewable energy curtailment. We include the implementation of a new congestion data type and corresponding extensions to the SDK, along with a command-line interface and API endpoints for practical integration. A proof-of-concept demonstrates the feasibility of the method through scheduling CI/CD tasks based on electricity surplus, highlighting potential environmental and economic benefits. Future developments proposed include enhanced data processing, full CI/CD pipeline automation, broader application to data centers, and adoption within industrial practices.