Project
MAST
The project addresses a critical challenge in modern software-intensive systems: balancing different dimensions of sustainability in digital infrastructures.
Full Project Name: Managing Sustainability Tradeoffs
Acronym: MAST
Programme: ITEA – AI & Digital Twins (ITEA Call 2022)
Grant Agreement: 22035 MAST
Duration: 12/2024 – 12/2027
Websites:
Project Overview
The MAST – Managing Sustainability Tradeoffs project addresses a critical challenge in modern software-intensive systems: balancing different dimensions of sustainability in digital infrastructures.
As software systems grow in complexity, development teams must increasingly reconcile conflicting priorities between maintainability, performance, energy efficiency, and carbon footprint. Optimizing one of these aspects can often negatively impact another, creating difficult trade-offs for system architects and developers.
MAST develops tools, methodologies, and AI-driven approaches to help organizations better understand and manage these sustainability tradeoffs. By integrating digital twins, advanced analytics, and software engineering methods, the project aims to support better decision-making during the design, development, and operation of complex systems.
The project brings together an international consortium of research institutions and industry partners from Portugal, Denmark, and the Netherlands, working collaboratively to deliver solutions that reduce carbon footprint, minimize technical debt, and improve long-term system sustainability.
Cleanwatts Digital Role
Cleanwatts Digital (CWD) contributes to MAST by developing and testing AI-driven digital twin approaches for energy systems, focusing on operational optimization and predictive maintenance within distributed energy infrastructures.
The company will leverage its CWD Operating System (CWD OS) and Living Lab – Regulatory Sandbox (ERSE) to pilot and validate new tools and methodologies developed in the project.
Key contributions include:
- Development and testing of digital twin models for CWD energy management algorithms
- Integration of new energy assets and communication protocols into the CWD platform
- Improvements to CWD APIs for enhanced interoperability
- Deployment of AI-based predictive maintenance and operational optimization solutions
Strategic Relevance for CWD
MAST strengthens CWD’s capabilities in AI-enabled energy system optimization and digital twins, two key pillars of the company’s technological roadmap.
The project also supports the expansion of the CWD Living Lab, enabling real-world experimentation with innovative energy technologies and digital solutions. This will help establish a pilot base for future innovation projects and support the evolution of the CWD Operating System.
Key Innovations
- AI-driven digital twin models for energy system optimization
- Tools for evaluating sustainability tradeoffs in software-intensive systems
- Integration of new energy assets and communication protocols into digital platforms
- Improved interoperability through enhanced APIs
- Predictive maintenance methods to reduce operational risks and downtime
Expected Impact for CWD
Participation in MAST will enable CWD to:
- Reduce operations and maintenance (O&M) costs through predictive maintenance
- Improve system reliability and reduce downtime
- Strengthen the technological capabilities of the CWD Operating System
- Expand the Living Lab and pilot environments for future innovation initiatives
- Create new opportunities for pre-commercial digital energy solutions
Funding Disclaimer
This work has been carried out in the framework of the MAST (Managing Sustainability Tradeoffs) project, funded by the national public authorities of the participating ITEA member countries under the EUREKA Network framework.
