Sustain3

Sustain3 strives to advance the evolution of intelligent, sustainable, and economically viable polygeneration systems, positioning them as transformative solutions within energy supply and management systems for both industrial settings and energy districts/communities. Recognizing the pivotal role of economic and environmental considerations, as well as physical limitations, the project addresses three primary challenges that will pave the way for a broader implementation of these systems, thereby contributing to the decarbonization of society: 

• Holistic Environmental Focus: Sustain3 embraces a holistic approach by integrating Life Cycle Assessment (LCA) criteria into the core of its objectives. A comprehensive set of LCA criteria will be proposed, extending beyond the conventional CO2 emissions indicator. These criteria are envisioned to cover diverse aspects of environmental impact and sustainability. Importantly, they will serve as Key Performance Indicators (KPIs) within the optimization models. This strategic integration of LCA criteria as KPIs aims to guide decisionmaking processes throughout the energy system's design and operation phases. By extending the evaluation criteria beyond CO2 emissions, Sustain3 seeks to create a robust foundation for optimizing polygeneration systems, ensuring a well-rounded consideration of environmental factors in tandem with operational efficiency.
• Predictive Control: Acknowledging the significance of predictive control in enhancing system efficiency, Sustain3 endeavors to overcome challenges associated with dynamic operational scheduling. The optimization models, integral to the project's objectives, will undergo adaptation to derive optimal operational strategies for implementation in predictive control. This adaptation process involves a refinement of the time step used in operational strategies, transitioning from hourly to minute-level. Additionally, the incorporation of machine learning techniques (AI) will be tackled, aiming to predict energy demands and climatic conditions more accurately. By reducing the time step and leveraging AI-driven predictions, the project aims to enhance the accuracy and responsiveness of the predictive control strategy, ensuring dynamic adaptability to changing environmental and operational conditions within the polygeneration system.