Skip to main content
Go to the home page of the European Commission (opens in new window)
English en
CORDIS - EU research results
CORDIS

Decision making methodologies for optimal design and operation of energy systems

Project description

Bridging gaps for sustainability in energy systems

In the complex landscape of energy systems, balancing cost-efficiency, environmental responsibility, and living standards is paramount. Yet, challenges like computational demands, lack of holistic approaches, and real-time data use hinder optimisation efforts. Supported by the Marie Skłodowska-Curie Actions (MSCA) programme, the DMOptfES project will integrate machine learning, optimisation, uncertainty analysis, and real-time operation to create a comprehensive framework. Emphasising system integration and optimisation under uncertainty, it promises to revolutionise energy system management. With industrial applications and a focus on sustainability, it is poised to shape a greener, more prosperous European economy.

Objective

The central challenge in energy systems is to balance cost-efficiency, environmental responsibility, and living standards. Multi-objective optimization in such systems is critical for striking compromises among stakeholders and ensuring competitiveness and sustainability. As energy systems face uncertainty, volatility, and environmental regulations, holistic optimization becomes more critical.

However, three main challenges currently hinder energy system optimization: complex models demanding substantial computational power, a lack of holistic and robust approaches, and the use of real-time data for proper operation and analysis. Although each of these challenges has been addressed individually, no contribution was able to bring them all together in a consistent methodological framework. This is the main objective of this fellowship.

This fellowship will be conducted at IST/University of Lisbon under the supervision of Prof. Henrique Matos, and aims to bridge these gaps by integrating machine learning, systems optimization, uncertainty analysis, and real-time operation. It seeks to develop advanced surrogate generation techniques, emphasize system integration and holistic analysis, optimization under uncertainty, and the use of real-time data for operation. These methods will be applied to real industrial challenges in an industrial secondment. This research project will develop methods and tools to address such challenges and contribute to a sustainable, profitable, and responsible European economy.

Fields of science (EuroSciVoc)

This project has not yet been classified with EuroSciVoc.
Be the first one to suggest relevant scientific fields and help us improve our classification service

Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

See all projects funded under this funding scheme

Call for proposal

(opens in new window) HORIZON-MSCA-2023-PF-01

See all projects funded under this call

Coordinator

INSTITUTO SUPERIOR TECNICO
Net EU contribution
€ 172 618,56
Address
AVENIDA ROVISCO PAIS 1
1049 001 Lisboa
Portugal
Activity type
Higher or Secondary Education Establishments
Links
Total cost
No data

Partners (1)