Project description
Innovative cross-optimisation platform for energy, data, and telecommunications networks
While telecommunications and data networks rely on energy to function, energy grids require data for efficient operation. This interdependent relationship is central to the COALESCE project, which will promote the sharing of expertise and knowledge among professionals in the energy, data, and telecommunications sectors across academia and industry. This collaborative approach will enable the project to leverage insights from diverse perspectives to create a comprehensive and effective cross-optimisation platform. The proposed platform has the potential to transform how these critical infrastructures interact by optimising the interplay between energy grids and telecommunications/data networks, ensuring not only the efficient exchange of data but also their joint sustainability. COALESCE will be the first MSCA-funded project to develop a twin green-digital transition in alignment with EU priorities and UN Sustainable Development Goals.
Objective
COALESCE aims to develop a cross-optimization platform that enables integrated operation and interplay between the energy grids and the data and telecommunication networks. Telecommunication and data networks need energy, while energy grids need data to operate efficiently. This project will develop a framework that will optimize the interplay between energy grids and telecommunications and data networks in a way that both the infrastructure pillars (energy and telecommunications) are jointly sustainable and efficient. Through the Staff Exchange program, we will be able to exchange expertise and know-how between energy, data and telecommunications sectors across both academia and industry.
We will assess how the proposed architecture performs by validating the framework against 4 use case scenarios;
a) To investigate optimization algorithms for energy efficiency under simultaneous wireless information and power transfer (SWIPT) will be investigated in a local energy system context for a wireless sensor network.
b) To develop a novel framework for predicting and validating trading optimization strategies for in-house energy asset management, considering battery storage, flexible domestic demand, windfarm, solar cells etc,. using neural network and transfer learning-based models; while maintaining sustainable and secure exchange of data and user (or individual residence) portfolio.
c) To design novel set of measurement methodologies for the characterization of 5G/6G RAN's energy consumption and open data sets for analysis, parametric models of the energy consumption transfer function for the uplink and downlink and generative neural network models of the energy transfer function for the uplink and downlink.
d) To formulate joint data-energy-transportation robust/stochastic optimization algorithms considering computational load flexibility, intermittent energy generation and storage and multi-agent learning algorithms for collaborative e-transportation and SLES.
Fields of science
- engineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunicationstelecommunications networksmobile network5G
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensorssmart sensors
- engineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunicationstelecommunications networksdata networks
- engineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunicationsradio technology
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Funding Scheme
HORIZON-TMA-MSCA-SE - HORIZON TMA MSCA Staff ExchangesCoordinator
X91 K0EK Waterford
Ireland