Summary
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.
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.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101130739 |
| Start date: | 01-12-2023 |
| End date: | 30-11-2027 |
| Total budget - Public funding: | - 1 591 600,00 Euro |
Cordis data
Original description
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.
Status
SIGNEDCall topic
HORIZON-MSCA-2022-SE-01-01Update Date
12-03-2024
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Organisations
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| SOUTH EAST TECHNOLOGICAL UNIVERSITY (Coördinator)
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| CLEANWATTS DIGITAL SA
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| CY.R.I.C CYPRUS RESEARCH AND INNOVATION CENTER LTD (CY.R.I.C CYPRUS RESEARCH AND INNOVATION CENTER L...
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| DHA Suffa University
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| ENERIM OY
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| HUAWEI TECHNOLOGIES (IRELAND) CO LIMITED
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| LUT University
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| MILLENNIUM INSTITUTE OF TECHNOLOGY & ENTREPRENEURSHIP
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| Trinity College Dublin
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| UNIVERSITY OF CYPRUS