Summary
The energy sector is going through a transformation from a one directional centralized system in which energy is produced by a limited number of big power plants into a highly decentralized ecosystem due to the introduction of small-scale renewable energies and the electrification of mobility. In this situation, power distribution grid operators’ current workflows and IT systems cause inconsistent data management which complicates their planning and operation activities. Therefore, essential grid planning and operation workflows involve significant manual input. As a result, e.g. the evaluation of a connection request for a single new wind turbine often takes multiple days. Additionally, Smart Grid Technologies cannot be used to their full extent to avoid curtailment of renewables in congestion management, and to substitute conventional expensive grid capacity expansions.
We have developed the Intelligent Grid Platform (IGP), the only integral software solution using machine learning algorithms to allow grid operators to reduce their operational and network expansion investment costs by giving them the means to clean and connect their data, digitize and automate their grid planning and operation activities and to more easily integrate smart grid technologies to help their operation processes and avoid expensive network capacity extensions. As a result, they save time and engineering resources by up to 70% and reduce grid operation and expansion costs by 40% to 60%. In this project, we will advance the IGP applications for online gird operation.
We have developed the Intelligent Grid Platform (IGP), the only integral software solution using machine learning algorithms to allow grid operators to reduce their operational and network expansion investment costs by giving them the means to clean and connect their data, digitize and automate their grid planning and operation activities and to more easily integrate smart grid technologies to help their operation processes and avoid expensive network capacity extensions. As a result, they save time and engineering resources by up to 70% and reduce grid operation and expansion costs by 40% to 60%. In this project, we will advance the IGP applications for online gird operation.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/867602 |
| Start date: | 01-05-2019 |
| End date: | 31-08-2019 |
| Total budget - Public funding: | 71 429,00 Euro - 50 000,00 Euro |
Cordis data
Original description
The energy sector is going through a transformation from a one directional centralized system in which energy is produced by a limited number of big power plants into a highly decentralized ecosystem due to the introduction of small-scale renewable energies and the electrification of mobility. In this situation, power distribution grid operators’ current workflows and IT systems cause inconsistent data management which complicates their planning and operation activities. Therefore, essential grid planning and operation workflows involve significant manual input. As a result, e.g. the evaluation of a connection request for a single new wind turbine often takes multiple days. Additionally, Smart Grid Technologies cannot be used to their full extent to avoid curtailment of renewables in congestion management, and to substitute conventional expensive grid capacity expansions.We have developed the Intelligent Grid Platform (IGP), the only integral software solution using machine learning algorithms to allow grid operators to reduce their operational and network expansion investment costs by giving them the means to clean and connect their data, digitize and automate their grid planning and operation activities and to more easily integrate smart grid technologies to help their operation processes and avoid expensive network capacity extensions. As a result, they save time and engineering resources by up to 70% and reduce grid operation and expansion costs by 40% to 60%. In this project, we will advance the IGP applications for online gird operation.
Status
CLOSEDCall topic
EIC-SMEInst-2018-2020Update Date
27-10-2022
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