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GridEye Scalable Transactive Distribution Systems

Sector: Power Generation (CCGT) • Location: Switzerland

Source: EU Funding & Tenders Portal

Project
Ended

Coordination of different players in active distribution systems by increasing the penetration of distributed energy resources and rapid advances on the aggregators, microgrids and prosumers with private territory individuals establishes new challenges in control and management systems from the owners’ point of views. Undertaking digitalization of future distribution systems, GiSTDS introduces an

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The project “GridEye Scalable Transactive Distribution Systems” is an infrastructure initiative in the Power Generation (CCGT) sector, located in Switzerland. Taiyo aggregates data on it from EU Funding & Tenders Portal.

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Description

Description

Coordination of different players in active distribution systems by increasing the penetration of distributed energy resources and rapid advances on the aggregators, microgrids and prosumers with private territory individuals establishes new challenges in control and management systems from the owners’ point of views. Undertaking digitalization of future distribution systems, GiSTDS introduces an edge computing framework based on GridEye, the core production of DEPsys, which provides real time visibility and monitoring. Relevant drawbacks in the distribution system management platforms in handling the scalability of players, look ahead preventive management systems regarding contingency condition and lack of physical boundaries for third party entities (aggregators) will be addressed by GiSTDS. The main novelties of this project in comparison to the GridEye are: 1) Developed P2P trading module provides automated double auction negotiation in real time fashion which enables all private entities with and without specific physical boundaries to participate in local and flexible electricity markets. 2) Modification of GridEye’s modules to address the scalability and resilient operation in both the normal and contingency conditions. 3) To present a look ahead energy managements schemes for the operators, GiSTDS will be equipped to the forecasting module based on auto-regressive with exogenous variables (ARX) and machine learning techniques such as long short term memory (LSTM) and recursive neural network (RNN). Therefore, GiSTDS based on modified and developed modules explores comprehensive distributed framework for control, monitoring and operation of energy systems with multiple dispersed players in different scales. The edge computing solutions in GiSTDS eectively digitalis energy systems and creates major opportunities in terms of avoiding big data concerns and getting a bottom-up monitoring approach for the network supervision.

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100%

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