Projektbeschreibung
Neues optimiertes Management von Multi-Energie-Systemen für Übertragungsnetzbetreiber
Die zuverlässige weltweite Bereitstellung von Energie für das Beheizen von Gebäuden, den Betrieb neuer Generationen von Elektrofahrzeugen und die Stromversorgung ist eine komplexe Aufgabe. Entscheidend für die künftige Emissionsreduzierung sind optimal aufeinander abgestimmte Multi-Energie-Systeme, die Energiedienste und -effizienz verbessern. Für diese Systeme entwickelt das EU-finanzierte Projekt MNRG Algorithmen und Technologien, um eine digitale, verteilte und Echtzeitüberwachung von Wärme, Mobilität und Strom zu gewährleisten. So können Übertragungsnetzbetreiber optimale Netztopologien wählen, die Netzauslastung genauer prognostizieren und Probleme in Echtzeit lösen.
Ziel
To achieve deep emission reductions in the European energy sector and in the heating sector in particular, stronger cross-sectoral linkages among the different energy carriers are needed. The main objective of proposed MNRG is improving operational flexibility by presenting a comprehensive digitalized, distributed and real-time monitoring of heat, mobility and electricity energy sectors which is required to deal with the uncertainty and variability of growing renewable resources and mobility. The main novelties of this project are: 1) MNRG provides an easy deployment and paired with unprecedented modularity for interconnected multi-energy systems by using GridEye's edge computing capabilities. 2) MNRG introduces a more practical way to manage and respond to the complex needs of the multi-energy systems in the presence of numerous energy components and devices. 3) The impact of DERs and EVs on the quality of energy sectors will be monitored in real-time and the flexibility in the operation of CHPs, use of reactive power controlling devices such as SVR and storage capacity of heat network will be considered as alternatives for technical challenges. 4) Real-time preventive and corrective actions based on dynamic feeder reconfiguration against over-voltage and congestion in the grid will be addressed. To determine network topologies optimally, methods based on machine learning or mathematical techniques will be implemented. 5) By MNRG, the behavior of feeders and transformers that are utilized by DERs and EVs will be predicted for DSO’s usage. In this regard, MNRG feeding by updated forecasts based on mathematical methods, like, ARIMA and deep learning methods, such as DRL and LSTM, will provide corrective and preventive actions. 6) MNRG can provide robust strategies for DSOs by using uncertainty modelling techniques such as robust optimization to tackle the volatility of uncertain parameters.
Wissenschaftliches Gebiet
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectrical engineeringpower engineeringelectric power distribution
- natural sciencesmathematicsapplied mathematicsdynamical systems
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- natural sciencesmathematicsapplied mathematicsstatistics and probability
- natural sciencesmathematicsapplied mathematicsmathematical model
Schlüsselbegriffe
Programm/Programme
Thema/Themen
Aufforderung zur Vorschlagseinreichung
Andere Projekte für diesen Aufruf anzeigenFinanzierungsplan
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Koordinator
1070 Puidoux
Schweiz
Die Organisation definierte sich zum Zeitpunkt der Unterzeichnung der Finanzhilfevereinbarung selbst als KMU (Kleine und mittlere Unternehmen).