Descrizione del progetto
Una spinta alle rinnovabili per le reti elettriche
Il successo della transizione verde dipende dall’integrazione delle fonti di energia rinnovabile nella rete elettrica. Tuttavia, l’energia solare, l’energia eolica e l’energia idroelettrica sono vulnerabili alle condizioni microclimatiche ela loro capacità di generazione varia in base alle condizioni meteorologiche. È questa variabilità che rende difficile l’integrazione delle fonti energetiche rinnovabili. In questo contesto, il progetto RESPONDENT, finanziato dall’UE, affronterà queste sfide sviluppando algoritmi di IA e di apprendimento automatico per generare energia e prevedere la domanda. Oltre ai modelli di conversione dell’energia rinnovabile, il progetto prenderà in considerazione i dati meteorologici provenienti dallo spazio (osservazione della Terra del programma Copernicus), i dati meteorologici specifici di un sito e i modelli multifisici. Inoltre, RESPONDENT costruirà unità di misurazione dei fasori abilitate da Galileo per misurare i segnali elettrici della rete in modo preciso e sincronizzato.
Obiettivo
Renewable Energy Sources Power FOrecasting and SyNchronisation for Smart GriD NEtworks MaNagemenT.
Renewable energy sources (RES) play a major role to the EU’s aspiration to transform to a climate-neutral economy. Their integration into the power grid is pivotal to the green transition and to the decarbonisation of the energy sector. However, as the most commonly used RES (solar, wind and hydropower) are also weather-dependent, their power generation capacity varies according to the local microclimatic conditions. This power production variability makes RES difficult to integrate into the power grid and to provide seamless, stable and secure amounts of power. On the other hand, power demand also affects the power grid operation, since there must always be a supply/demand balance in the power grid. Grid power imbalances can cause frequency fluctuations and other unwanted transient phenomena, which can compromise grid stability and operation. For that matter, advanced grid monitoring techniques have been developed, employing phasor measurement units (PMUs) to measure the electrical signals in a precise and synchronised way, based on a reliable timing reference. Yet, currently, no Galileo-based applications on PMU timing exist.
In the above framework, RESPONDENT comes to address the challenges of RES power generation forecasting, demand forecasting and smart power grid monitoring and supply/demand balancing. An AI/ML RES power generation forecasting algorithm is proposed, exploiting both Copernicus EO and site-specific weather data, along with renewable energy power conversion models. Furthermore, an AI/ML – multiphysics model for power demand of certain communities is also developed. Lastly, RESPONDENT will build a Galileo-enabled PMU and develop a monitoring module, in order to test and verify the advantages offered from the Galileo timing and synchronization services in smart grid monitoring, power balancing and overall operation.
Campo scientifico
- engineering and technologyenvironmental engineeringenergy and fuelsrenewable energysolar energy
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectrical engineeringpower engineeringelectric power transmission
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Parole chiave
Programma(i)
Argomento(i)
Meccanismo di finanziamento
HORIZON-IA - HORIZON Innovation ActionsCoordinatore
15341 Athina
Grecia
L’organizzazione si è definita una PMI (piccola e media impresa) al momento della firma dell’accordo di sovvenzione.