Descripción del proyecto
Las renovables impulsan las redes eléctricas
El éxito de la transición ecológica depende de la integración de las fuentes de energía renovables en la red eléctrica. Sin embargo, la energía solar, la eólica y la hidroeléctrica son vulnerables a las condiciones microclimáticas. Su capacidad de generación varía en función de las condiciones meteorológicas. Esta variabilidad dificulta la integración de las energías renovables. Ante este contexto, el equipo del proyecto RESPONDENT, financiado con fondos europeos, abordará estos retos mediante el desarrollo de algoritmos de inteligencia artificial y aprendizaje automático para la generación de energía y algoritmos para la previsión de la demanda. Además de los modelos de conversión de energía renovable, tendrán en cuenta datos meteorológicos obtenidos desde el espacio (observación de la Tierra de Copernicus), datos meteorológicos específicos de lugares y modelos multifísicos. Además, en RESPONDENT se construirán unidades de medición fasorial compatibles con Galileo para medir las señales eléctricas de la red de forma precisa y sincronizada.
Objetivo
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.
Ámbito científico
- 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
Palabras clave
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Régimen de financiación
HORIZON-IA - HORIZON Innovation ActionsCoordinador
15341 Athina
Grecia
Organización definida por ella misma como pequeña y mediana empresa (pyme) en el momento de la firma del acuerdo de subvención.