Project description
A renewables boost for power grids
A successful green transition hinges on the integration of renewable energy sources into the power grid. However, solar power, wind power and hydropower are vulnerable to microclimatic conditions. Their generation capacity varies based on the weather. It is this variability that makes renewable energy sources difficult to integrate. In this context, the EU-funded RESPONDENT project will address these challenges by developing AI and machine learning power generation and demand forecasting algorithms. In addition to renewable energy power conversion models, the project will consider weather data from space (Copernicus Earth observation), site-specific weather data and multiphysics models. Additionally, RESPONDENT will build Galileo-enabled phasor measurement units to measure the electrical signals of the grid in a precise and synchronised way.
Objective
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.
Fields of science
- 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
Keywords
Programme(s)
Topic(s)
Funding Scheme
HORIZON-IA - HORIZON Innovation ActionsCoordinator
15341 Athina
Greece
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.