Project description
Shining a light on renewable energy integration challenges
As the EU races to meet its ambitious climate and energy targets, the challenge of integrating renewable energy sources looms large. Europe’s climate and energy targets demand high integration of renewable energy sources, making operational flexibility more important than ever to manage the variability and uncertainty of renewable generation. With this in mind, the MSCA-funded THINKPV project aims to enhance photovoltaic (PV) generation forecasting by developing a probabilistic system based on machine learning. By leveraging data from the distribution network, this innovative solution will improve accuracy, supporting large-scale PV system integration and predictive maintenance. Operational testing carried out at the Electric Energy Systems Laboratory of the National Technical University of Athens will demonstrate the project solution's effectiveness for a PV Operation and Maintenance (O&M) company in Italy.
Objective
The European Union policy for climate and energy imposes significant targets for a high integration of renewable energy sources in the period from 2020 to 2030. System operators have to deal with operational flexibility to respond to variability and to uncertainty of the renewable generation, ensuring the network reliability and security. While significant efforts have been made into the developing accurate forecasts, much work remains to integrate the forecasting in the electric system operations. The successful incorporation of forecasts into grid operation emerges as an important challenge. Accurate photovoltaic (PV) generation forecasts are major themes of the research roadmap of many international task forces, as Smart Grids SRA 2035 to support the flexibility increasing of the power systems. In this context, the project aims to support large scale integration of PV systems in countries with a high solar resource and a significant potential of small capacity PV systems such as Greece. The Institute of Communication and Computer Systems (ICCS) is the most important Hellenic research institute, committed to support Hellenic Electricity Distribution Network Operator S.A. (HENDO) that is dealing with a radical modernization of the existing network. The THINKPV project encourages the ICCS and its industrial partners to facilitate PV grid integration by the development of a probabilistic forecasting system based on machine learning, taking advantage of data that can be measured in the distribution network, in order to improve forecast accuracy compared to the state of art. The model will be assembled into a solar power forecasting system that will be operational at the Electric Energy Systems Laboratory (EESL) of the ICCS to operate directly with tools for simulating power system operations. A prototype of operational solar forecasting systems will be demonstrated for HENDO, providing also a training program for its efficiency and correct application.
Fields of science
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectrical engineeringelectric energy
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- engineering and technologyenvironmental engineeringenergy and fuelsrenewable energysolar energyphotovoltaic
Programme(s)
Funding Scheme
MSCA-IF-EF-ST - Standard EFCoordinator
106 82 ATHINA
Greece