Key data
Duration: 24 months
Overall budget: 813.9k €
Partners: 3
Demonstrators: Monte das Flores PV Farm (Portugal) and Green Energy Park (Morocco)
Goals
The main goal of AI4PV is to increase the operational performance of PV plants, by combining Digital Twin, AI and Data analytics solutions. The expected result from this project is a set of tools for PV plant O&M and Asset Managers to:
- Increase operational reliability and efficiency via early detection of failures and high precision diagnosis;
- Enhance economic performance by reducing downtime and detecting underperformance conditions that can affect the energy production.
The suite of tools developed within the project will be able to determine the root cause of reliability and performance problems and recommend actions to optimally manage these issues from a technical and economical prospective. PV plants have numerous fault sources hard to be identified by technicians: in this sense AI4PV attempts to provide a better insight on the identification and classification of faults and underperformances, focusing on inverter-related malfunctions and efficiency, soiling effect, transformer efficiency and cell performance. The suite of tools will be integrated, tested and validated in two different real-PV farms that offer different and unique operating conditions.
Ultimately, the goal of ProLight is to investigate how to accelerate the implementation of these refurbishment measures while ensuring that all relevant socio-economic, environmental and cultural aspects are considered along the way. With this innovative approach, the project hopes to increase the percentage of renewable energy consumed in the housing sector as well as to reduce energy consumption per capita.
Highlights
- Project coordination by EDP NEW;
- Consortium formed by 3 partners from the Iberian Peninsula (INESCTEC-Portugal and ISOTROL-Spain).
- The developed solutions will be demonstrated in EDP Renewables’ PV farm (Montes das Flores – Portugal).
Demonstrators
The AI4PV’s solutions will be demonstrated and validated in two different real PV farms, which differ for operating conditions and technologies: Monte das Flores plant in Portugal owned and operated by EDP Renewables and the Green Energy Park in Morocco. The diversity between these two pilots, in terms of technology installed, environmental and operating conditions, will allow to demonstrate the versatility, adaptability and replicability of the developed solutions.
This activity will leverage from the expertise of EDP, which will make use of its expertise as energy utility related to O&M PV practises to define safety procedures sided by detailed test programs to enable a full technological and economic assessment of the solutions.
Role NEW R&D
EDP NEW is the project coordinator of AI4PV: it will ensure the proper development of the project and the fulfilment of the envisioned results and outcomes. Besides the coordination in WP5, EDP NEW is also responsible for the dissemination and communication of the project’s results.
In WP1 EDP NEW will contribute to the definition of the use cases’ requirements and the system architecture leveraging from its experience as energy utility.
In WP2 EDP NEW will assist ISOTROL in the creation of the Digital Twins, providing insights on the different components and the needed data from the SCADA system to fine-tune the different models.
In WP3 EDP NEW will support INESCTEC in the development of data-driven AI algorithms, by granting access to the necessary operational data. Moreover, within this WP EDP NEW will develop and implement a decision-aid layer on top of the digital twin to prioritise the different activities, based on their economic impact as well as different AI algorithm for fault detection and diagnosis.
EDP NEW will also lead WP4 about test and validation of the different solutions, providing access to the PV plant facility in Portugal (owned by an affiliated company, EDP Renewables) for all the testing and will provide support during their execution. EDP NEW will also draw the conclusions and insights yielded from the tests by working on a cost-benefit analysis, contributing to the definition of novel O&M policies.
Partners
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