Operation, Performance and Maintenance of PV Systems

Photovoltaic power generation is pivotal in the transition to a clean energy system and the achievement of a climate-neutral economy. Its current contribution to global electricity demand is around 5% and is rapidly growing. PV already represents a share of more than 8% of the electricity generation in some EU Member States/Associated countries while penetration levels are expected to reach soon double-digit in Europe. It is within this scenario that the PV sector will ensure that the installed power capacity in GW can also reliably generate TWh of electricity for an extended lifetime.

Therefore, project results are expected to contribute to all of the following expected outcomes:

• Increase PV system performance, reliability, security and flexibility under various topology and operating conditions with enhanced digitalisation
• Increase utility-friendly integration of PV generation into the European energy system at high-penetration levels and the profitability of PV systems

Proposals are expected to demonstrate technical solutions, processes and models, which increase a PV system's operational performance, stability and reliability. The introduction of novel PV technologies and novel PV system designs makes the need of increased field performance and reliability a continuous industry demand.

More specifically, proposals are expected to:

• Demonstrate integrated multi-aspect sensing (optical, thermal, electrical) into PV modules to suppress degradation, detect unwanted operating conditions and avoid failures with emphasis on achieving high MWh/Wp (e.g. shade tolerant; more advanced electronic design with in-module components).
• Demonstrate smart control/tracking systems (e.g. coupled with real-time monitoring data, forecasting, EMS, etc.) for performance optimisation in specific PV applications (e.g. “self-protection” under extreme events in harsh environments like dust/snow storms).
• Demonstrate hybrid or integrated monitoring-diagnostic imagery solutions for maximum spatiotemporal granularity and diagnostic resolution. Multispectral imagery inspections linked with electrical signature, synchronisation of field techniques with monitoring.
• Apply edge AI and Big Data to improve the energy yield (advanced module control, self-reconfigurable topologies, etc.), module and plant models, monitoring and yield forecasting considering user behaviour and modelling of the entire electricity system including storage.
• Build large (time and scale-wise), wide (including not only yield but multisensory operational, thermal, mechanical and environmental data) and possibly publicly available datasets to enable, foster and empower AI for Digital PV at European scale.
• Demonstrate automated and predictive PV asset management software based on sensor-data-image fusion and/or AI / Machine learning techniques to reduce human effort and increase trustworthiness of current PV asset management software.
• Enable AI-based energy trading at plant level, taking care of specific climates /applications / conditions (snow, dust, environmental pollution, water…)/user behaviours.

Activities are expected to achieve TRL 6-7 by the end of the project – see General Annex B.