Providing REliability and DegradatIon sCience for the TW-scale PV industry

The development of the PV industry is simply impressive. It has proven that PV technology is able to provide reliable power over several decades in the challenging outdoor environment, and it has demonstrated a tremendous ability to cut costs, making PV the lowest source of electricity in many markets. This has led to extremely rapid growth. With the current development, PV is well on the way to become the largest source of electricity on the planet within two decades. However, at this point in time the massive scale-up coincides with two other trends, leading to important research challenges: 1. A rapidly accelerating technological innovation cycle in PV modules, and 2. A substantial expansion and diversification of deployment environments and configurations for PV. This combination substantially challenges the historically established reliability learning cycle for PV. Reliability is crucial for PV, investments rely on PV power plants to maintain high performance for decades. The previous test methods hace served the industry well, but are expensive and time-consuming. KSP PREDICT is set up to enable accelerated deployment of PV power plants based on new technologies, in new applications and/or in new environments which currently have little available data. We will develop new methods able to rapidly generate performance and reliability data. KSP PREDICT builds on IFE's state-of-the-art methods based on production and sensor data time series, selected laboratory measurements and modelling. The development of the new methods will be supported by generating performance and reliability data for two environments where long-term reliability is sorely lacking: Nordic conditions and floating PV. Data access is a major challenge in the international R&D community, but is secured by the consortium. The KSP PREDICT partners are IFE, Endra, Energeia, Equinor, Multiconsult, Sunlit Sea and TGS-Prediktor.

The development of the PV industry is simply impressive. It has proven that PV technology is able to provide reliable power over several decades in the challenging outdoor environment, and it has demonstrated a tremendous ability to cut costs, making PV the lowest source of electricity in many markets. This has led to extremely rapid growth. With the current development, PV is well on the way to become the largest source of electricity on the planet within two decades. However, at this point in time the massive scale-up coincides with two other trends, leading to important research challenges: 1. A rapidly accelerating technological innovation cycle in PV modules, and 2. A substantial expansion and diversification of deployment environments and configurations for PV. This combination substantially challenges the historically established reliability learning cycle for PV. Reliability is crucial for PV, investments rely on PV power plants to maintain high performance for decades. The previous test methods hace served the industry well, but are expensive and time-consuming. KSP PREDICT is set up to enable accelerated deployment of PV power plants based on new technologies, in new applications and/or in new environments which currently have little available data. We will develop new methods able to rapidly generate performance and reliability data. KSP PREDICT builds on IFE's state-of-the-art methods based on production and sensor data time series, selected laboratory measurements and modelling. The development of the new methods will be supported by generating performance and reliability data for two environments where long-term reliability is sorely lacking: Nordic conditions and floating PV. Data access is a major challenge in the international R&D community, but is secured by the consortium. The KSP PREDICT partners are IFE, Endra, Energeia, Equinor, Multiconsult, Sunlit Sea and TGS-Prediktor.