DNV GL Issues Recommendations for Novel Materials in Renewable Energy
DNV GL’s Materials Research Programme has published a new position paper that looks at the latest developments in materials and shows the need for establishing long term reliability of novel materials deployed in renewable energy systems.
Attention has focused to a large extent on cost and efficiency and to a lesser extent on the key parameters of durability and reliability of materials in renewable energy over 20 or 30 years. This gap can be filled by new approaches and predictive modelling tools for long-term performance assessment, DNV GL said.
According to DNV GL, a rapid upscaling of wind, both onshore and offshore, and solar energy in the energy mix in the coming decade and beyond can only be made possible with concomitant developments in materials, including:
- alternative semiconductor material in photovoltaics (e.g. halide perovskite),
- new PV module coatings, materials and coatings for the harsh conditions of CSP and thermal energy storage,
- hybrid reinforcements of wind turbine blades,
- cheaper permanent magnets in gearless direct drive wind turbines,
- a range of innovative battery chemistries in energy storage systems.
For any of these novel materials to be commercially viable, they should not only offer a cheaper and better alternative to existing materials, but must also be readily available and reliable over long periods of time. “Trade-offs between availability, cost and performance may be made, but in all cases long-term reliability is a key requirement for materials used in the energy industry,” said Liu Cao, researcher at DNV GL Research & Innovation and lead author of the position paper.
Materials reliability is mainly a function of long term degradation, which is difficult to model in service conditions and often not adequately assessed in the testing of systems. More specifically, DNV GL provides evidence for the following insights:
- Single average degradation rate is an inadequate metric of long-term performance
- Qualification tests are insufficient for lifetime assessment
- Accelerated laboratory tests may not reveal all the degradation mechanisms
- Real-time monitoring is valuable, but unable to predict lifetime alone
To address these challenges, DNV GL proposes the following:
- Coupling empirical models to a fundamental understanding of degradation.
- Transforming rich and increasingly ubiquitous sensor data into predictive models.
- Deploying a Bayesian network approach to bring together diverse sources of knowledge of relevance to the performance and degradation of materials.