DNV GL says a new position paper from its Materials Research Program looks at the latest developments in materials and shows the need for establishing long-term reliability of novel materials deployed in renewable energy systems.
Following the climate accord in Paris at COP21, the world is looking to a rapid upscaling of wind and solar energy in the energy mix in the coming decade and beyond. DNV GL says this will only be made possible with concomitant developments in materials, including the following:
– alternative semiconductor material in photovoltaics (e.g. halide perovskite);
– new PV module coatings, materials and coatings for the harsh conditions of concentrating solar power and thermal energy storage;
– hybrid reinforcements of wind turbine blades;
– cheaper permanent magnets in gearless direct drive wind turbines; and
– a range of innovative battery chemistries in energy storage systems.
For any of these novel materials to be commercially viable, DNV GL says they not only should 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,” states 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 says it 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; and
– 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; and
– deploying a Bayesian network approach to bring together diverse sources of knowledge of relevance to the performance and degradation of materials. For example, DNV GL says a Bayesian network model, MARV, has enabled the assembly of diverse data for pipeline risk assessment. A similar approach could be applied to risk assessment for renewable energy and energy storage systems.
DNV GL’s position paper, entitled “Advanced materials in renewable energy – tackling the reliability challenge,” is available here.
