Optimising strategies for integrating intermittent renewable generation

Transitioning to high proportions of renewable energy generation will be necessary in order to achieve substantial decarbonisation of energy usage across all sectors.

However, the integration of these renewable sources into modern energy systems presents substantial challenges, due to the inherent variability of many prominent renewable technologies and the associated uncertainty this introduces.

Existing energy systems and infrastructure must be adapted to support this intermittent generation, for instance via the introduction of auxiliary technologies such as energy storage, demand-side response, and sector coupling. However, these mitigation techniques impose additional costs to the provision of low-carbon energy.

Energy systems modelling provides insight into the nature and behaviour of future, fully renewable energy systems, and can be used to identify minimal cost strategies for integrating renewables into the power mix to support policy making for system development.

The proposed research seeks to investigate how advanced concepts of statistical and modelling uncertainty can be incorporated into energy systems modelling.

It aims to develop analysis methods that are uncertainty aware and thus able to determine strategies which will be robust and perform optimally under the uncertainties faced by the energy system.

Such methods would improve the reliability of energy system model results and thus the trust which policy makers can place in their strategy recommendations.