Propagating uncertainty in a network of energy models

TL;DR

This paper introduces an efficient framework for propagating uncertainty through interconnected energy models with high-dimensional outputs, demonstrated via a UK case study on low-carbon infrastructure planning.

Contribution

It presents a novel computational approach for uncertainty propagation in complex energy model networks, adaptable to larger and more intricate systems.

Findings

Framework effectively propagates uncertainty in energy model networks

Applicable to large, complex energy planning scenarios

Demonstrated with a practical UK case study

Abstract

Computer models are widely used in decision support for energy systems operation, planning and policy. A system of models is often employed, where model inputs themselves arise from other computer models, with each model being developed by different teams of experts. Gaussian Process emulators can be used to approximate the behaviour of complex, computationally intensive models and used to generate predictions together with a measure of uncertainty about the predicted model output. This paper presents a computationally efficient framework for propagating uncertainty within a network of models with high-dimensional outputs used for energy planning. We present a case study from a UK county council considering low carbon technologies to transform its infrastructure to reach a net-zero carbon target. The system model considered for this case study is simple, however the framework can be applied to larger networks of more complex models.