A Bayesian Decision Support System in Energy Systems Planning

TL;DR

This paper develops a Bayesian decision support system combining Gaussian Process emulators and state-space models to evaluate energy policy impacts with uncertainty quantification, demonstrated on a UK county's low-carbon transition planning.

Contribution

It introduces a novel Bayesian framework that integrates GP emulators with classical state-space models for energy systems decision support, enabling uncertainty propagation across interconnected models.

Findings

Successfully coupled energy, demand, and price models for policy assessment.

Quantified operational cost impacts of policy and market changes.

Provided a decision support tool for low-carbon infrastructure planning.

Abstract

Gaussian Process (GP) emulators are widely used to approximate complex computer model behaviour across the input space. Motivated by the problem of coupling computer models, recently progress has been made in the theory of the analysis of networks of connected GP emulators. In this paper, we combine these recent methodological advances with classical state-space models to construct a Bayesian decision support system. This approach gives a coherent probability model that produces predictions with the measure of uncertainty in terms of two first moments and enables the propagation of uncertainty from individual decision components. This methodology is used to produce a decision support tool for a UK county council considering low carbon technologies to transform its infrastructure to reach a net-zero carbon target. In particular, we demonstrate how to couple information from an energy model, a heating demand model, and gas and electricity price time-series to quantitatively assess the impact on operational costs of various policy choices and changes in the energy market.