Probabilistic cost prediction for submarine power cable projects

Abstract

It is estimated that Europe alone will need to add over 250,000km of transmission capacity by 2050, if it is to meet renewable energy production goals while maintaining security of supply. Estimating the cost of new transmission infrastructure is difficult, but it is crucial to predict these costs as accurately as possible, given their importance to the energy transition. Transmission capacity expansion plans are often founded on optimistic projections of expansion costs. We present probabilistic predictive models of the cost of submarine power cables, which can be used by policymakers, industry, and academia to better approximate the true cost of transmission expansion plans. The models are both generalizable and well-specified for a variety of submarine applications, across a variety of regions. The best performing statistical learning model has slightly more predictive power than a simpler, linear econometric model. The specific decision context will determine whether the extra data gathering effort for the statistical learning model is worth the additional precision. A case study illustrates that incorporating the uncertainty associated with the cost prediction to calculate risk metrics - value-at-risk and conditional-value-at-risk - provides useful information to the decision-maker about cost variability and extremes.