Comparison of statistical and optimization models for projecting future PV installations at a sub-national scale
Spatially-disaggregated projections of new solar photovoltaic (PV) installations are essential for planning electricity grids and managing the electricity system at large scale. Such projections at sub-national level can be obtained by statistical models or by electricity system optimization models, but there is barely any study that compares the performances of these approaches. This study aims to compare methods for projecting PV installations at a level of 143 districts in Switzerland, using a simple extrapolation method (as a benchmark of the common practice today), a multiple linear regression model, two spatial regression models, and a spatially-explicit optimization model (EXPANSE) with various features to account for policy. The performance of different approaches is evaluated retrospectively for 2012–2020, using multiple accuracy indicators. The evaluation results show that statistical regression models, which account for socio-demographic and techno-economic characteristics as predictors of future PV growth, overall perform better than simple extrapolation or optimization. Although commonly used, extrapolation has the highest variability in accuracy, indicating the least robust performance. The optimization model tends to underestimate PV installations in its least-cost scenarios, if the role of policy is not considered. Incorporating solar PV policies and renewable electricity generation targets increases the overall accuracy of the optimization model at a national level, but not necessarily at a spatially-explicit level. We thus conclude that statistical models are preferred over extrapolation or optimization models for projecting future PV installations at a sub-national scale.
