The current power grid is heavily congested, as a result of more itermittent power loads and less controllable sources and drains of power. This issue is exacerbated by the planned introduction of large quantities of solar and wind capacity, introducing more uncertainty into the grid operation problem. Current expansion plans are not able to cope with the expected loads, nor can they keep the delivery-guarantees currently kept while also decarbonizing the grid. This is a problem for the green transition, and more efficient grid expansion can help mitigating this problem.

The proposed solution leverages extra information found using the scenario approach to grid operation, instead of the Monte-Carlo simulations currently used in grid expansion studies. Doing so exploits the extra information the scenario approach yields with regard to reliability, and operational performance. By optimizing robustly against all samples we find more useful improvements that actually push the boundary of operation, where the Monte-Carlo approach might be less likely to find those same improvements given that is overly focuses on samples of little consequence.

To test this hypothesis, a simple graph representation of a power grid is constructed, and a grid expansion program either using the scenario approach or the Monte-Carlo approach is applied to this graph, resulting in two sets of candidate modifications that either program found to be most promising. To validate the results, operational performance using both the scenario approach and the Monte-Carlo approach are computed, the empirical violation probabilty is computed and computation time is compared.