A distributionally-ambiguous two-stage stochastic approach for investment in renewable generation

The optimal expansion of a power system with reduced carbon footprint entails dealing with uncertainty about the distribution of the random variables involved in the decision process. Optimization under ambiguity sets provides a mechanism to suitably deal with such a setting. For two-stage stochastic linear programs, we propose a new model, that is between the optimistic and pessimistic paradigms in distributionally robust stochastic optimization. When using Wasserstein balls as ambiguity sets, the resulting optimization problem has nonsmooth convex constraints depending on the number of scenarios, and a bilinear objective function. We propose a decomposition method along scenarios that converges to a solution, provided a global optimization solver for bilinear programs with polyhedral feasible sets is available. The solution procedure is applied to a case study on expansion of energy generation that takes into account sustainability goals for 2050 in Europe, under uncertain future market conditions.