Evaluation of scenario-generation methods for stochastic programming

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Model-Free Renewable Scenario Generation Using Generative Adversarial Networks (2017, unknown): “able from real historical samples, then we show that they also exhibit the same statistical properties [29], [30].”
Generation Capacity Expansion Planning Under Hydro Uncertainty Using Stochastic Mixed Integer Programming and Scenario Reduction (2015, background): “Another set of techniques for scenario reduction in a more general context are the ones based on clustering [31], probability metrics [32], and importance sampling [33].”
Prosumer bidding and scheduling in electricity markets (2016, methodology): “[27]) by generating several scenario trees, running the models and checking the total costs.”
Generation of multivariate scenario trees to model stochasticity in power management (2005, background): “A systematic comparison, both theoretical and computationl, of these approaches has not been undertaken so far.”
Risk-Constrained Profit Maximization for Microgrid Aggregators With Demand Response (2015, methodology): “We have applied the method in [55]–[57] to perform these stability tests.”
Scenario Reduction for Futures Market Trading in Electricity Markets (2009, methodology): “A short overview of the most common methods is provided in [12].”