In their article “Modeling Time-dependent Randomness in Stochastic Dual Dynamic Programming“ , Nils Löhndorf and Alexander Shapiro, compare the effectiveness of two prominent approaches on how to represent inflow uncertainty in models of long-term hydropower planning. The planning problem is formulated as a multistage stochastic program and solved with QUASAR®’s dual dynamic programming solver.
Numerical results indicate that discretizing the stochastic inflow process to a scenario lattice yields lower system cost than sample average approximation when evaluating decisions out-of-sample. The article also demonstrates how dynamic risk measures can make the solution of a stochastic programming model robust against the model risk of using a mis-specified stochastic process.
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 Löhndorf N, Shapiro A. 2019. Modeling time-dependent randomness in stochastic dual dynamic programming. European Journal of Operational Research 273(2), 650-661.