The multistage solution is very important to achieve optimal hydrothermal economic dispatch considering the uncertainty of renewable energy sources. In data-driven settings, only some historical trajectories are avail...
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The multistage solution is very important to achieve optimal hydrothermal economic dispatch considering the uncertainty of renewable energy sources. In data-driven settings, only some historical trajectories are available and the probability distribution is unknown. A data-driven scheme for multistage stochastic hydrothermal economic dispatch with Markovian uncertainties is proposed in this paper. Then a data-drivendistributionallyrobuststochasticdualdynamicprogramming (DDR-SDDP) is proposed to tackle the corresponding computational intractability, where the conditional probability distributions are estimated by using kernel regression. The out-of-sample performances are improved by distributionallyrobust optimization on a Wasserstein distance-based ambiguity set. Furthermore, a scenario aggregation method is designed to reduce the computational burden. Numerical results for a practical regional power system in China are presented and analyzed to verify the effectiveness of the proposed method.
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