2017/08/26 by Jianqiu Huang, Huang, Jianqiu, Kezhuo Zhou +3 · 1 citation
Decision Sciences · Engineering · #Electric Power System Optimization #FOS: Mathematics #Optimization and Control (math.OC) #Risk and Portfolio Optimization #Water resources management and optimization
paper · pdf · doi:10.48550/arxiv.1708.07930
openalex publication_date 2017/08/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we focus on a data-driven risk-averse multistage stochastic programming (RMSP) model considering distributional robustness. We optimize the RMSP over the worst-case distribution within an ambiguity set of probability distributions constructed directly from historical data samples. The proposed RMSP is intractable due to the multistage nested minimax structure in its objective function, so we reformulate it into a deterministic equivalent that contains a series of convex combination of expectation and conditional value at risk (CVaR), which can be solved by a customized stochastic dual dynamic programming (SDDP) algorithm in this paper. As the size of collected data samples increases to infinity, we show the consistency of the RMSP with distributional robustness to the traditional multistage stochastic programming. In addition, to test the computational performance of our proposed model and algorithm, we conduct numerical experiments for a risk-averse hydrothermal scheduling problem, the results of which demonstrate the effectiveness of our RMSP framework.