In hydro predominant systems, the long-term hydrothermal scheduling problem (LTHS) is formulated as a multistage stochastic programming model. A classical optimization technique to obtain an operational policy is the stochastic dual dynamic programming (SDDP), which employs a forward step, for generating trial state variables, and a backward step to construct Benders-like cuts. To assess the quality of the obtained policy (the cuts obtained over the iterations), a confidence interval is computed on the optimality gap. As the SDDP is a cutting-plane based method, it exhibits slow convergence in large-scale problems. To improve computational efficiency, we explore different regions in which the cuts are usually constructed by the classical algorithm. For that, the cuts in the forward step are translated using ideas related to the definition of Chebyshev centers of certain polyhedrons. Essentially, the cuts are lifted by a parameter that vanishes along with iterations without harming the convergence analysis. The proposed technique is assessed on an instance of the Brazilian LTHS problem with individualized monthly decisions per plant, indicating a higher policy quality in comparison with the classical approach, since it computes lower optimality gaps throughout the iterative process.

在水电为主的系统中，长期水热调度问题（LTHS）被表述为多阶段随机规划模型。获得操作策略的经典优化技术是随机双重动态规划（SDDP），它采用向前的步骤来生成试验状态变量，而向后的步骤来构造类似Benders的切口。为了评估所获得策略的质量（在迭代中获得的割据）的质量，会在最佳差距上计算一个置信区间。由于SDDP是基于切面的方法，因此在大规模问题中显示出缓慢的收敛性。为了提高计算效率，我们探索了通常通过经典算法构造切割的不同区域。为了那个原因，使用与某些多面体的切比雪夫中心的定义有关的思想来翻译前进的步骤。本质上，削减是通过一个随迭代消失的参数来提升的，而不会损害收敛分析。该技术是针对巴西LTHS问题进行评估的，每个工厂每个月都有个性化的月度决策，这表明与传统方法相比，该技术具有更高的政策质量，因为它在整个迭代过程中计算出的最优差距较小。