论文标题
配备了太阳能电池板和电池的家用微电网的优化：模型预测控制和随机双动态编程方法
Optimization of a domestic microgrid equipped with solar panel and battery: Model Predictive Control and Stochastic Dual Dynamic Programming approaches
论文作者
论文摘要
在这项研究中，考虑了带有存储（电池，热水箱）和太阳能电池板的微电网。我们基准了两种算法MPC和SDDP，它们产生了在线政策以管理微电网，并将它们与基于规则的策略进行比较。模型预测控制（MPC）是一种众所周知的算法，它以确定性的预测对未来的不确定性进行建模。相比之下，随机双动态编程（SDDP）将未来的不确定性模拟为具有已知概率分布的阶段式独立随机变量。我们提出了一个基于样本验证的方案，以公平地比较MPC和SDDP产生的两个在线政策。我们的数值研究表明，与基于规则的策略相比，MPC和SDDP取得了显着收益，而SDDP的表现不仅平均而且在大多数样本外评估方案中都平均而且在大多数样本外评估方案中。
In this study, a microgrid with storage (battery, hot water tank) and solar panel is considered. We benchmark two algorithms, MPC and SDDP, that yield online policies to manage the microgrid, and compare them with a rule based policy. Model Predictive Control (MPC) is a well-known algorithm which models the future uncertainties with a deterministic forecast. By contrast, Stochastic Dual Dynamic Programming (SDDP) models the future uncertainties as stagewise independent random variables with known probability distributions. We present a scheme, based on out-of-sample validation, to fairly compare the two online policies yielded by MPC and SDDP. Our numerical studies put to light that MPC and SDDP achieve significant gains compared to the rule based policy, and that SDDP overperforms MPC not only on average but on most of the out-of-sample assessment scenarios.
