论文标题
部分可观测时空混沌系统的无模型预测
Determination of Optimal Size and Number of Movable Energy Resources for Distribution System Resilience Enhancement
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
This paper proposes an approach based on graph theory and combinatorial enumeration for sizing of movable energy resources (MERs) to improve the resilience of the electric power supply. The proposed approach determines the size and number of MERs to be deployed in a distribution system to ensure the quickest possible recovery of the distribution system following an extreme event. The proposed approach starts by generating multiple line outage scenarios based on fragility curves of distribution lines. The generated scenarios are reduced using the k-means method. The distribution network is modeled as a graph where distribution network reconfiguration is performed for each reduced line outage scenario. The combinatorial enumeration technique is used to compute all combinations of total MER by size and number. The expected load curtailment (ELC) corresponding to each locational combination of MERs is determined. The minimum ELCs of all combinations of total MER are used to construct a minimum ELC matrix, which is later utilized to determine optimal size and number of MERs. The proposed approach is validated through a case study performed on a 33-node distribution test system.
