The uncertainties of distribution energy resources (DERs) have significant impacts on active distribution networks (ADNs), and it is necessary to control the DER outputs to hedge the negative impacts of their uncertainties on ADNs. However, DER uncertainties are complicated, containing spatial and temporal correlation, which makes it challenging to design proper control schemes, especially when there exist temporal-correlated units such as energy units (EUs). This paper provides an Itô process model to describe the characteristics of stochastic resources and EUs in a unified way, which makes it easy to evaluate the impacts of stochastic resources on temporal-correlated units. Based the moment form of the Itô process model, a moment optimization (MO) approach is provided to transform the stochastic control (SC) problem into an optimization problem with respect to the first-order and second-order moments of the system variables. The scale of MO is comparable to that of the corresponding deterministic control problem, which means that the computational efficiency of MO is much higher than that of traditional approaches. Case studies also show that the proposed approach outperforms existing approaches in both the performance and computational efficiency, which means that the proposed approach has attractive potential for use in large-scale applications.

中文翻译：

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时空相关不确定性下ADN中DER的最优控制

配电能源（DER）的不确定性对有源配电网（ADN）产生重大影响，因此有必要控制DER的输出以对冲其不确定性对ADN的负面影响。但是，DER的不确定性很复杂，包含空间和时间相关性，这使得设计适当的控制方案具有挑战性，尤其是在存在与时间相关的单元（例如能量单元（EU））时。本文提供了一个Itô流程模型，以统一的方式描述随机资源和EU的特征，从而可以轻松评估随机资源对时间相关单位的影响。根据Itô流程模型的矩形式，提供了矩优化（MO）方法，以将随机控制（SC）问题转换为关于系统变量的一阶和二阶矩的优化问题。MO的规模与相应的确定性控制问题的规模可比，这意味着MO的计算效率远高于传统方法。案例研究还表明，该方法在性能和计算效率上均优于现有方法，这意味着该方法在大规模应用中具有诱人的潜力。这意味着MO的计算效率远远高于传统方法。案例研究还表明，该方法在性能和计算效率上均优于现有方法，这意味着该方法在大规模应用中具有诱人的潜力。这意味着MO的计算效率远远高于传统方法。案例研究还表明，该方法在性能和计算效率上均优于现有方法，这意味着该方法在大规模应用中具有诱人的潜力。