Fast Inverter Control by Learning the OPF Mapping Using Sensitivity-Informed Gaussian Processes
Fast Inverter Control by Learning the OPF Mapping Using Sensitivity-Informed Gaussian Processes
复制标题
基于灵敏度高斯过程学习最优潮流映射的快速逆变器控制
DOI：
10.1109/tsg.2022.3210837
复制
发表时间：
2022-02
影响因子：
9.6
通讯作者：
M. Jalali;Manish K. Singh;V. Kekatos;G. Giannakis;Chen-Ching Liu
中科院分区：
文献类型：
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作者：
M. Jalali;Manish K. Singh;V. Kekatos;G. Giannakis;Chen-Ching Liu
快速逆变器控制是实现可再生能源更平稳整合的理想选择。调整分布式能源的逆变器注入设定值是一种有效的电网控制机制。然而，找到这样的最佳设定值需要解决最优潮流（OPF），这可能是计算上的实时负担。以前的工作已经提出使用高斯过程（GPs）学习从网格条件到OPF最小化的映射。该GP-OPF模型在新的电网条件下预测逆变器设定值。训练使用封闭形式的表达式，GP-OPF预测带有置信区间。为了提高数据效率，我们独特地将OPF映射的灵敏度（偏导数）纳入GP-OPF中。这加快了生成训练数据集的过程，因为需要解决的OPF实例更少，以达到相同的精度。为了进一步降低计算效率，我们利用随机特征的概念近似GP-OPF的核函数，并将其巧妙地扩展到灵敏度数据。我们对OPF的二阶锥规划（SOCP）松弛进行了灵敏度分析，其灵敏度可以通过求解一个线性方程组来计算。使用IEEE 13和123总线馈线的实际数据进行的大量数值测试证实了GP-OPF的优点。
Fast inverter control is a desideratum towards the smoother integration of renewables. Adjusting inverter injection setpoints for distributed energy resources can be an effective grid control mechanism. However, finding such setpoints optimally requires solving an optimal power flow (OPF), which can be computationally taxing in real time. Previous works have proposed learning the mapping from grid conditions to OPF minimizers using Gaussian processes (GPs). This GP-OPF model predicts inverter setpoints when presented with a new instance of grid conditions. Training enjoys closed-form expressions, and GP-OPF predictions come with confidence intervals. To improve upon data efficiency, we uniquely incorporate the sensitivities (partial derivatives) of the OPF mapping into GP-OPF. This expedites the process of generating a training dataset as fewer OPF instances need to be solved to attain the same accuracy. To further reduce computational efficiency, we approximate the kernel function of GP-OPF leveraging the concept of random features, which is neatly extended to sensitivity data. We perform sensitivity analysis for the second-order cone program (SOCP) relaxation of the OPF, whose sensitivities can be computed by merely solving a system of linear equations. Extensive numerical tests using real-world data on the IEEE 13- and 123-bus feeders corroborate the merits of GP-OPF.