论文标题
分布式学习，以最佳分配基于同步和转换器的生成
Distributed learning for optimal allocation of synchronous and converter-based generation
论文作者
论文摘要
通过将基于转换器的生成渗透到电网中的动机，我们重新访问了经典的对数线性学习算法，以用于最佳分配（同步机和转换器的最佳分配），以用于混合发电。目的是为每个发电机单元分配A类型（闭环中的同步机或DC/AC转换器，并具有下垂控制），同时最大程度地降低了稳态角度偏差，而不是通过同步和基于DC/AC转换器的最佳构型引起的最佳最佳。此外，我们研究了学习算法的鲁棒性，以与线条置换率均匀下降以及描述可接受功率偏差的明确可行性区域的均匀下降。我们显示了可行的功率流的保证概率收敛到最大化电势函数的最大化器，并通过具有六个世代单位的功率网络的模拟示例来证明我们的理论发现。
Motivated by the penetration of converter-based generation into the electrical grid, we revisit the classical log-linear learning algorithm for optimal allocation {of synchronous machines and converters} for mixed power generation. The objective is to assign to each generator unit a type (either synchronous machine or DC/AC converter in closed-loop with droop control), while minimizing the steady state angle deviation relative to an optimum induced by unknown optimal configuration of synchronous and DC/AC converter-based generation. Additionally, we study the robustness of the learning algorithm against a uniform drop in the line susceptances and with respect to a well-defined feasibility region describing admissible power deviations. We show guaranteed probabilistic convergence to maximizers of the perturbed potential function with feasible power flows and demonstrate our theoretical findings via simulative examples of power network with six generation units.
