分布式学习在同步发电和变流器发电优化分配中的应用
Distributed learning for optimal allocation of synchronous and converter-based generation
摘要：
基于转换器的发电到电网的渗透的动机，我们重新审视经典的对数线性学习算法的最优分配{同步机和转换器}的混合发电。我们的目标是分配给每个发电机单元的类型（无论是同步机或DC/AC转换器在闭环下垂控制），同时最大限度地减少稳态角偏差相对于由未知的同步和DC/AC转换器为基础的发电的最佳配置引起的最佳。此外，我们研究的鲁棒性的学习算法对一个统一的下降线磁阻和相对于一个定义良好的可行性区域描述容许的功率偏差。我们证明了保证概率收敛到最大化的扰动势函数与可行的潮流，并证明了我们的理论研究结果，通过电力网络的仿真算例与6个发电机组。
英文摘要：
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.
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