基于局部轨迹卷积的分散式增益学习用于电压控制
Decentralized Gain Learning for Voltage Control via Local Trajectory Convolution
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- New York University（纽约大学）
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中文总结 AI 辅助
针对分布式能源引起的电压波动，提出一种仅利用局部母线电压偏差轨迹自卷积恢复系统级目标梯度的分散式增益优化方法，无需网络模型或通信，仿真验证其有效性。
中文摘要 AI 辅助
分布式能源带来的快速且空间异质的波动要求电压调节既快速又能响应变化的运行条件。局部线性伏安（Volt/VAR）控制具有无需实时通信即可迅速调节电压的优势，但其性能关键取决于控制增益的选择。然而，为系统级目标优化这些增益通常需要准确的网络模型或集中式通信。在本工作中，我们揭示了一个结构性质，使得仅利用局部测量即可实现这种优化。具体而言，通过利用闭环电压动力学的自伴结构，我们表明系统级目标梯度的每个分量可以从相应母线电压偏差轨迹的自卷积中精确恢复。基于这一结果，我们开发了一种简单的分散式增益优化方法，其中每条母线仅使用其局部数据轨迹更新其控制增益。仿真结果表明，所提出的方法能有效响应变化的运行条件更新伏安控制增益，同时仅需局部信息。
英文摘要
Fast and spatially heterogeneous fluctuations from distributed energy resources call for voltage regulation that is both fast and responsive to changing operating conditions. Local linear Volt/VAR control offers the advantage of promptly regulating voltages without real-time communication, but its performance depends critically on the choice of control gains. Optimizing these gains for a system-level objective, however, typically requires an accurate network model or centralized communication. In this work, we uncover a structural property that enables such optimization using only local measurements. Specifically, by exploiting the self-adjoint structure of the closed-loop voltage dynamics, we show that each component of the system-level objective gradient can be exactly recovered from a self-convolution of the corresponding bus's voltage deviation trajectory. Building on this result, we develop a simple decentralized gain optimization method in which each bus updates its control gain using only its local data trajectory. Simulation results demonstrate that the proposed approach effectively updates the Volt/VAR control gains in response to changing operating conditions while requiring only local information.
