论文标题
使用未知网格拓扑的强大在线电压控制
Robust Online Voltage Control with an Unknown Grid Topology
论文作者
论文摘要
电压控制通常需要有关网格拓扑的准确信息，以确保网络稳定性。但是，对于现有方法来说，准确的拓扑识别是一个具有挑战性的问题，尤其是由于电网由于采用了可再生能源而受到越来越频繁的重新配置。此外，使用不正确的网络信息运行现有的控制机制可能会导致不稳定的控制。在这项工作中，我们将嵌套的凸孔追逐算法与强大的预测控制器相结合，以在最初未知网络拓扑的在线环境中实现可证明有限的时间收敛到安全电压限制。具体而言，在线控制器不知道真实的网络拓扑和线路参数，而必须通过缩小与其观察值并相应调整反应性发电的一系列网络拓扑和线参数来随着时间的推移学习它们，以将电压保持在所需的安全限制内。我们使用案例研究证明了该方法的有效性，该案例研究表明，在实际情况下，控制器确实能够迅速缩小一组一致的拓扑结构，以做出确保稳定性的控制决策。
Voltage control generally requires accurate information about the grid's topology in order to guarantee network stability. However, accurate topology identification is a challenging problem for existing methods, especially as the grid is subject to increasingly frequent reconfiguration due to the adoption of renewable energy. Further, running existing control mechanisms with incorrect network information may lead to unstable control. In this work, we combine a nested convex body chasing algorithm with a robust predictive controller to achieve provably finite-time convergence to safe voltage limits in the online setting where the network topology is initially unknown. Specifically, the online controller does not know the true network topology and line parameters, but instead must learn them over time by narrowing down the set of network topologies and line parameters that are consistent with its observations and adjusting reactive power generation accordingly to keep voltages within desired safety limits. We demonstrate the effectiveness of the approach using a case study, which shows that in practical settings the controller is indeed able to narrow the set of consistent topologies quickly enough to make control decisions that ensure stability.
