实时DC最优传输切换的K-最近邻启发式算法
A K-Nearest Neighbor Heuristic for Real-Time DC Optimal Transmission Switching
摘要：
虽然已知传输切换可以降低发电成本，但是即使是DC最优传输切换（DCOTS）的求解也很困难，这已经阻止了最优传输切换在实时电力系统操作中变得司空见惯。在本文中，我们提出了一个k-最近邻（KNN）启发式DCOTS依赖于洞察力，在固定网络上的日常操作，类似的负载配置文件和发电成本配置文件的DCOTS解决方案可能会关闭类似的线路。我们采用数据驱动的方法，并假设我们有许多历史实例的DCOTS解决方案，这是现实的，因为实际上问题每5分钟解决一次。给定一个新实例，我们从过去的实例中找到一组“接近”的实例，并返回新实例的最佳解决方案。我们提出了一个案例研究的7个测试网络的大小从118到3,375总线。我们比较建议的启发式DCOTS算法从文献中，商业求解器算法，和一个简单的贪婪的局部搜索算法。在大多数情况下，我们在更少的计算时间内找到更好的质量解决方案。此外，计算时间在实时操作所施加的限制内，即使在较大的网络上。最后，我们对我们的训练数据进行了实证研究，以了解为什么启发式效果很好。
英文摘要：
While transmission switching is known to reduce power generation costs, the difficulty of solving even DC optimal transmission switching (DCOTS) has prevented optimal transmission switching from becoming commonplace in real-time power systems operation. In this paper, we present a k-nearest neighbors (KNN) heuristic for DCOTS which relies on the insight that, for routine operations on a fixed network, the DCOTS solutions for similar load profiles and generation cost profiles will likely turn off similar sets of lines. We take a data-driven approach and assume that we have DCOTS solutions for many historical instances, which is realistic given that the problem is solved every 5 minutes in practice. Given a new instance, we find a set of "close" instances from the past and return the best of their solutions for the new instance. We present a case study on 7 test networks ranging in size from 118 to 3,375 buses. We compare the proposed heuristic to DCOTS heuristics from the literature, commercial solver heuristics, and a simple greedy local search algorithm. In most cases, we find better quality solutions in less computational time. In addition, the computational time is within the limits imposed by real-time operations, even on larger networks. Last, we present an empirical study of our training data to understand why the heuristic works well.
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