通过结合社区检测的分解增强基于Kron的最优网络约简（Opti-KRON）的可扩展性
Enhancing Scalability of Optimal Kron-based Reduction of Networks (Opti-KRON) via Decomposition with Community Detection
AI总结：
针对大型电力网络约简的计算瓶颈，本文提出结合社区检测分解、并行MILP与新割平面约束的DC Opti-KRON方法，可削减80%至95%节点并优于现有方法。
摘要：
电力网络包含数千个相互连接的节点和边，这给某些电力系统研究带来了计算挑战。为应对这些挑战，我们认为网络约简可以作为实现电力系统可扩展计算的框架。在先前交流（AC）“Opti-KRON”公式的基础上，本文提出一种直流（DC）潮流公式，用于寻找在大型输电分析背景下最优的网络约简。此前，Opti-KRON将基于Kron的最优网络约简表述为混合整数线性规划（MILP），其中二元变量的数量随节点数量增长。为提高Opti-KRON方法的可扩展性，我们在MILP公式中加入社区检测（CD）技术，将大型网络分割成更小、互不相交但连续的子图（即社区）。随后，对于每个子图，我们（并行地）应用基于MILP的方法以及一个新的割平面约束，从而增强可扩展性。最终，新的基于DC的Opti-KRON方法能够实现网络80%至95%的约简（按节点计），同时在统计上优于其他基于CD和基于Kron的方法。我们给出了IEEE RTS-96和2383节点波兰网络的仿真结果。
英文摘要：
Electrical networks contain thousands of interconnected nodes and edges, which leads to computational challenges in some power system studies. To address these challenges, we contend that network reductions can serve as a framework to enable scalable computing in power systems. By building upon a prior AC "Opti-KRON" formulation, this paper presents a DC power flow formulation for finding network reductions that are optimal within the context of large transmission analysis. Opti-KRON previously formulated optimal Kron-based network reductions as a mixed integer linear program (MILP), where the number of binary variables scaled with the number of nodes. To improve the scalability of the Opti-KRON approach, we augment the MILP formulation with a community detection (CD) technique that segments a large network into smaller, disjoint, but contiguous sub-graphs (i.e., communities). For each sub-graph, we then (in parallel) apply MILP-based along with a new cutting plane constraint, thus, enhancing scalability. Ultimately, the new DC-based Opti-KRON method can achieve a 80-95\% reduction of networks (in terms of nodes) while statistically outperforming other CD- and Kron-based methods. We present simulation results for the IEEE RTS-96 and the 2383-bus Polish networks.
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