论文标题
基准大规模ACOPF解决方案和最佳界限
Benchmarking Large-Scale ACOPF Solutions and Optimality Bounds
论文作者
论文摘要
我们介绍了旨在评估和比较最先进的开源工具来解决交替流动的最佳功率流（ACOPF）问题的全面基准测试工作的结果。我们的数值实验包括在公共图书馆PGLIB中发现的所有实例，其网络大小高达30,000个节点。基准的工具涵盖了许多编程语言（Python，Julia，Matlab/Octave和C $ ++ $），非线性优化求解器（IPOPT，MIPS和INLP）以及不同的数学建模工具（跳跃和重力）。我们还介绍了使用稀疏性探索半决赛编程方法和相应的计算时间获得的最新最佳界限。
We present the results of a comprehensive benchmarking effort aimed at evaluating and comparing state-of-the-art open-source tools for solving the Alternating-Current Optimal Power Flow (ACOPF) problem. Our numerical experiments include all instances found in the public library PGLIB with network sizes up to 30,000 nodes. The benchmarked tools span a number of programming languages (Python, Julia, Matlab/Octave, and C$++$), nonlinear optimization solvers (Ipopt, MIPS, and INLP) as well as different mathematical modeling tools (JuMP and Gravity). We also present state-of-the-art optimality bounds obtained using sparsity-exploiting semidefinite programming approaches and corresponding computational times.
