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arXiv 2103.09853eess.SYcs.SY
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基于先验知识的快速AC稳态电网仿真与优化

Fast AC Steady-State Power Grid Simulation and Optimization Using Prior Knowledge

Aayushya Agarwal, Amritanshu Pandey, Larry Pileggi

摘要:

快速准确的优化和仿真正广泛成为大规模输电弹性和规划研究的必要条件,如N-1 SCOPF,批量应急求解器和随机潮流。然而,当前的商业工具通过依赖于从不保证位于有效解的凸区域内的类似网络配置的稳态解中获取的初始条件来优先考虑收敛速度而不是精度。在本文中,我们介绍了一种全局收敛的算法,以方便快速,准确的交流稳态仿真和优化的基础上,从类似的网络的先验知识。该方法使用同伦方法,逐步有效地将先前已知的网络配置转换为当前的网络配置。所提出的配方是高度可扩展的,其功效被证明为弹性研究和优化的大型网络高达70 k总线。

英文摘要:

Fast and accurate optimization and simulation is widely becoming a necessity for large scale transmission resiliency and planning studies such as N-1 SCOPF, batch contingency solvers, and stochastic power flow. Current commercial tools, however, prioritize speed of convergence over accuracy by relying on initial conditions that are taken from the steady state solution of similar network configurations that are not guaranteed to lie within a convex region of a valid solution. In this paper we introduce a globally convergent algorithm to facilitate fast and accurate AC steady state simulation and optimization based on prior knowledge from similar networks. The approach uses a homotopy method that gradually and efficiently translates a previously known network configuration to the current network configuration. The proposed formulation is highly scalable, and its efficacy is demonstrated for resiliency study and optimization of large networks up to 70k buses.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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