A network approach for power grid robustness against cascading failures

التفاصيل البيبلوغرافية
العنوان: A network approach for power grid robustness against cascading failures
المؤلفون: Wang, Xiangrong, Koç, Yakup, Kooij, Robert E., Van Mieghem, Piet
سنة النشر: 2015
المجموعة: Computer Science
Physics (Other)
مصطلحات موضوعية: Physics - Physics and Society, Computer Science - Social and Information Networks
الوصف: Cascading failures are one of the main reasons for blackouts in electrical power grids. Stable power supply requires a robust design of the power grid topology. Currently, the impact of the grid structure on the grid robustness is mainly assessed by purely topological metrics, that fail to capture the fundamental properties of the electrical power grids such as power flow allocation according to Kirchhoff's laws. This paper deploys the effective graph resistance as a metric to relate the topology of a grid to its robustness against cascading failures. Specifically, the effective graph resistance is deployed as a metric for network expansions (by means of transmission line additions) of an existing power grid. Four strategies based on network properties are investigated to optimize the effective graph resistance, accordingly to improve the robustness, of a given power grid at a low computational complexity. Experimental results suggest the existence of Braess's paradox in power grids: bringing an additional line into the system occasionally results in decrease of the grid robustness. This paper further investigates the impact of the topology on the Braess's paradox, and identifies specific sub-structures whose existence results in Braess's paradox. Careful assessment of the design and expansion choices of grid topologies incorporating the insights provided by this paper optimizes the robustness of a power grid, while avoiding the Braess's paradox in the system.
Comment: 7 pages, 13 figures conference
نوع الوثيقة: Working Paper
URL الوصول: http://arxiv.org/abs/1505.06312
رقم الانضمام: edsarx.1505.06312
قاعدة البيانات: arXiv