Open AccessRisk assessment‐based long‐term transmission system hardening under prior probabilistic information
Author(s) -
Ding Tao,
Li Cheng,
Chen Chen,
Chen Bo,
Yang Yongheng
Publication year - 2019
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2018.6278
Subject(s) - probabilistic logic , computer science , piecewise linear function , electric power system , logarithm , mathematical optimization , transmission system , term (time) , hardening (computing) , power transmission , piecewise , transmission (telecommunications) , reliability engineering , mathematics , engineering , power (physics) , artificial intelligence , telecommunications , physics , layer (electronics) , quantum mechanics , mathematical analysis , chemistry , geometry , organic chemistry
In the long‐term transmission system hardening, critical components in transmission infrastructure should be identified and hardened to reduce the load loss risk in the presence of scheduled and unscheduled outages. Generally, the probabilistic information of assets in power systems can be evaluated via historical statistics. On the basis of the prior probabilistic information, this study proposed a tri‐level optimisation model to deal with the problem of long‐term transmission system hardening, in which the risk assessment is taken into account in the objective function. Furthermore, the problem is formulated as a two‐stage robust optimisation model. To address the non‐linear problem in the inner model, logarithmic transformation and piecewise linearisation are utilised to exactly linearise the non‐linear terms in the model. Finally, the standard column‐and‐constraints generation algorithm is employed to solve the proposed model with a master–sub‐problem framework. The test results on a standard IEEE RTS‐96 system show the effectiveness of the proposed model.