4.4 Article

Risk assessment-based long-term transmission system hardening under prior probabilistic information

期刊

IET GENERATION TRANSMISSION & DISTRIBUTION
卷 13, 期 1, 页码 108-115

出版社

INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-gtd.2018.6278

关键词

power system security; risk analysis; optimisation; power transmission planning; probability; integer programming; risk management; power system reliability; power generation reliability; prior probabilistic information; tri-level optimisation model; two-stage robust optimisation model; nonlinear terms; standard IEEE RTS-96 system; risk assessment-based long-term transmission system hardening; transmission infrastructure; load loss risk; power systems

资金

  1. National Natural Science Foundation of China [51607137]
  2. Science and Technology Project of SGCC [5202011600UG]
  3. China Postdoctoral Science Foundation [2017T100748]
  4. State Key Laboratory of Electrical Insulation and Power Equipment in Xi'an Jiaotong University [EIPE17205]

向作者/读者索取更多资源

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.

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