Scenario generation and reduction methods for power flow examination of transmission expansion planning

Nowadays with enlargement of power grid and increasing renewable energy integration, uncertainties and randomness challenge traditional power system analysis methods. Scenarios are widely applied to deal with power system uncertainties. Especially power flow examination in transmission expansion planning needs typical scenarios to verify normal power flow, and provide useful information for auxiliary planning strategies by studying typical extreme scenarios. In order to perform large-scale and multidimensional scenario analysis in power flow examination, this paper proposes an improved initial-center-refined and weighted K-means (ICRW K-means) method to improve efficiency and reduce computational time. Taking extreme scenarios into account, an extreme scenario reduction method is presented to retain typical extreme scenarios, maintaining more features of scenario reduction results. A real network is studied using proposed ICRW K-means and extreme scenario reduction method to perform power flow examination in transmission expansion planning. Numerical results demonstrate the effectiveness of proposed method, providing suggestions for auxiliary planning strategies to enhance power grid in resisting extreme conditions and reducing economic losses.