Power grid vulnerability evaluation method under high-proportion renewable energy source access

Abstract

The invention discloses a power grid vulnerability evaluation method under high-proportion renewable energy access, and the method comprises the steps: carrying out the clustering of high-proportion renewable energy output data of a target region power grid through employing a fuzzy C-means clustering algorithm through employing a multi-scene technology, and obtaining a multi-scene time sequence distribution model of high-proportion renewable energy output; evaluating element vulnerability by using a DEAHP-based vulnerability index system in each typical scene to obtain a vulnerability time sequence evaluation index changing along with renewable energy output change, so that the fluctuation degree and the real-time operation condition of node vulnerability characteristics are reflected. The idea of weighted entropy is introduced, and the static evaluation model of each time section is linearly weighted and aggregated by using a time sequence weighting vector so as to comprehensively consider the fluctuation characteristics and the equilibrium degree of node vulnerability under all typical time sequence scenes. The combination of typical scene probabilistic and vulnerability evaluation time sequence is realized, and the vulnerability of each node in the network is evaluated statically, dynamically and comprehensively, so that the evaluation result better conforms to the actual operation of the power grid.

Description

technical field [0001] The invention relates to the field of power systems, in particular to a method and equipment for assessing the vulnerability of a power grid under the access of a high proportion of renewable energy. Background technique [0002] Wind power and photovoltaics are currently the most mature renewable energy power generation technologies. However, these two types of power sources are highly volatile and random, and cannot be stored or controlled. They have different characteristics in different time scales and different spatial ranges. volatility characteristics. These unfavorable factors may lead to adverse effects on the level of power quality, adequacy of power transmission, and continuity of power supply after the integration of renewable energy into the grid, greatly increasing the risk of safe and stable operation of the power grid. At the same time, when the power grid accepts a high proportion of renewable energy, due to its intermittent and rando...

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