The optimization of black start decisiommaking plays an important role in the rapid restoration of a power system after a major failure/outage. With the introduction of the concept of smart grids and the development of real-time communication networks, the black-start decision-makers are no longer limited to only one or a few power system experts such as dispatchers, but rather a large group of professional people in practice. The overall behaviors of a large decision-making group of decision-makers/experts are more complicated and unpredictable. However, the existing methods for black-start decision-making cannot handle the situations with a large group of decision-makers. Given this background, a clustering algorithm is presented to optimize the black-start decision-making problem with a large group of decision-makers. Group decision-making preferences are obtained by clustering analysis, and the final black-start decisiommaking results are achieved by combining the weights of black-start indexes and the preferences of the decision-making group. The effectiveness of the proposed method is validated by a practical case. This work extends the black-start decision-making problem to situations with a large group of decision-makers.
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