This paper presents an evaluation method for the entropy-weighting of wind power clusters that comprehensively evaluates the allocation problems of wind power clusters by considering the correlation between indicators and the dynamic performance of weight changes. A dynamic layered sorting allocation method is also proposed. The proposed evaluation method considers the power-limiting degree of the last cycle

the adjustment margin

and volatility. It uses the theory of weight variation to update the entropy weight coefficients of each indicator in real time

and then performs a fuzzy evaluation based on the membership function to obtain intuitive comprehensive evaluation results. A case study of a large-scale wind power base in Northwest China was conducted. The proposed evaluation method is compared with fixed-weight entropy and principal component analysis methods. The results show that the three scoring trends are the same

and that the proposed evaluation method is closer to the average level of the latter two

demonstrating higher accuracy. The proposed allocation method can reduce the number of adjustments made to wind farms

which is significant for the allocation and evaluation of wind power clusters.