The detection of multiple change-points without prior knowledge of the number and location of the change-points is investigated in this paper. By making full use of the geometric information of local statistics, a new change-point detection algorithm called the shape-based cutting and clustering (SCC) algorithm is established. There are three key techniques in the proposed SCC procedure: data-driven threshold, adaptive bandwidth and single peak recognition. Our simulation results show that the proposed method is highly competitive in terms of computational speed and effectiveness. In order to validate the feasibility of the proposed algorithm, we apply the methodology to an operational problem in renewable integrated electrical distribution networks. The results of the real data analysis illustrate the effectiveness of the proposed algorithm.