Chinese provincial power consumption convergence features based on CEEMDAN hierarchical clustering algorithms

After China put forward the goal of building a new power system, the proportion of renewable energy power generation is increasing, which makes it more difficult for the power system to respond to power demand. Therefore, this paper analyses the convergence of power consumption in China's provinces. Based on theoretical research combined with the actual power consumption in China, this paper achieves a systematic evaluation of power consumption in each province. This paper combines the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method with the clustering method to form the CEEMDAN hierarchical clustering algorithms. With the application of this proposed approach, we assess the convergence features among the provincial power consumption in China. There are four convergence clubs can be organized from both low-term and short-term consumption components for different provinces. This paper can sufficiently illustrate the commonalities and uniqueness for power consumption of different provinces.