A study of the allocation of energy consumption permits among the regions of China based on clustering analysis and cuckoo search.

Energy sources are power force of society and economy to develop. In recent years, extensive energy consumption patterns have threatened China's economic development step by step and made China's economic growth encounter unprecedented "bottlenecks." Therefore, this paper introduce a novel energy allocation scheme including cluster analysis and weighted voting allocation model to limit energy consumption in each region of China. The entire quota allocation process is divided into two parts by the proposed allocation scheme. In the first part, 30 regions in China are grouped into four classes through energy conservation pressure, capacity, responsibility, potential, and effectiveness. And, the total energy consumption is quoted to various classes. In the second part, the total energy consumption of each class is allocated to the corresponding regions. The weighted voting model runs through two-tier allocation schemes, and the allocation schemes based on historical energy consumption, GDP, and population are selected by each class and each region based on the voting rights. The voting rights are quantified by multi-index comprehensive evaluation model, which adopts entropy weight method in the first part owing to inexperience and cuckoo search algorithm (CS) in the second part to choose better weights. The combination of entropy weight method and CS can increase the flexibility of reducing energy consumption policy while maintaining impartiality in decision process. According to the proposed allocation scheme, case study of the allocation for energy consumption in China by 2020 is performed. The allocation results indicate that the proposed allocation scheme improves the fairness and effectiveness to a certain extent, which is superior to the allocation scheme based on historical energy consumption, GDP, and population. We also compare with the state-of-the-art algorithm and prove that our algorithm is more fair and effective. In addition, the proposed distribution scheme can stimulate all regions to cut down energy intensity in the case of meeting the energy consumption needs of each region.