# Two-Sided Matching Decision Method of Electricity Sales Package Based on Disappointment Theory
## 1 Introduction  
In the context of the implementation of \"dual carbon\" targets, the reform of the power system is being promoted further, all sectors of society are paying more and more attention to the application of renewable energy, and the demand of electric power customers for clean energy is also growing. Not only China, but also the whole world is facing the dual carbon problem. EU member states are currently focusing on the Renewable Energy and Energy Efficiency Program [1]. Only by further increasing the proportion of renewable energy and making it deeply replace traditional fossil fuels can the high-carbon coal, natural gas, and other power systems develop in a low-carbon or even zero-carbon direction [2], eventually achieving a low-carbon economy. A large number of power-selling companies have emerged, and the demand for high-quality electricity from social electric power customers is also constantly increasing [3]. High-quality electricity refers to electricity with higher power quality indicators than those specified in existing public grid power supply regulations and restrictive standards [4]. Electricity sales companies need to attractelectricity users by setting reasonable electricity sales package prices, improving power quality, increasing the proportion of renewable energy, and providing additional value-added services. Electric power customers need to choose appropriate electricity sales packages according to different needs such as power supply reliability and electricity cost [5]. Therefore, the matching between electricity users and electricity sales packages can not only improve the revenue of electricity sales companies and meet the demand of users for high-quality electricity, but also have great significance in promoting the operation of the low-carbon economy. At present, the main method for electric power customers to choose the electricity sales package is where the electricity sales company recommends the electricity sales package, and then the electric power customers make the decision. For power-selling companies, recommending appropriate power-selling package services for users is an effective way to improve the viscosity of electric power customers to power-selling companies and to attract a large number of new users [6]. At present, there are two main methods for recommending electricity sales packages: indirect recommendation and direct recommendation. There are two main categories of existing indirect recommendation methods, namely, statistical analysis recommendation and mathematical modeling recommendation. In statistical analysis, the main methods include regression analysis, cluster analysis, and preference analysis. The authors of [7] studied the impact of income, consumption expenditure, and price on household electricity consumption based on quantile regression analysis, avoiding sampling deviation, and providing a more accurate electricity sales package for families. The authors of [8] used time load and time series to cluster and analyze residential electricity customers, extracting customer behavior or load curves from the time series to recommend more targeted electricity sales packages to users. The authors of [9; 10] used a preference analysis-based approach to develop fair energy allocation policies and unified pricing mechanisms for energy trading in P2P markets. However, most statistical analysis methods rely heavily on reliable and sufficient data. If the data quality is low or the quantity is small, it may reduce the accuracy of the recommendation results. In mathematical modeling, the main methods used are collaborative filtering and hybrid recommendation methods. The authors of [10; 11] measured the relationship between users or products through collaborative filtering, looked for similar neighbor sets, and then completed the recommendation. However, the attribute of the item itself is not considered in the recommendation, which is likely to reduce the accuracy of the recommendation.