## 1 Introduction  
Renewable energy sources such as solar and wind power are characterized by their clean and sustainable nature, making them key tools for reducing carbon emissions and achieving sustainable green development. Traditional energy dispatch methods often rely on fossil fuels, and their strategies typically struggle to balance economic and environmental benefits. These conventional methods are no longer sufficient to meet the requirements for optimizing renewable energy dispatch. Therefore, studying joint optimization dispatch methods for renewable energy under \"dual carbon\" goals not only helps improve energy utilization efficiency and reduce carbon emissions, but also provides strong support for the sustainable development of the energy industry [1, 2]. The uncertainty and volatility of their output cannot be ignored in terms of their impact on microgrid scheduling. Therefore, the impact of new energy on microgrid scheduling is not only reflected in the start stop of thermal power units, but also in the early planning of supporting facilities such as energy storage devices. Therefore, a method is needed to comprehensively quantify the impact of the volatility of new energy outputs on the total cost of microgrid scheduling and operation, identify key new energy sources that affect microgrid planning, and determine the capacity of wind and photovoltaic units to be invested in order to promote the goal of renewable energy integration [3; 4]. [PERSON] [5] proposes an evaluation system for the capacity of the power grid to accept wind power, but does not quantitatively analyze the various influencing factors; [PERSON] [6] proposes a schedulable potential evaluation algorithm that considers the different needs of dispatchers and users, achieving potential assessments on both the dispatch and user sides of electric vehicles; and [PERSON] [7] constructs an evaluation index system for the static and characteristic indicators of users, achieving a comprehensive quantitative analysis of the regulatory potential of typical users. In view of the above issues, this article defines the indicator of \"scheduling potential\" to comprehensively represent the degree of correlation between the volatility of new energy outputs and the total cost of scheduling operations. The greater the correlation between a certain new energy and the total operating cost of scheduling, the greater the impact of fluctuations in the output of new energy on the total operating costs, indicating that the scheduling potential of the new energy is greater. Considering uncertain factors such as the output of new energy in microgrids [8], the existing literature mainly describes the uncertainty of new energy outputs as stochastic optimization [9], robust optimization [10], and distributed robust optimization. At the same time, in order to comprehensively analyze the scheduling potential of new energy, this article needs to construct multiple scenarios to simulate the output of new energy under different levels of fluctuation, which makes the model difficult to solve. To solve the above problems, the literature uses [PERSON]'s decomposition algorithm to solve the model [11]. The literature proposes more efficient column and constraint generation algorithms [12]. The [PERSON]'s decomposition algorithm belongs to the row constraint generation algorithm, which constructs the main problem using a dual form of sub-problems and has a higher computational complexity compared to the column constraint generation algorithm. Therefore, current decomposition methods tend to favour column and constraint generation algorithms. The methods for quantitatively analyzing the load that affects power grid planning in the existing algorithm mainly include grey relational analysis [13], wavelet analysis [14], and variable correlation testing. To obtain more intuitive results for a comprehensive analysis index system, it is usually necessary to perform a comprehensive weighted analysis on the various data generated using the analysis [15; 16; 17].