## 1 Introduction  
With the rapid development of the national economy, demand for fossil fuels is increasing. However, the output of traditional energy is limited, and the utilization of renewable energy has become the general trend. As new energy sources, wind and light are widely distributed and can be permanently used. They are characterized by low cost and strong environmental protection. Nevertheless, there are considerable random fluctuations of output power due to natural conditions, which affect the security and stability of the power system after grid connection [1; 2; 3]. Therefore, designing a stable and low-cost scheduling strategy based on the structure of the wind-photovoltaic-storage electric vehicle complementary power generation system has become a key issue. By coordinating wind-photovoltaic and flexible devices, we can ensure the reliability of the system, improve the economy, and increase environmental friendliness [4; 5]. In the process of modeling, parameter uncertainty becomes a significant problem that threatens system security with the increasing scale of the microgrid. In order to solve this problem, robustness becomes one of the parameters worthy of study [6; 7]. The improvement of robustness can make the system maintain its balance when it is disturbed, thereby reducing the loss caused by disturbances [8]. Meanwhile, some other power consumption units are introduced, such as electric vehicles (EVs) and fuel cells, on the basis of wind-photovoltaic-storage microgrid architecture, which can ensure the system maintainsa stable operation by interacting power with other components when it is impacted [9; 10]. In terms of the objective function, some microgrid systems, including fossil fuels, need to be considered regarding carbon emissions to reduce the environmental pollution caused by them [11]. Therefore, multi-objective functions combined with economic objectives and carbon emissions are established in recent studies. Furthermore, in order to ensure the normal operation of the microgrid system, it is necessary to ensure power conservation is met, and power flow constraints, including power flow calculation, should be added to the scheduling study [12; 13]. Additionally, equipment parameters are important constraints to enable the system to operate under the superior performance of the equipment, thereby ensuring equipment safety and power quality. In order to realize the scheduling strategy and accurately find the optimal value of the objective function, a variety of high-performance intelligent algorithm schemes based on biological habits are proposed, and verified in specific cases [14; 15; 16]. Bionics algorithms, such as the flying sparrow search algorithm, flower pollination algorithm, and other heuristic algorithms, are proposed and applied to practical problems [17; 18; 19]. Among them, as a new bionics algorithm, the mayfly algorithm (MA) has better optimization performance than the traditional intelligent algorithms. The integration of the advantages of various algorithms has significantly improved the optimization process in terms of speed and accuracy [20]. However, when it comes to high-dimensional complex problems, it is still difficult to jump out of local optimal regions simply by relying on its own mechanism, and the convergence accuracy of the algorithm is not high enough because the step size of the mayfly is too short when it moves. Based on the above shortcomings, logistic mapping is used to improve the optimization ability of mayfly individuals [21; 22], or the learning factor is changed to improve the degree of self-adaptation [23]. In order to describe the research status of microgrid scheduling and its characteristics more clearly, the studies conducted for our paper, and other related studies, are summarized in Table 1, which is represented as follows:  
Through the above table, it can be seen that the robustness and stability of the system are studied by some scholars.