## 1 Introduction
In these studies, robust model predictive control (RMPC) and other methods are used to deal with the uncertainties of renewable energy. In the microgrid system containing fossil energy, reducing carbon emissions is also an important issue that needs to be solved to improve the environmental protections of the system. More studies focus on the microgrid system containing EVs, namely the EVs charging and discharging states, as well as the stability of the EVs themselves.However, it is also worth studying how to interact with the microgrid while ensuring its stable operation. Meanwhile, some studies are aimed at improved algorithms, which have been greatly modified in optimization rate, convergence, and escape from local deadlock; however, there is still room for further improvement. Therefore, the problems that are concentrated on in this paper are represented as follow: \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number** & **EV** & **Robustness** & **Algorithm** & **Pollution** & **Market** & **Remark** \\ \hline
[6] & & \(\surd\) & & \(\surd\) & & ANN-based scheduling control approaches \\
[7] & & \(\surd\) & & & \(\surd\) & Proposes a robust model predictive control approach \\
[9] & \(\surd\) & \(\surd\) & & \(\surd\) & Addresses the uncertainty of PV output and EV charging \\
[10] & & & & \(\surd\) & Solves the sub-problems with fitted Q-iteration \\
[11] & & & \(\surd\) & \(\surd\) & \(\surd\) & Uses improved algorithm to mine magnesium energy \\
[12] & & & & & \(\surd\) & Introduces a non-cooperative framework \\
[17] & & & \(\surd\) & & & Improvements and comparisons of algorithms \\
[18] & \(\surd\) & & \(\surd\) & & \(\surd\) & Uses ASAPSO algorithm in multi-objective optimization \\
[19] & & & \(\surd\) & & \(\surd\) & Plans two-stage form of multi-energy \\ This & & & & & \(\surd\) & supply optimization \\ paper & \(\surd\) & & \(\surd\) & & \(\surd\) & Improves MA algorithm and designs a scheduling model \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of relevant studies. (1) The EV is taken as one of the dispatching objects, and its interaction state with the microgrid is judged according to its SOC. The microgrid system is able to make full use of power in this measure. (2) The QMA algorithm raised in this paper is compared with the moth-flame optimization (MFO), grey wolf optimize (GWO), particle swarm optimization (PSO), whale optimization algorithm (WOA), sine cosine algorithm (SCA), and MA to verify its advantages, and is applied to the solution of the objective function. The rest of the paper is organized as follows: The structure model of the microgrid is established in Section 2. Meanwhile, the scheduling strategy of each unit, as well as the objective functions with constraint conditions of economic dispatching regarding the microgrid, are established. In Section 3, the operation rules of the QMA are analyzed, and the comparison between various intelligent algorithms is realized. The algorithms are combined with the objective function in Section 4, and the economic scheduling of the microgrid in a specific area is carried out according to the actual situation. Section 5 summarizes the content and contributions of this paper, and puts forward prospects for future studies.