# Configuration Optimization of Mobile Photovoltaic-Diesel-Storage Microgrid System Based on CPS-MOEA
## 6 Conclusions  
The study presented in this paper offers a significant advancement in the field of mobile photovoltaic-diesel-storage microgrid systems by focusing on the system's dependability and maneuverability. The key contributions of this research are twofold and are presented below.  
This paper introduces a two-step optimization method, employing the newly applied CPS-MOEA algorithm. This approach is groundbreaking in the optimization of mobile photovoltaic-diesel-storage microgrid systems, marking a first in the domain of optimal design and planning for such systems. The CPS-MOEA algorithm showcases superior performance in generating a well-distributed Pareto front, indicating improved convergence and distribution compared to traditional multi-objective optimization algorithms like PICEA-g, MOPSO, and GDE3.  
The research utilizes a combination of fuzzy C-means (FCM) clustering and grey relational projection (GRP) techniques during the decision-making phases. This innovative methodology allows for the effective selection of the best compromise solutions (BCSs) from the Pareto optimal solutions (POSs), catering to the diverse preferences of decision-makers. This approach offers a more nuanced and practical pathway for optimizing systems, presenting decision-makers with a variety of viable options that align with different preferences and requirements.  
The simulation results underscore the efficacy of the CPS-MOEA algorithm in handling complex multi-objective problems, demonstrating its ability to achieve better convergence and a more efficient distribution of solutions along the Pareto front. Furthermore, the mobile photovoltaic-diesel-storage microgrid system model considered in the study incorporates critical metrics of mobility and reliability. By leveraging multi-objective evolutionary algorithms, the research successfully derives a set of constrained Pareto optimal solutions. These solutions stand as valuable benchmarks for decision-making in both scientific research and practical applications, ensuring that the designed system fulfills operational and strategic needs effectively.  
In conclusion, this paper contributes significantly to the optimization and decision-making processes in the design and planning of mobile photovoltaic-diesel-storage microgrid systems. It introduces a cutting-edge optimization algorithm and a decision-making methodology that together enhance the system's dependability and maneuverability, providing a robust framework for achieving optimal designs that meet a wide range of requirements and preferences.  
The experimental results have shown that the application of a multi-objective evolutionary algorithm with classification based on preselection is more effective in the optimization of mobile microgrid systems compared to a single MOEA. However, there are still many issues worth exploring regarding CPS-MOEA. Firstly, in the selection of classification methods, what impact do different classification methods (such as reinforcement learning, Naive Bayes classification, support vector machines, random forests, etc.) have on the same MOEA? At the same time, what kinds of effects are produced by the combination of different MOEAs with different classification methods, and which combinations are suitable for application in different kinds of optimization problems? In the future, our research will focus on these issues and apply them in the field of microgrids.  
Methodology, T.Z.; software, T.L.; validation, W.L.; formal analysis, T.L.; resources, T.Z.; writing--original draft preparation, T.L.; writing--review and editing, W.L.; visualization, T.L.; supervision, T.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.