# Configuration Optimization of Mobile Photovoltaic-Diesel-Storage Microgrid System Based on CPS-MOEA
## 1 Introduction
Researchers have also [13] presented and evaluated three energy management systems (EMSs) based on Particle Swarm Optimization (PSO) for long-term operation optimization of a grid-connected hybrid system. In order to reduce the curtailment of renewable energy resources through improving the operational flexibility, an optimal scheduling model based on chance-constrained programming (CCP) was proposed for a small-scale integrated energy system (IES) with CHP units, thermal power units, renewable generations and representative auxiliary equipment [14]. A generic optimal planning model was proposed in the literature [15] to assess the economic and environmental benefits of the capacity allocation of the grid-connected integrated energy system considering both price-based demand response and incentive-based demand response, respectively. The literature [16] has established a compact model of cogeneration systems by taking nonlinear energy conversion and heat transfer constraints of combined heat and power units into account. The configuration optimization problem of microgrids is a complex constrained nonlinear optimization problem. The multi-objective evolutionary algorithm performs well when faced with these problems. [PERSON] first reviewed the related works about multimodal multi-objective problems (MMOPs) in the literature [17], then chose 15 state-of-the-art algorithms that utilize different diversity-maintaining techniques and compared their performance on different types of the existing test suites. Later, [PERSON] proposed an evolutionary algorithm with a hierarchy ranking method (HREA) to find both the global and the local PFs based on the decision-maker's preference [18], then presented a novel multimodal multi-objective coevolutionary algorithm (CoMMEA) to better produce both global and local PSs, and simultaneously to improve the convergence performance in dealing with high-dimension MMOPs [19]. Researchers [20] have proposed a reference vector-guided EA for multi-objective optimization. In the proposed algorithm, a scalarization approach, termed angle-penalized distance, is adopted to balance the convergence and diversity of the solutions in the high-dimensional objective space. Researchers [21] have also proposed a new concept for solving multi-objective optimization problems: PICEAs. In the algorithms, a family of preferences were coevolved with candidate solutions. A decomposition-based EMO algorithm called the multi-objective evolutionary algorithm based on the decomposition LWS (MOEA/D-LWS) was proposed in the literature [22]. In the literature [23], a GD indicator-based evolutionary algorithm is proposed to solve multi-objective optimization problems. Refs. [24; 25] have utilized a pre-existing multi-objective evolutionary algorithm, specifically the Pareto sorting multi-objective evolutionary algorithm with classification-based preselection (CPS-MOEA). This study introduces a scientific configuration approach that employs advanced optimization methods and algorithms to plan and configure a mobile photovoltaic-diesel-storage microgrid system. The main challenge in optimal planning and configuration lies in balancing the system's weight with minimizing power supply deficiency rates. This balance is crucial for preserving maneuverability and reliability while maximizing the system's potential. This process is recognized as a large-scale nonlinear mixed-integer programming problem, along with equality and inequality constraints. Taking into account the system's planning and configuration challenges, this research applies CPS-MOEA to the unique context of the mobile photovoltaic-diesel-storage microgrid system, addressing the specific combination of constraints and objectives. This paper first constructs a hybrid microgrid system incorporating a photovoltaic-storage-diesel generator. The system's weight and power supply deficiency rate are considered as objective functions, which are optimized to achieve a balance between the system weight and sustainable power in the microgrid.