# Optimal Scheduling of Microgrids Based on an Improved Dung Beetle Optimization Algorithm
## 1 Introduction
The running speed is slow, which has a negative impact on the overall optimization efficiency. [PERSON] et al. [18] introduced a model that optimizes costs, emissions, and power demand using a column and constraint generation algorithm (C and CG). But this model only involved WT and BESS. [PERSON] et al. [19] created a hybrid MG model with solar thermal and photovoltaic systems, using Non-dominated Sorting Genetic Algorithm II (NSGA-II), but this model applies only to specific cases like scenarios involving thermal energy. [PERSON] et al. [20] focused on a very innovative scenario involving RE and hydrogen, solving their model with distributionally robust optimization (DRO). [PERSON] et al. [21] used Multi-Objective War Strategy Optimization (MOWSO) for MG optimal scheduling, focusing on operational and environmental costs. A very new algorithm was used, which has a strong parallelism ability and a simple process. But there is a lack of discussion on fossil fuels. Other studies such as that by [PERSON] et al. [22], who used an Improved Grey Wolf Optimization (I-GWO) for an MG with PV, WT, MT, DE, and BESS. Although I-GWO worked well for generating the initial population, it was prone to becoming stuck in local optima. [PERSON] et al. [23] applied the Multi-Objective SparrowSearch Algorithm (MOSSA) to optimize operational and environmental costs, considering the DR of MG. MOSSA uses fused sinusoidal search strategy to improve searching ability. Although the performance has been improved, trigonometry will make the calculation process more complex. [PERSON] et al. [PERSON] et al. (2018) used data-driven robust optimization (RO) to develop a comprehensive system, but they did not account for environmental costs. [PERSON] et al. [PERSON] et al. (2018) used Lagrange Multipliers (LMs), though their comparison lacked sufficient experiments in different scenarios. [PERSON] et al. [PERSON] et al. (2018) proposed a model using the Improved Bacterial Foraging Optimization Algorithm (I-BFO) but faced challenges with local optima, considering the uncertainty of wind and solar power. Finally, [PERSON] et al. [PERSON] et al. (2018) applied the Improved Butterfly Optimization Algorithm (I-BOA). The I-BOA showed faster convergence speed, though improvements could be made in generating the initial population. [PERSON] et al. [PERSON] et al. (2018) introduced a probability distribution and employed Monte Carlo sampling to generate a large number of scenarios. The number of scenarios was then reduced through scenario reduction techniques, comprehensively modeling the uncertainty of RE. [PERSON] et al. (2018) accomplished energy management in MG by progressively establishing deterministic optimization models and robust optimization models, addressing the uncertainties in RE units and user consumption. They innovatively proposed the concepts of \"good deviation\" and \"bad deviation\" to enhance the flexibility of the model. [PERSON] et al. [PERSON] et al. (2018) managed the energy scheduling of MG by establishing a clear two-stage optimization model. Utilizing a rolling optimization approach, they updated the optimization decisions at each time step based on the latest forecast information, thereby effectively addressing the uncertainties in PV generation and load. By applying Stackelberg game theory, they simulated the interactive relationship between the Energy Management Operator (EMO) and the MG, ensuring that both parties could identify optimal strategies within the game. [PERSON] et al. [PERSON] et al. (2018) effectively addressed the uncertainties in the bidding strategies of thermal generators and battery storage by establishing two robust models. They thoroughly investigated the impact of robustness parameters on the models and rigorously introduced mathematical formulations. A summary of the MG literature review is presented in Table 1.