# Optimal Scheduling of Microgrids Based on an Improved Dung Beetle Optimization Algorithm
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & **Renewable** & **Fossil** & **Energy** & **DR and** & & \\  & **Energy** & **Energy** & **Storage** & **Uncertainty** & & \\ \hline
[13] & PV, WT & DE & Yes & No & Operating & DEMPC \\
[14] & PV, WT & MT, DE & Yes & No & Operating & IAO \\
[15] & No & MT, DE & Yes & No & Operating & ATC \\
[16] & PV, WT & No & Yes & No & Net present & PESA-II \\
[17] & PV, WT & DE & Yes & Uncertainty & Net present & AMVOA \\
[18] & WT & No & Yes & Uncertainty & Operating, environmental & C and CG \\
[19] & PV & Thermal & No & No & Operating, environmental & NSGA-II \\
[20] & Hydrogen, PV & No & Yes & No & Operating & DRO \\
[21] & PV, WT & No & Yes & No & Operating, environmental & MOWSO \\
[22] & PV, WT & MT, DE & Yes & No & Operating, environmental & IGWO \\
[23] & PV, WT & DE & Yes & DR & Operating, environmental & SSA \\
[24] & PV, WT & DE & Yes & No & Operating & RO \\
[25] & PV, WT & MT, DE & Yes & No & Operating & LM \\
[26] & PV, WT & MT, DE & Yes & Uncertainty & Operating, environmental & IBPO \\
[27] & PV, WT & MT, DE & Yes & No & Operating & IBOA \\
[28] & PV, WT & No & Yes & DR, Uncertainty & Operating, user satisfaction & CPLEX \\
[29] & PV & No & Yes & Uncertainty & Energy management & Gurobi \\
[30] & PV & No & Yes & No & Energy trading, profits & MIP \\
[31] & No & Thermal & Yes & Uncertainty & Profits & RO \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of MG literature review. The Dung Beetle Optimizer (DBO) is a new swarm bio-inspired algorithm introduced in 2022 [32]. It mimics the behavior of dung beetles, selecting random candidate solutions and simulating the best path for delivering food. The algorithm divides the beetles into four groups, each using different search strategies. By adding complexity to the search process, DBO reduces the impact of environmental changes on finding the best solution. Although DBO is relatively new, it has already been applied in many studies. [PERSON] et al. [33] used DBO for 3D path planning of drones, initializing the population with a chaotic strategy. [PERSON] et al. [34] utilized DBO to optimize the dimensions of ship hulls, achieving certain results by combining the algorithm with a random forest surrogate model. [PERSON] et al. [35] established a financial futures prediction model, integrating deep learning with DBO. It is evident that DBO algorithm exhibits high versatility, showing good performance in path planning, industrial production and finance, etc. However, there is currently a significant gap in applying DBO for optimal scheduling in MG. In this study, we aim to minimize total costs, including operational costs and environmental costs, by modeling an MG system that includes PV, WT, DE, MT, BESS, and grid interactions. To flexibly adjust the load and improve system efficiency, DR, as well as the uncertainty of wind and solar power, is factored into MG. To enhance the DBO algorithm's robustness in addressing initial population randomness and optimize its global search performance, three innovative strategies are introduced: Tent chaotic mapping initialization, non-dominated sorting, and elite reverse learning. The main contributions and novelties of this study are summarized as follows:  
* A more comprehensive microgrid scheduling model. In response to the shortcomings of existing research in terms of system efficiency, power supply reliability, and economic optimization, an integrated optimal scheduling model is constructed, considering DR and the uncertainties of wind and solar power. Under the time-of-use pricing mechanism, DR is divided into two categories for modeling: reducible load and transferable load. Additionally, the Monte Carlo scenario sampling combined with the K-means clustering algorithm is proposed to simulate the uncertainties of wind and solar power output. This approach effectively addresses the issue of insufficient consideration of uncertainty factors in traditional optimization models.