## 1 Introduction  
With the global emphasis on sustainable development and environmental protection, the construction of new power systems dominated by new energy, and the large-scale development of energy sources such as wind and solar power, are receiving increasing attention. However, due to the randomness of power generation and output fluctuations, they face the problem of consumption in actual power grids. The wind-solar-hydrogen storage system, as an effective solution, has its optimized management crucial for ensuring stability and economy of energy supply. Microgrids can overcome the voltage and frequency instability caused by the volatility and randomness of renewable energy outputs, such as photovoltaics and wind power, through the coordinated control of energy storage output. However, as the scale of microgrids has increased significantly, efficiently utilizing renewable energy output has become critically important. Microgrids can integrate multiple distributed energy resources (DERs), such as microturbines (MTs), wind turbines (WTs), batteries (BATs), fuel cells (FCs), photovoltaics(PVs), and other energy sources [1; 2]. When constructing microgrid scheduling models, many variables are considered, so intelligent algorithms are mostly used for solving. [PERSON] et al. [3] proposed a microgrid grid-connected optimization scheduling model based on mutation particle swarm algorithm, and simulated it to verify the feasibility and superiority of the optimized PSO algorithm applied to the microgrid optimization scheduling model in different time periods. [PERSON] et al. [4] improved the Empire Competition Algorithm using Gaussian and Cauchy mutations, and solved the microgrid optimization scheduling model using the Gaussian Cauchy Empire Competition Algorithm. The correctness of the model and the superiority of the solution method were verified. [PERSON] et al. [5] proposed a multi-strategy collaborative for bottle sea squirts (MSC-BSS), which considers both operation and environmental pollution simultaneously. With comprehensive cost as the objective function, constraints such as power balance, ramp rate, and extreme power of interconnection lines are set. Then, MSC-BSS is utilized to address the microgrid scheduling model. By comparing simulation results, the superiority of MSC-BSS over other algorithms and the rationality of optimizing microgrid systems are verified. Although intelligent algorithms have good solving accuracy, they are prone to getting stuck in local optima during the solving process, which can lead to the inability to fully utilize renewable energy. The broad integration of new energy sources into the grid has raised the level of uncertainty in power dispatch, making the optimization of microgrid scheduling an important research focus. [PERSON] et al. [6] proposed a day ahead real-time collaborative scheduling method for wind power storage large-scale bases, which determines the start-stop plan and adjustable output range of thermal power units based on rough wind power prediction. In the real-time stage, scheduling strategies are generated according to quantile rules based on the current wind and solar output. [PERSON] et al. [7] introduced an optimization strategy for the engagement of wind, thermal, and energy storage systems in frequency regulation utilizing robust model predictive control. Response models for wind, fire, and energy storage were established separately, and a double-layer robust optimization model was constructed to minimize the frequency regulation cost of the system under the maximum uncertainty of wind turbine output. [PERSON] et al. [8] proposed a wind-fire storage collaborative frequency regulation control strategy based on multi-scale decomposition, which can effectively achieve the joint participation of wind-fire storage in frequency regulation. Thermal power units generate certain carbon emissions and have significant start-up, shutdown, and operating costs.