## 1 Introduction
The ability to regulate resources during power generation is limited, so it is crucial to consider a coordinated multi-target optimization scheduling strategy for wind, fire, and storage systems. In dealing with the economic dispatch problem of smart microgrids, it is necessary to consider the practical problems in the power dispatch process of smart microgrid systems, so multi-target optimization of the joint system is often required. [PERSON] et al. [9] used the NSGA II algorithm with operating costs and pollutant emissions as objective functions to achieve multi-target optimization operation of a grid-connected microgrid testing system. [PERSON] et al. [10] proposed an adaptive genetic algorithm to optimize the operation of microgrids by considering power loss and voltage distribution as objective functions, and used multi-target optimization methods to solve the upper level optimization model of distribution network scheduling. [PERSON] et al. [11] introduced a multi-target PSO algorithm to solve the multi-target energy management issue in grid connected microgrids, with operating costs and pollutant emissions as objective functions. Simulation was conducted in a microgrid testing system composed of wind turbines, solar cells, batteries, internal combustion engines, and diesel generators. [PERSON] et al. [12] constructed a dual layer optimization configuration model for cloud energy storage driven by economic, low-carbon, and reliable multiple objectives, and implemented a model solution based on a second-generation nondominated genetic algorithm, verifying the effectiveness of the cloud energy storage mode in improving system investment and operation efficiency. However, in current research, the proposed multi-target optimization algorithms are not accurate enough in finding the optimal solution between reducing pollutant emissions and minimizing power generation costs, and their convergence speed is slow. [PERSON] introduced GWO motivated by the predatory actions of grey wolves [13]. Usually, there are four wolf types: Alpha, then Beta, Delta, and lastly Omega [14; 15]. In a wolf pack, the Alpha is the dominant leader and the primary decision-maker, while other pack members must follow [PERSON]'s orders and decisions. The second position is occupied by the Beta wolf, who acts as a mediator between the Alpha wolf and the rest of the wolves. It helps the alpha wolf and becomes the first choice of nominee for [PERSON], in case the Alpha wolf dies or is too old for swarm management. In terms of rank, Omega is the lowest-ranked grey wolf [16; 17], following other top-ranked grey wolves. [PERSON] tends to manage Omega wolves, while also assisting Alpha and Beta wolves. As the number of iterations of the GWO algorithm increases, the diversity of the grey wolf population decreases, leading to a decrease in search ability and a tendency to fall into local optima rather than global optima. Opposition-based learning (OBL) is a powerful optimization tool, and the successful implementation of OBL involves evaluating the populations of opposite and current stations of the same generation in order to obtain better candidate solutions for a given problem [18]. In order to obtain the optimal value of multi-objective optimization scheduling in terms of carbon emission cost and time-dependent electricity tariffs, this paper proposes a multi-objective optimization scheduling design for wind-solar energy storage microgrids based on an improved oppositional gradient grey wolf optimization (OGGWO) algorithm. The main contributions are as follows:  
(1) The OGGWO is introduced for multi-target optimization planning of grid-tied microgrids, enhancing solution quality and convergence speed. This algorithm incorporates instantaneous considerations of estimates and their equivalent oppositional estimates, leveraging the advantages of gradient features to provide output boundaries for thermal power units.