## 1 Introduction
### Literature Review  
The optimal scheduling problem of a DES is a typical multi-objective optimization problem (MOP) with constraints, characterized by the optimal control of various operating indexes under the constraints of the actual operating boundary of the system. Regarding the solution of the MOP, some experts from various countries have carried out a large number of studies [5; 6; 7]. In [8], a non-dominated sorting genetic algorithm (NSGA) is proposed to achieve the optimization of operating costs, labor efficiency and the penalty cost of pollutant gas emission in an integrated park scenario, obtaining an effective non-dominated solution set. Reference [9], after applying an NSGA to obtain the set of non-dominated solutions, proposes to utilize an approximate ideal solution sorting method to obtain scheduling instructions including wind farm (WF), photovoltaic station (PV), battery energy storage system (BESS) and load. Similarly, reference [10] evaluates the optimal capacity allocation scheme of an energy storage system (ESS) from two aspects of balancing the reliability and economy of the power grid by using the method of approximate ideal solution sorting. Reference [11] combines the improved artificial bee colony (ABC) algorithm with the entropy weight method to recommend an optimal allocation scheme for the energy storage system. Reference [12] uses the genetic algorithm (GA) for a multi-objective optimization of the three parameters in combined cycle power plants to determine and recommend optimal values. The characteristic of these methods is to leverage the global optimization ability of swarm intelligence optimization algorithms and, after obtaining numerous non-dominated solutions, use evaluation methods to recommend the final result. The disadvantage of this approach is that non-dominated solutions tend to be limited and few in number, while recommended solutions are directly affected by different evaluation methods. Another way to solve an MOP is to combine multiple optimization objectives into a joint optimization objective, transforming it into a single-objective optimization problem (SiOP), of which one of the most common ways is to combine multiple optimization objective functions using summation [13; 14]. For example, in the economic optimization of electric power, reference [15] equates the power loss to an operating cost and then sums it with the economy of other operating units in the system to form an SiOP. Reference [16] transforms the aging damage of the charging and discharging power process of the BESS into the operating cost of the BESS, accomplishing the union of multiple optimization objectives. References [17; 18] equate the amount of discarded new energy as an economic loss for electricity applications, transforming the problem of new energy consumption into an optimization problem for economic objectives. Similarly, in references [19; 20], an economic benefit of the reserve power from the power generation unit has been established to decide the operation status of the power grid and achieve the optimization of scheduling strategies for multiple types of energy. Other than converting various optimization objectives into the economic indicators mentioned above, some studies have achieved the reasonable addition of multiple optimization objectives by eliminating the physical meaning of each optimization objective function or allocating weights [21; 22]. For example, references [23; 24] combine multiple optimization objective functions through a weight allocation method, but the subjective weight values defined by users are easy to cause a lack of objectivity. By analyzing the indicators from the application scenarios, references [25; 26] have assigned the objective function weight and completed the MOP transformation based on the analytic hierarchy process and fuzzy theory, respectively.