## 1 Introduction  
Faced with the increasingly severe circumstances of energy and the environment, many actions with respect to renewable energy have been taken around the world. One of the most important targets is to improve the access capacity of renewable energy on the user's side. With the development of smart grids and the implementation of related policies, many users have already attempted to use distributed energy. However, the asynchrony between the output of distributed generation and the users' loads leads to a low utilization rate of renewable energy and low profit for users. To make matters worse, the intermittence of renewable energy may lead to harmful effects on the distribution grid or even power failure due to the absence of a reasonable program [1]. Therefore, the local consumption of distributed generation is an important direction of energy management for both power grids and users. The users of electric power can be divided into residential users, utility users, commercial users, and industrial users. With the development of smart devices and advanced metering infrastructures, several programs of energy management for users have been proposed. The conception of user-side energy management has been proposed, including home energy management systems, building energy management systems, and enterprise energy management systems [2]. Based on user-side energy management systems, smart communities, smart industrial parks, and smart grids are steadily developing [3]. However, many existing energy management systems focus on energy monitoring systems rather than systems with optimization functions. There is much literature about energy management and optimization. Among them, demand side management [4; 5] and demand response technology are the main focus [6; 7]. References [8; 9; 10; 11; 12] explore the method of improving energy efficiency with the assistance of demand response technology, aimed at water heaters, air conditioners, fridges, and washing machines. Reference [13] keeps the balance between economy and comfort by introducing comfort constraints in energy strategies that include distributed generation. In addition, the study of a micro-grid energy management system, analogous to the demand side energy management system, is also a hot topic for researchers [14]. As for the building energy management systems, in reference [15], a semi-centralized decision-making methodology using multi-agent systems was proposed to improve energy efficiency and reduce energy costs. Reference [16] introduces the actual experiment of using a building energy management system. After installing the building energy management system in a 21-floor building in Tirana, the total electrical energy footprint of the building was 135 kWh/m\({}^{2}\)/year; it was 200 kWh/m\({}^{2}\)/year before the installation of the system, which indeed is a massive drop. In addition, an enterprise energy management system was used in Guangzhou Iron and Steel Co., Ltd. (Guangzhou, China), to help them to arrange production [17]. However, there are still some issues that remain to be tackled. For one thing, the above existing studies, ignoring the distribution generation, cannot ensure the application of their technology to the demand-side energy management, which includes distributed generation. Moreover, the available research does not take the industrial task-continuous load into consideration when doing research on energy management. The main contribution of this paper lies in building a model of energy management considering the synchronization between the distributed generation and the industrial task-continuous load. In addition, this paper proposes a method that optimizes the industrial task-continuous load schedule to improve both the local consumption of the distributed generation and the economic benefit of users. Specifically, the contribution of this paper is as follows:  
1.