## 4 Optimization Applications for SGs in Smart Manufacturing
### Optimizing SG Systems for Smart Manufacturing  
To optimize their SG systems to avoid shutdowns in continuous manufacturing operations, as shown in Table 2, the applications of using AI and optimization technologies for SGs in smart manufacturing are detailed as follows:  
1. Energy cost management: [PERSON] and [PERSON] [86] developed an algorithm for forecasting manufacturing energy load to effectively reduce peak facility power. [PERSON] and [PERSON] [87] proposed an incentive-based demand response algorithm to enable the SG system to have reinforcement learning and deep neural network capabilities. Targeting natural gas demand in the SG, [PERSON] and [PERSON] [88] proposed a modified simulated annealing algorithm to establish a production scheduling model to allow manufacturers to reduce energy costs. [PERSON] et al. [89] proposed a mixed integer linear programming model to schedule actual multi-tasks to minimize the energy cost. 2. Installation of smart meters: To effectively manage energy consumption, [PERSON] [90] adopted smart meters and an artificial bee colony-based random forest clustering algorithm for data classification and analysis, and the adopted method was more accurate than other classification methods. [PERSON] et al. [91] developed a smart meter data-driven rate model to recover distribution network-related charges and imported grid-edge technologies to meet the needs of consumers of different power scales and save costs. 3. Reliable energy system: [PERSON] and [PERSON] [92] carried out a reliability assessment for SG-integrated distributed power-generating with AI methodology-based search algorithms to ensure the reliability and accuracy of the power system. [PERSON] et al. [93] simulated the SG system being attacked by the IoT energy network through an attack generator algorithm and used the deep neural network to detect it to establish a safe and reliable energy system. 4. Establishment of the digital twin: [PERSON] et al. [94] surveyed the approaches and applications of digital twins for energy systems. [PERSON] et al. [95] proposed a complex SG system with the digital twin based on data and knowledge for duplication of similar unit-level and management. In view of the large energy consumption and fluctuations in the manufacturing system, [PERSON] et al. [96] developed the stored energy allocation model based on the digital twin technology to optimize energy allocation and reduce CO\({}_{2}\) emissions. Figure 3: Illustration of a smart manufacturing framework based on the SG system [82; 83; 84; 85; 4]. Data-driven optimization: [PERSON] et al. [97] surveyed smart manufacturing energy policies and cases, in which a lot of actual cases used SG data collection and analysis and machine learning methods to control energy consumption and electricity prices, allowing continuous data-driven optimization. To monitor and optimize the energy consumption of manufacturing factories, [PERSON] et al. [98] proposed a data-driven energy prediction model to control machine energy consumption and fault warning and improve energy efficiency. [PERSON] et al. [99] summarized the solutions to energy consumption in the manufacturing industry and explained how to make smart manufacturing move forward toward sustainable development through big data collection and the development of decision-making technologies. * Quality-of-service (QoS) of communication networks and data collected: [PERSON] and [PERSON] [100] considered that electromagnetic interference and multipath effects exist at the manufacturing site due to the use of industrial wireless sensors and IoT, and they would affect the QoS for data collection. They then proposed a QoS-aware data acquisition protocol model to reduce data error rate and improve the quality of manufacturing data communication. [PERSON] et al. [101] proposed a software-defined network (SDN) for the energy internet to improve the response time and QoS of the controller, which can also increase the utilization rate of green energy in the SG system.