## 1 Introduction  
Since the concept of Industry 4.0 was proposed, manufacturers have actively introduced the latest technologies such as the Internet of Things (IoT), artificial intelligence (AI), big data, machine learning, blockchain, edge computing, 5G, drone applications, AR/VR, and cyber-physical systems into manufacturing processes and operations [1]. Smart manufacturing is not only beneficial to optimize product manufacturing processes with minimum costs, but also conducts product life cycle management to reduce energy consumption [2]. In smart manufacturing, the IoT collects data, including the input/output data and parameters (or recipes) for manufacturing equipment, workforce-related data, and work environmental conditions. Then, based on the data, the AI and optimization techniques are further developed to provide intelligent decisions and actions to improve the manufacturing processes and operations while optimizing some objectives, e.g., minimizing production costs and energy consumption. Recently, manufacturers have paid increasing attention to achieving net zero carbon emissions by 2050. Under uncertain energy demand, manufacturers control intermittent energy generation and usage time, as well as long-term operation planning, and must develop economical designs and smart resource utilization of micro-grids to achieve net-zero energy operations [3]. Therefore, there is of urgent need for manufacturers to effectively implement manufacturing resource allocation (including energy data and equipment resources), manufacturing data monitoring (including big data collection and analysis), and manufacturing process control (including energy usage and cost control) to achieve the goal of sustainable and smart manufacturing. There are requirements for continuously developing novel technologies and sustainable environments, therefore, manufacturers rely increasingly on electricity and require a smart, efficient, and reliable energy management system. As a solution, the smart grid (SG)replaces the existing electrical grid to effectively adjust and distribute energy according to demand [4]. Generally, the SG is integrated with renewable energy (e.g., solar, wind, and geothermal energy) to provide clean, sustainable, efficient, and reliable energy sources, allowing manufacturers to have better choices for energy planning in manufacturing processes. As shown in Figure 1, the SG provides a platform for energy supply using the latest technologies (including communication technologies, information provision, cybersecurity, and computational intelligence) to demonstrate various characteristics, including self-healing, flexibility, prediction, interaction, optimization, and security [4]. The application areas of SG have existed not only in life, but also widely in different industries. [PERSON] [5] investigated SG technologies and their applications that provide two-way power systems in industrial applications, electric vehicles, home buildings, intelligent electronic devices, and local area networks. [PERSON] et al. [6] reviewed distributed energy resources applications as well as control prediction in wind energy conversion systems, solar photovoltaics, fuel cells, and energy storage systems. [PERSON] et al. [7] provide an energy transformation solution of SG for various industries for strengthening the power system, integrating renewable sources, electrifying the transport sector, and harnessing bioenergy. However, as the power market has become increasingly free and open, more diversified energy sources are integrated into the SG, which increases the complexity of transmitting and distributing energy. Over time, such an increasing interconnection scale makes the SG system more complex than the previous systems. Therefore, it has been important and unavoidable to investigate how to maintain a stable energy supply through the SG system.