<![CDATA[Digital twin (DT) technology is revolutionizing manufacturing by bridging the gap between physical and virtual environments, enabling real-time monitoring, simulation, and optimization of processes. This paper explores the pivotal role of DTs in enhancing renewable energy utilization and energy efficiency within manufacturing ecosystems. The study delves into how DTs facilitate renewable energy forecasting, resource scheduling, and integration into manufacturing operations. Through real-time energy flow analysis, DTs aid in identifying inefficiencies, optimizing production processes, and implementing waste heat recovery systems. Specific applications in automotive and electronics manufacturing underscore the transformative impact of DTs, showcasing reductions in energy consumption and operational costs while improving resilience against energy variability. Case studies highlight successful integrations of DTs with renewable energy systems, such as photovoltaic installations, which strategically align energy-intensive activities with peak energy availability. Moreover, this research examines the challenges associated with DT adoption, including high implementation costs, data integration complexities, and organizational resistance, alongside emerging solutions tailored for scalability, particularly for small and medium-sized enterprises (SMEs). Future directions emphasize the incorporation of blockchain and artificial intelligence to enhance energy transaction security, data-driven decision-making, and operational autonomy. The paper also advocates for the development of global standards and supportive policies to foster widespread DT adoption. By showcasing both the current applications and future potential of DTs, this review underscores their critical role in driving sustainability, operational efficiency, and energy resilience in the manufacturing sector.]]>
