# Maximizing Efficiency in Energy Trading Operations through IoT-Integrated Digital Twins
## 1 Introduction
As previously stated, integrating IoT technology has led to substantial breakthroughs in mission-critical systems within the healthcare and navigation sectors. This integration has replaced manual operations with fully automated systems that utilize edge-based IoT technologies. Nevertheless, numerous research studies have been conducted to explore the utilization of Internet of Things (IoT) technology in energy operations, particularly in smart grid architecture, with a specific emphasis on microgrid and nano-grid energy operations. According to several research, it has been shown that the building sector is accountable for approximately 40% of worldwide energy use. By strategically incorporating IoT orchestration into nano-grid energy trading systems, managing the entire framework orchestrated becomes feasible, improving overall efficiency [5; 6]. The utilization of Internet of Things (IoT) technology has the potential to enhance energy systems' capabilities greatly. The effectiveness of orchestration in mission-critical systems has been demonstrated in its ability to facilitate autonomous process management [18]. The term \"orchestration\" pertains to the automated administration and configuration of system components facilitated by the Internet of Things (IoT), specifically in scenarios that need immediate responsiveness [5; 6]. However, orchestration is widely utilized in other fields, like healthcare and power systems, which still necessitate orchestration to leverage its scalability through automated energy operations. The literature has previously discussed service-level orchestration, but there needs to be more focus on implementing task-level orchestration [5]. A digital twin is a virtual representation of a real-world object, system, or process created by collecting and integrating data from various sources, such as sensors, simulations, and historical records, as shown in Figure 1. It is a dynamic and interactive model mimicking its physical counterpart's behavior, status, and characteristics. Digital twins are used for analysis, monitoring, prediction, and optimization, enabling insights into real-world performance, maintenance, and potential outcomes, particularly in manufacturing, healthcare, and infrastructure management. The critical analysis of contemporary state-of-the-art methods revealed that the energy sector has seen limited exploration of the Internet of Things (IoT) technology, specifically in smart grid operations, like energy power management in distributed generation-based systems. While there is extensive research on IoT applications in healthcare, the energy domain needs a comparable depth of exploration. Furthermore, there needs to be more research focusing on digital twin-based IoT task orchestration within energy systems, particularly in nano-grids. The aforementioned issues have motivated the following key contributions:  
* This study introduces a novel IoT-enabled digital twin perspective on nano-grid energy system orchestration by proposing the inclusion of task-level orchestration, a feature currently lacking in the existing research. * The incorporation of digital twins focuses on the scalability of the nano-grid system by replicating physical resources through virtual objects. * To optimize the energy operations of energy storage systems (ESSs) before peer-to-peer (P2P) trading, the proposed study implements PSO-enabled ESS power optimization. * The application of particle swarm optimization (PSO) can enhance the efficiency of the energy storage system (ESS), facilitating the optimal management of surplus energy. * By conducting a comprehensive evaluation of the system's performance through a case study and comparison analysis, this study aims to contribute significant insights to the current body of knowledge. * The evaluation of the virtualization module encompasses an examination of round-trip time analysis (RTT), latency analysis (LA), and response time (RT).