# Maximizing Efficiency in Energy Trading Operations through IoT-Integrated Digital Twins
## 1 Introduction  
The Internet of Things (IoT) has resulted in notable transformations across multiple domains, encompassing smart residences, urban settings, and healthcare implementation [1, 2, 3, 4]. One of the key contributors to the advancement of Internet of Things (IoT) technology was [PERSON], who argued for the notion of digitally modifying the physical world to enable intelligent operations ([PERSON] [5, 6, 7]). The Internet of Things (IoT) is a networked framework that facilitates connections between physical objects, such as sensors, software, or similar technologies. This framework enables efficient communication between these devices and individuals over the Internet. The devices encompass a wide spectrum, includingadvanced industrial gear and everyday household items [8]. Based on empirical research about the substantial expansion of the Internet of Things (IoT) framework, projections indicate that the quantity of IoT-enabled gadgets is anticipated to surpass 25.4 billion by 2030. It is anticipated that roughly 33% of the overall amount will be allocated specifically for mission-critical applications [9]. Mission-critical systems encompass various applications, such as real-time navigation, healthcare, and energy power [4; 5; 6; 7; 10; 11]. The inception of microgrids as a concept can be attributed to [PERSON] in 2002. Microgrids are decentralized distribution systems that function at reduced voltage levels and significantly emphasize promoting environmental sustainability. The technologies are specifically engineered to oversee distributed generation systems efficiently [12]. A less complex version of a microgrid, a nano-grid, is often characterized by a load that serves a single family [13]. The scientific community has focused on overcoming shortcomings in minimizing power loss in DC microgrids by employing both the Lagrange multiplier and the distributed gradient algorithm [14; 15]. A nano-grid is widely recognized as an independent system that oversees operations, regulates voltage, and ensures reliability. Generally, the system comprises at least one gateway, establishing an external connection [16]. Integrating a nano-grid system within a healthcare clinic, comprising linked nano-grids that provide peer-to-peer (P2P) exchange of environmentally sustainable energy, can be considered a pivotal and significant application. The term \"peer-to-peer (P2P) energy operation\" refers to the decentralized management and operation of energy generation, consumption, and trade inside a nano-grid system. In the context of these conditions, it is imperative to guarantee a consistent and uninterrupted provision of energy. IoT technology can enhance the efficiency of crucial mission-critical power structures, similar to the indispensable systems in healthcare [4]. This can be achieved by optimizing fundamental aspects of energy operations. An exemplification of such a system is elucidated in [17], wherein the utilization of IoT task orchestration is employed to enhance the efficacy of the nano-grid energy management system. Task orchestration effectively coordinates and automates various jobs and processes by replicating virtual objects. So far, there needs to be more research that explores the utilization of Internet of Things (IoT) technology in the energy sector, specifically in the context of smart grid energy operations, such as energy trading and management. This contrasts the healthcare domain, where IoT technology has been extensively studied and implemented. The utilization of Internet of Things (IoT) technology has the potential to significantly improve the functionality of diverse applications related to the management of smart grids. Employing Internet of Things (IoT) task orchestration within nano-grid energy trading can augment energy transactions' effectiveness, dependability, and mechanization. This implementation also facilitates the instantaneous monitoring, regulation, and enhancement of the nano-grid system.