# Maximizing Efficiency in Energy Trading Operations through IoT-Integrated Digital Twins
## 3 Proposed Architecture
### IoT-Enabled Task Orchestration-Based Energy Trading Operation  
This study employs an Internet of Things (IoT) framework for task orchestration, utilizing digital twin technology to replicate physical resources and virtualize them within the P2P nano-grid, facilitating energy trading. The methodology involves five main steps of the digital twin, including task generation, virtual object creation, task mapping, task scheduling, task allocation, and deployment through natural language processing (NLP) and the creation of virtual objects. Task scheduling prioritizes tasks based on priority level and deadline, optimizing the sequence for IoT devices. The task allocator assigns tasks to specific devices, enhancing temporal efficiencies, and deployment concludes the process. Our proposed study covers the integration of digital twin-based IoT orchestration, task automation, and efficient energy resource management within the nano-grid context, offering a comprehensive solution for enhanced energy trading and system performance. Figure 3 shows the conceptual overview of digital twin concepts in the form of task orchestration. The layered view starts with task generation, which is generated based on a user-provided description, followed by task mapping using a server on a PC, energy trading tasks scheduling, and the final step of task allocation and deployment.  
The utilization of IoT sensors and Raspberry Pi devices plays a crucial part in the task orchestration of peer-to-peer energy trading within the virtualization process. Internet  
Figure 3: Digital twin-based Internet of Things (IoT) task orchestration layered viewer for optimal energy trading.  
of Things (IoT) sensors are strategically positioned to collect real-time data pertaining to the production, consumption, and storage of energy inside a nano-grid. The data are subsequently transferred to a central orchestrator, which is most likely built on a Raspberry Pi. This orchestrator utilizes task orchestration principles to dynamically oversee and enhance the process of energy trading between multiple participants. By utilizing the data obtained from Internet of Things (IoT) sensors and harnessing the computing capacities of Raspberry Pi, the system is able to make well-informed decisions, effectively coordinating the process of peer-to-peer energy trading in a timely manner. This approach improves the efficiency of the virtualized energy trading system by combining insights from the Internet of Things (IoT) with the automated job management capabilities of the Raspberry Pi platform. The diagram in Figure 4 depicts a framework for facilitating peer-to-peer energy trading through Internet of Things (IoT) sensors, a Raspberry Pi, nano-grid technology, and a digital twin. As shown, Internet of Things (IoT) sensors are utilized to collect real-time data about energy consumption, which is subsequently transferred to the Raspberry Pi. The Raspberry Pi is the central controller for task coordination and energy trading within the nano-grid system. The nano-grid, which serves as a tangible energy infrastructure, interacts with a digital twin for virtualization. The concept of the digital twin involves the simulation of physical entities and the analysis of past data, hence facilitating the development of a comprehensive framework for optimizing energy management and trading processes.  
Figure 4: Overview of Internet of Things (IoT) sensors and Raspberry Pi in proposed IoT-ochesterated Network.