IoT-orchestrated optimal nanogrid energy management: Improving energy trading performance and efficiency via virtual operations

Abstract

Over the past decade, IoT-orchestrated frameworks have demonstrated significant advantages in enhancing the performance of mission-critical applications. However, distributed generation systems like microgrids and nanogrids have not fully harnessed the capabilities of IoT technology to address the limitations of conventional methods. The effectiveness of a nanogrid's energy trading system depends on several factors, including the efficient management of core components like energy storage systems (ESS) and renewable energy devices. To address these issues, we introduce an optimal power management system for nanogrid energy trading. This system incorporates an RNN prediction module to provide valuable insights to energy distributors. It comprises three core optimization modules: (1) Minimizing Grid Power Consumption, (2) Optimizing Energy Trading Costs, and (3) Managing ESS Power. Our proposed model operates within an IoT-orchestrated framework, utilizing IoT sensors and Raspberry Pi-based Edge technology for virtual execution of operations. We evaluate the model's performance through a case study involving data from 12 nanogrid-equipped houses, resulting in a robust energy trading optimization system. Furthermore, the outcomes of IoT orchestration highlight the potential for virtual operations to enhance system performance significantly.