Networked Hybrid AC-DC Microgrids: Leveraging Fog Computing and Linear Solver for Efficient Energy Management

The global electricity landscape is undergoing a profound transformation, with an increasing demand for resilient and sustainable energy infrastructure. In this context, microgrids (MGs) have emerged as a promising solution, offering localized, decentralized energy generation and distribution. This research paper proposes a distributed energy management system for grid-connected hybrid AC-DC MGs, interconnected through a DC link. The work proposes a three-layer cloud fog-enabled energy management system of networked MGs which aims to minimize the energy cost by facilitating optimal energy utilization within each MG as well as among the connected MGs. The paper presents a fog-enabled comprehensive mathematical model of networked MGs to ensure fast data transmission and real-time decision-making within the system. K-mean clustering is used to segregate the load into three categories residential, commercial, and industrial each of which is primarily supplied by an individual MG. Python 3.10.12 programming has been employed for simulating the model, ensuring a realistic and adaptable approach to assess the suggested energy management system's efficacy and performance within the context of networked MGs. Simulation results demonstrate that the proposed model of networked MGs integrating fog computing and MILP optimization, enhances optimal energy allocation and utilization within and among MGs along with minimizing the operating cost of networked MGs effectively.