Maximizing Power Utilization through Machine Learning and IoT based Power Flow Strategies in DC Micro Grids with Renewable Energy Resources
G. Senthilkumar, Balasubbareddy Mallala, Sri Sivarajan, Cholleti Harish, Devareddy Harsha, L. Natrayan
Abstract
The various challenges in the power grid with renewable energy systems are overcome using the implementation of DC microgrids. This helps in obtaining a reliable and efficient energy management system. The maximization of power utilization in microgrids with renewable energy resources is achieved using machine learning with simulated annealing optimization integrated with the Internet of Things through power flow strategies. The variable and intermittent generation patterns are exhibited through renewable energy sources such as wind and solar energy sources. The energy is monitored and obtained through control strategies. The proactive energy management with energy storage capacity and load demand through renewable energy is achieved through machine learning algorithms. The optimum allocation of energy resources is achieved through a simulated annealing optimization algorithm. Another parameter includes the energy distribution strategy that helps to maximize the power utilization in microgrids. This is initiated through factors such as energy storage capacity and various energy generation constraints. This helps to improve the grid stability by reducing the energy losses in the system during transmission and distribution. The power flow strategies with real-time monitoring and control parameters are achieved through the Internet of Things (IoT). The storage levels are monitored using sensors and smart devices. This helps to obtain sustainable power utilization with improved reliability