Sustainable Energy Management and Control for Variable Load Conditions Using Improved Mayfly Optimization

In recent trends, renewable energies are infinite, safe, and are becoming a reliable source for electricity requirements. However, they have certain variations in their results because of climate change, which is its major issue. To solve this challenge, a hybrid renewable energy system was created by combining various energy sources. Energy management strategies must be employed to determine the best possible performance of renewable energy-based hybrid systems, as well as to fulfil demand and improve system efficiency. This work describes an Energy Management System (EMS) for a Hybrid Renewable Energy System (HRES) called Improved Mayfly Optimization-based Modified Perturb and Observe (IMO-MP&O). The developed EMS is based on basic conceptual constraints and has the goal of meeting the energy demand of connected load, ensuring energy flow stabilization, and optimizing battery utilization. In addition, the suggested IMO-MP&O can identify the condition and operating state of every HRES sub-system and assure the network stability of frequency and voltage changes. Numerical simulations in the MATLAB/Simulink environment were used to evaluate the proposed EMS. The simulated results show that the proposed IMO-MP&O achieves the harmonic error of 0.77%, which is less than the existing Maximum Power Point Tracking (MPPT) control and Artificial Neural Network (ANN)-based Z-Source Converter methods.