Enhancing Hybrid Wind-Solar-Battery System Efficiency through Artificial Neural Network-Based Energy Management and Voltage Regulation in Micro Grids

Abstract

Energy management in micro grids involves an integrated information and control system pivotal for optimizing energy flow from generation and distribution, minimizing operational costs. Energy Management Systems (EMS) are crucial for leveraging distributed energy resources, especially amidst variable generation and pricing. This paper introduces an Artificial Neural Network (ANN)-powered approach for managing a hybrid wind, solar, and Battery Storage System (BSS). Additionally, a 3 Port DC-DC Converter is proposed to sustain DC voltage. While renewable energy systems offer numerous benefits, their intermittent power generation poses challenges, resulting in grid power fluctuations. EMS seeks to mitigate these fluctuations while preserving the battery state of charge (SOC) within permissible limits to extend battery life. Implementation is conducted using the Simulink/Matlab platform. The efficacy of the proposed approach is demonstrated by comparing the Total Harmonic Distortion (THD) of the suggested controller (1.52%) against conventional controllers: ZSI-based PID (3.05%), PI (4.02%), and FO-PI (3.32%).