An Improved Multiple Adaptive Neuro Fuzzy Inference System Based on Genetic Algorithm for Energy Management System of Island Microgrid

Microgrid (MG) is basically composed of different distribution generators (DGs) connected in parallel for supplying a specific set of loads managed by an Energy management system (EMS). EMS is a control system integrated within MGs for managing the operations of these DGs effectively to fulfill a power balance between power production and load demand in the most optimal way, especially in island MGs. In this paper, an EMS based on Multiple Adaptive Neuro-Fuzzy Inference System optimized by Genetic Algorithm (MANFIS-GA) is proposed for PV/Wind/Diesel Generator/Battery (PWDB) island MG system, to optimize the output power of diesel generator, manage charging-discharging operation of MG Battery Storage keeping its State of Charge (SOC) in acceptable limits, and improve the MG system reliability and stability by mitigating the effects of sudden changes in the electrical loading and Renewable energy sources (RES) Power. The prediction system is implemented by using 8760 samples based on an hourly meteorological data of a whole year. GA is used as an optimization technique for training MANFIS to accomplish the desired objects of EMS. For evaluation purpose, a real case study of a day-ahead data is tested and discussed in details. Experiments show that the proposed smart system provides accurate results for the expected outputs and achieves a good performance.