Enhancing Micro Energy Grid (MEG) Performance by Novel D-FACTS based on GA-ANFIS Integration

Abstract

This paper concerns with enhancing Micro Energy Grid (MEG) performance by Novel Developed Flexible AC Transmission System (D‐FACTS) based on the integration of Genetics algorithm (GA) and Adaptive Neuro‐Fuzzy Inference System (ANFIS). The design and development of MEG, with hardware demonstration, is developed at the Energy Safety and Control Laboratory (ESCL), University of Ontario Institute of Technology. The hardware/software based system includes implementation of control strategies for Distributed Energy Resources (DER) and programmable loads in a laboratory scale; and the appropriate software was developed to monitor all MEG parameters and to control the various components. The interconnection of renewable energy sources, such as wind power, solar PV and others, are implemented, studied and integrated into this MEG. Furthermore, gas based DERs operate as Combined Heat and Power (CHP) to supply both thermal and electrical loads. The design, development, and hardware setup of this MEG has been presented in a planning stage and an operational stage. Firstly, the planning stage optimizes the size and type of DERs for minimum cost and emissions. Then, in the operational stage, there will be the evaluation of the dynamic response to fine tuning the dynamic response. So a novel D‐FACTS device, Green Plug‐Energy Economizer (GP‐EE) with two DC/AC schemes, is proposed and integrated into this MEG. The integrated GA with ANFIS has been applied to control the settings of GP‐EE to fine‐tune the system dynamic response. The proposed controller ensures the adaptation of the global control error of dynamic tri‐loop regulation for GP‐EE. The proposed control strategy leads to get full MEG utilization by increasing the energy efficiency and reliability. Power factor improvement, bus voltage stabilizing, feeder loss minimization and power quality enhancement are realized and achieved. Hardware demonstration with digital simulations have been used to validate the results to show the effectiveness and the improved performance.