Smart Frequency Control in Multi-Carrier Micro-Grid with the Presence of V2G Electric Vehicles
Subject Areas : journal of Artificial Intelligence in Electrical Engineering
Ebadollah Amouzad Mahdiraji
1
(Department of Engineering, Sari Branch, Islamic Azad University, Sari, Iran)
Maziyar Khodadadi Zarini
2
(Department of Engineering, Sari Branch, Islamic Azad University, Sari, Iran)
Keywords: Electric vehicles, micro-grid, frequency control, ANFIS,
Abstract :
Due to the high cost of fossil fuels, concerns about environmental contamination, and the requirement to satisfy rising energy demands, renewable energy sources have recently gained a lot of attention. Since the output of renewable resources like solar and wind energy depends on meteorological factors, the energy sector faces several issues as a result of their dependability. The microgrid frequency is managed in accordance with the peak use of the gas network. Both the distribution of electric and gas network loads are taken into account. In a multi-carrier network, the frequency is adjusted in a nonlinear manner. On the other hand, the rising trend in production and the use of electric cars has increased the amount of new demands on the electrical network; if effective management is not implemented to handle these new loads, the rise in network frequency deviations might cause the network to malfunction or even collapse. In this study, the ANFIS adaptive fuzzy control approach is utilized to fine-tune the frequency of the network using vehicle-to-grid (V2G) electric cars. The wind turbine, solar panel, battery, flywheel, electric vehicle (EV), diesel generator, and multi-carrier energy hub (MCEH) systems with combined heat and power (CHP) make up the proposed micro-grid. A fuzzy controller is contrasted with the suggested approach in terms of frequency control. The simulations are carried out using MATLAB/SIMULINK software. The simulation results demonstrated that the studied microgrid's SMART controller can deliver stable output power and strong frequency control performance. In terms of effective (RMS) values and maximum frequency deviation, the suggested technique outperformed the fuzzy method.