ANALYSIS OF RENEWABLE ENERGY SOURCES AND ELECTRICAL VEHICLES INTEGRATION INTO MICROGRID

Abstract

The increasing penetration of renewable energy sources in microgrid systems has intensified the need for intelligent control strategies capable of ensuring reliability under high load demand and variable generation conditions. While conventional standalone microgrids incorporating photovoltaic, wind, and diesel generator units offer partial autonomy, their operational resilience during extreme contingencies remains limited. In this work, an enhanced microgrid architecture is proposed as an extension of an existing offgrid renewable-based system by integrating the utility grid as an emergency backup source. The grid connection is activated only during critical operating conditions when renewable generation and diesel support are insufficient to meet the load demand, thereby improving system reliability without compromising autonomous operation. Furthermore, the conventional diesel generator controller is upgraded using an Artificial Neural Network (ANN)-based control strategy. The proposed ANN continuously evaluates the instantaneous power contribution from solar and wind sources and dynamically determines the additional power required from the diesel generator to maintain power balance. This intelligent coordination ensures accurate load matching and stable operation under fluctuating generation scenarios. Additionally, the system load capacity is increased from 15 MW to 20 MW with unity power factor operation to evaluate the scalability and robustness of the proposed control approach. Simulation results demonstrate that the coordinated operation of renewable sources and ANN-controlled diesel generation successfully supplies the enhanced load demand, confirming the effectiveness of the proposed modifications for reliable high- capacity microgrid operation.