EV Charging Station Integrated Microgrid Planning by Using Fuzzy Adaptive DE Algorithm

Due to environmental concerns, renewable energy has gained significant popularity over the past two decades. Integrating distributed generation and renewable energy sources, particularly through microgrids in power distribution systems, has become feasible. Additionally, there has been a notable increase in the adoption of electric vehicles (EVs) driven by environmental initiatives and their advantages over internal combustion engines. As a result, the planning and operation of microgrids in distribution systems have become more complex. To address these complexities, computational evolutionary algorithms have emerged as effective solutions. The Differential Evolution (DE) algorithm stands out for its speed and userfriendly simplicity. The proposed study uses the Fuzzy Adaptive Differential Evolution (FADE) analysis for microgrid planning integrated with EV charging infrastructure, using the IEEE 33bus system. The FADE algorithm combines the power of fuzzy logic and adaptive strategies within the DE framework to tackle the planning and optimization challenges of microgrids integrated with Electric Vehicle Charging Stations (EVCS). The findings provide valuable insights into the effectiveness of the FADE algorithm in addressing the challenges associated with the planning and operation of microgrids with EVCS in modern power systems.