Impact Analysis of Electric Vehicles and Solar Distributed Generations in Power Systems Using Grey Relational Analysis and t- Stochastic Neighbour Embedding

The increasing prevalence of electric vehicles (EVs) presents challenges to the grid stability, particularly due to their uneven distribution throughout the network. This study investigates the impact of varying EV penetration levels and optimizes the placement of distributed generation (DG) and EVs to enhance grid voltage stability. A 24-hour load flow analysis using Electrical Transient Analyzer Program (ETAP) software evaluates voltage stability, power losses, and ampere loading, providing input for grey relation analysis (GRA) to optimize DG and EV placement, with EVs modeled as both loads and sources. However, at 100% EV penetration, voltage drops by 7.5% during peak hours, and system losses increase by 15%. The use of DGs and EVs alleviates these challenges by enhancing the voltage profile to exceed 95% and decreasing system losses by 19%. Load flow results are further analyzed using the t- Stochastic Neighbour Embedding (t-SNE) machine learning algorithm, revealing deeper insights into system behavior and underlying patterns. The findings indicate that higher EV penetration enhances power system voltage stability and efficiency, with strategic DG placement and time-of-use strategies being essential for minimizing voltage drops, reducing losses and optimizing grid performance.

Impact Analysis of Electric Vehicles and Solar Distributed Generations in Power Systems Using Grey Relational Analysis and t- Stochastic Neighbour Embedding Keywords:

electric vehicles , Solar PV , Vehicle to grid , Grid to vehicle , GRA , t-SNE