Strategizing EV Charging and Renewable Integration in Texas
Mohammad Mohammadi Jesse Thornburg
Exploring the convergence of electric vehicles (EVs), renewable energy, and
smart grid technologies in the context of Texas, this study addresses
challenges hindering the widespread adoption of EVs. Acknowledging their
environmental benefits, the research focuses on grid stability concerns,
uncoordinated charging patterns, and the complicated relationship between EVs
and renewable energy sources. Dynamic time warping (DTW) clustering and k-means
clustering methodologies categorize days based on total load and net load,
offering nuanced insights into daily electricity consumption and renewable
energy generation patterns. By establishing optimal charging and
vehicle-to-grid (V2G) windows tailored to specific load characteristics, the
study provides a sophisticated methodology for strategic decision-making in
energy consumption and renewable integration. The findings contribute to the
ongoing discourse on achieving a sustainable and resilient energy future
through the seamless integration of EVs into smart grids.