Strategizing EV Charging and Renewable Integration in Texas

TL;DR

This paper develops a strategic framework for EV charging and renewable energy integration in Texas, using clustering techniques to optimize grid stability and promote sustainable energy practices.

Contribution

It introduces a novel methodology combining DTW and k-means clustering to identify optimal EV charging and V2G windows based on load patterns.

Findings

Identified load-based optimal charging windows

Enhanced understanding of renewable and EV load interactions

Proposed decision-making framework for grid stability

Abstract

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.