Artificial Intelligence-Driven Optimal Charging Strategy for Electric Vehicles and Impacts on Electric Power Grid

Electronics, Journal Year: 2025, Volume and Issue: 14(7), P. 1471 - 1471

Published: April 6, 2025

Electric vehicles (EVs) play a crucial role in achieving sustainability goals, mitigating energy crises, and reducing air pollution. However, their rapid adoption poses significant challenges to the power grid, particularly during peak charging periods, necessitating advanced load management strategies. This study introduces an artificial intelligence (AI)-integrated optimal framework designed facilitate fast mitigate grid stress by smoothing “duck curve”. Data from Caltech’s Adaptive Charging Network (ACN) at National Aeronautics Space Administration (NASA) Jet Propulsion Laboratory (JPL) site was collected categorized into day night patterns predict duration based on key features, including start time requested. The AI-driven strategy developed optimizes management, reduces loads, alleviates strain. Additionally, evaluates impact of integrating 1.5 million, 3 5 million EVs under various AI-based strategies, demonstrating framework’s effectiveness managing large-scale EV adoption. consumption reaches around 22,000 MW without EVs, 25,000 for 28,000 35,000 any strategy. By implementing optimization that considers both early duck curve smoothing, demand is reduced approximately 16% 21.43% 34.29% EVs.

Language: Английский

Two-stage dual-attention spatiotemporal joint network model for multi-energy load prediction of integrated energy system

Sustainable Energy Technologies and Assessments, Journal Year: 2024, Volume and Issue: 72, P. 104085 - 104085

Published: Nov. 18, 2024

Language: Английский