# Optimizing EV charging stations and power trading with deep learning and path optimization

## TL;DR

This paper presents a system using deep learning and optimization to manage EV charging and power trading, improving grid efficiency and user satisfaction.

## Contribution

The novel contribution is an integrated framework combining deep learning, reinforcement learning, and path optimization for EV charging and power grid management.

## Key findings

- An LSTM model improved EV demand forecasting accuracy by 12.3%.
- DQN reduced supply-demand imbalances in charging stations by 8.9%.
- Power trading optimization reduced LMP disparities by 10%.

## Abstract

The rapid growth of electric vehicles (EVs) presents significant challenges for power grids, particularly in managing fluctuating demand and optimizing the placement of charging infrastructure. This study proposes an integrated framework combining deep learning, reinforcement learning, path optimization, and power trading strategies to address these challenges. A Long Short-Term Memory (LSTM) model was employed to predict regional EV charging demand, improving forecasting accuracy by 12.3%. A Deep Q-Network (DQN) optimized charging station placement, reducing supply-demand imbalances by 8.9%. Path optimization, using the Dijkstra algorithm, minimized travel times for EV users by 11.4%. Additionally, regional power trading was optimized to balance electricity supply and demand, reducing locational marginal price (LMP) disparities by 10%. The combined system resulted in reduced grid congestion, lower operational costs, and improved user satisfaction. These findings demonstrate the potential of integrating advanced machine learning techniques with power grid management to support the growing demand for EVs.