Enhancing EV Charging Station Integration: A Hybrid ARIMA-LSTM Forecasting and Optimization Framework

Abstract

Electric vehicles (EVs) offer substantial environmental benefits but introduce challenges to power grids due to capacity limitations. Effective deployment and coordination of EV Charging Stations (EVCS) are crucial, as improper placement can lead to increased power losses and voltage fluctuations. This paper presents a comprehensive approach to address these challenges by proposing a deep learning-based, two-tier optimization strategy for EVCS placement within distribution networks. A hybrid forecasting model that combines autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) techniques is employed to accurately predict EV charging load. The ARIMA model captures seasonality and transient patterns, while the LSTM model identifies long-term dependencies, enhancing forecasting accuracy. Additionally, the paper introduces a Hybrid Archimedes-Genetic Algorithm (HAG), which merges the Archimedes Optimization Algorithm (AOA) with the Genetic Algorithm (GA). This multi-objective approach aims to minimize both active and reactive power losses and control voltage deviations. Tested on IEEE 33-bus and 69-bus systems using MATLAB and Python, the HAG method demonstrated significant improvements, with reductions in active losses of up to 33% and 10.7%, and reactive losses of up to 33% and 10.8% compared to GA and AOA alone. These findings underscore the effectiveness of combining accurate EV charging load forecasting with advanced optimization techniques to optimize EVCS placement while minimizing network impact.