Machine Learning Forecasting and GAN-Based Scenario Control for EV Charging and PV Integration

The increasing integration of electric vehicles (EVs) and photovoltaic (PV) generation introduces significant uncertainty into modern distribution grids. This paper presents a dual-stage, AI-driven framework for resilient energy management that combines machine learning-based forecasting with generative modelling for scenario-based control. The first stage uses a hybrid forecasting architecture: long short-term memory (LSTM) and convolutional LSTM (ConvLSTM) models for EV demand prediction and eXtreme gradient boosting (XGBoost) for PV generation forecasting. The second stage employs a generative adversarial network (GAN) to produce realistic EV and PV scenarios, capturing both typical variability and a wide range of operating conditions. The framework is validated on a modified IEEE 33-bus distribution system with integrated EV charging and stationary storage. Results show that the dual-model forecasting approach achieves high accuracy across diverse temporal patterns, while GAN-based scenario generation improves the adaptability of control decisions. Scenario-based optimisation enhances performance under uncertainty, especially at high-variance nodes, and offers greater flexibility than deterministic control in balancing energy cost and demand satisfaction.