Optimization of electric vehicle charging strategies in residential integrated energy systems: A SARIMA model approach for dynamic electricity prices

Implementing effective operational strategies is important to enhancing the performance of smart building energy networks. While prior research primarily centered on standalone photovoltaic (PV) systems or energy storage (ES) systems, the integration of electric vehicles (EVs), PV systems, and ES systems has demonstrated significant potential in mitigating costs and reducing carbon emissions. Nonetheless, investigations into dynamic pricing strategy (DPS) for optimizing the management of such integrated systems remain scarce. To address this gap, this study proposes a DPS that integrates Seasonal Autoregressive Integrated Moving Average (SARIMA) model-based electricity price forecasts with real-time coordination of PV, ES, and EV operations. The SARIMA model captures short-term and seasonal price fluctuations, providing high-accuracy inputs for DPS, which dynamically schedules energy flows to minimize grid dependency. Specifically, relative to the immediate charging strategy (ICS), the DPS strategy reduces grid purchasing costs by 9.15%; compared to the scheduled charging strategy (SCS), it improves energy storage efficiency by 15.6%; and against the time-of-use pricing strategy (TOU), it achieves a reduction in carbon emissions by 11.02%. Striking a balance among cost efficiency, energy optimization, and environmental sustainability, the DPS strategy outperforms alternative approaches in the management of integrated energy systems. This study offers valuable technical insights and practical guidance for optimizing EV charging schedules and advancing energy management strategies in smart buildings.