EV and RE Uncertainty Analysis in Micro-Grids

Micro-grids have gained significant attention as localized energy systems that incorporate RES (Renewable Energy Sources) and EVs (Electric Vehicles), contributing to enhanced sustainability and resilience. However, the integration of these variable and stochastic components introduces uncertainty to micro-grid operations. This paper presents a comprehensive uncertainty analysis of EV (electric vehicles) and REs (Renewable Energy Sources) in micro-grids. The study begins by developing probabilistic models to characterize the inherent variability in renewable energy generation from sources such as wind and solar. It further discovers the impact of meteorological conditions and geographical location on the uncertainty associated with these resources. Subsequently, the uncertain nature of electric vehicle charging and discharging behaviors is investigated, considering factors like user preferences, charging infrastructure availability, and driving patterns. The paper introduces the LSTM technique for load forecasting in MG (Micro-Grid) operations to assess uncertainty propagation. Several scenarios are investigated in order to assess the dynamic interactions between EVs (Electric Vehicles), RES (Renewable Energy Sources), ESS (Energy Storage Systems) under various uncertainty scenarios. The analysis quantifies the effects of uncertainties on key performance metrics, including energy supply reliability, economic feasibility, and carbon emissions.