Online Aging-Aware Energy Optimization for Vehicle-Home-Grid Integration
It addresses energy cost savings for electric vehicle owners and grid operators through real-time management, though it is incremental by combining existing methods like LSTM and degradation models.
This paper tackles the economic optimization of vehicle-home-grid integration by developing an online algorithm that manages bidirectional energy flows, achieving annual economic benefits up to EUR 3046.81 compared to smart unidirectional charging, with only a 1.96% increase in battery aging.
This paper investigates the economic impact of vehicle-home-grid integration through an online optimization algorithm that manages energy flows between an electric vehicle, a household, and the electrical grid. The algorithm exploits vehicle-to-home (V2H) for self-consumption and vehicle-to-grid (V2G) for energy trading, adapting in real-time via a hybrid long short-term memory (LSTM) network for household load prediction and a nonlinear battery degradation model including cycle and calendar aging. Simulations show annual economic benefits up to EUR 3046.81 compared to smart unidirectional charging, despite a modest 1.96% increase in battery aging. Even under unfavorable market conditions, with no V2G revenue, V2H alone provides yearly savings of EUR 425.48. Sensitivity analyses on battery capacity, household load, and price ratios confirm the consistent benefits of bidirectional energy exchange, highlighting the role of EVs as active energy nodes for sustainable management.