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| Hauptseite > Publikationsdatenbank > Degradation-cost-aware scheduling of electric vehicle charging and discharging |
| Hauptseite > Publikationsdatenbank > Degradation-cost-aware scheduling of electric vehicle charging and discharging |
| Poster (After Call) | FZJ-2024-00254 |
; ;
2023
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Please use a persistent id in citations: doi:10.34734/FZJ-2024-00254
Abstract: Degradation-cost-aware scheduling of electric vehicle charging and discharging Lithium-ion battery degradation is strongly dependent on operating conditions. Battery degradation imposes costs on electric vehicle owners. To ensure economically viable participation in vehicle-to-grid schemes, the cost of battery degradation due to battery cycling for this purpose has to be taken into account. Cycle depth is one crucial stress factor for battery operation according to tests. Therefore, an optimization model for a vehicle-to-grid-enabled electric vehicle is presented that schedules charging and discharging while modeling calendar degradation as a function of state of charge. To keep the model computationally tractable, a mixed-integer linear program is formulated. Battery degradation in terms of capacity loss is defined as a piecewise-linear function based on an empirical model. In the model, capacity loss due to calendar aging is dependent on state of charge and capacity loss due to cycle aging is dependent on number of full equivalent cycles. In a case study, we optimize the charging and discharging schedule of a vehicle-to-grid-enabled electric vehicle in a household for the prediction horizon of one day. Energy costs consist of an energy costs and a power costs. The energy cost is based on electricity cost per unit. The power price is based on grid fee costs per unit of the daily peak power use. The schedules aim to minimize both energy costs and battery degradation costs. The study compares state of charge levels for the described degradation-cost-aware model to a reference model without degradation cost model. By comparison to the reference model, the study shows that degradation-cost-aware scheduling is able to reduce the degradation.
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