Energy arbitrage through smart scheduling of battery energy storage considering battery degradation and electricity price forecasts

As a result of growing markets for electric vehicles and residential batteries for buffering energy from photovoltaics, the number of grid-integrated lithium-ion batteries has been continuously increasing in the past years. Apart from their primary purpose, these batteries may also be employed to provide services to the power grid in terms of peak shaving or frequency regulation. The profitability of such services for the battery owner, however, remains a controversial issue. Particularly battery degradation resulting from increased energy throughput is discussed as a major impediment for profitable operation. This paper presents a scheduling approach which considers the non-linear dependencies of battery aging from various operation parameters along with real-time prices and price forecasts for computing optimal charging/dispatching schedules. The methodology is applied to price-data obtained from four different electricity markets. The investigation partly confirms existing profitability concerns but further shows that explicit consideration of battery degradation can yield profitable outcomes. Various scenarios using aggregated and locational marginal prices as well as different forecasting horizons and time resolutions are explored to identify favorable operating conditions.