Autonomous Operation of Stationary Battery Energy Storage Systems—Optimal Storage Design and Economic Potential

Abstract

Global warming requires a changeover from fossil fuel based to renewable energy sources on the electrical supply side and electrification of the demand side. Due to the transient nature of renewables and fluctuating demand, buffer capacities are necessary to compensate for supply/demand imbalances. Battery energy storage systems are promising. However, the initial costs are high. Repurposing electric vehicle batteries can reduce initial costs. Further, storage design optimization could significantly improve costs. Therefore, a battery control algorithm was developed, and a simulation study was performed to identify the optimal storage design and its economic potential. The algorithm used is based on autonomous (on-site) optimization, which relies on an incentive determining the operation mode (charge, discharge, or idle). The incentive used was the historic day-ahead stock market price for electricity, and the resulting potential economic gains for different European countries were compared for the years 2015–2019. This showed that there is a correlation between economic gain, optimal storage design (capacity-to-power ratio), and the mean standard deviation, as well as the mean relative change of the different day-ahead prices. Low yearly mean standard deviations of about 0.5 Euro Cents per kWh battery capacity lead to yearly earnings of about 1 €/kWh, deviations of 1 Euro Cent to 10 €/kWh, and deviations of 2 Euro Cents to 20 €/kWh. Small yearly mean relative changes, lower than 5%, lead to capacity-to-power ratios greater than 3, relative changes around 10% to an optimal capacity-to-power between 1.5 and 3, and for relative changes greater than 10% to an optimal capacity-to-power ratios of 1. While in countries like the United Kingdom, high potential earnings are expected, the economic prospective in countries like Norway is low due to limited day-ahead price performance.