Economic evaluation of battery storage systems bidding on day-ahead and automatic frequency restoration reserves markets

Abstract

Abstract In future electricity systems, not only electricity generation but also frequency stabilization must be provided by low-carbon technologies. Battery systems are a promising solution to fill this gap. However, uncertainties regarding their revenue potential may hinder investments. Therefore, we apply the agent-based electricity market model AMIRIS to simulate a day-ahead market and an automatic frequency restoration reserves market. Demonstrating the model setup, we chose a scenario with high shares of renewable energies. First, we back-test our model with historic market data from Germany in 2019. The simulation results’ mean day-ahead prices of 39.20\xa0EUR/MWh are close to the historic ones of 38.70\xa0EUR/MWh. Second, we model both markets in a scenario for 2030. The simulated day-ahead market prices are higher on average than observed today, although, we find around 550\xa0h/yr in which the load is fully covered by renewable energies. The variance in simulated prices is slightly higher compared to historic values. Bids on the reserve capacity market are derived from opportunity costs of not participating in the day-ahead market. This results in prices of up to 45\xa0EUR/MW for positive reserve while the prices for negative reserve are 0\xa0EUR/MW. Finally, we evaluate revenue potentials of battery storages. Compared to 2019, we see an improved economic potential and increased importance of the day-ahead market. High power battery storages perform best whereas improvements in round-trip efficiency only marginally improve revenues. Although demonstrated for Germany, the presented modular approach can be adapted to international markets enabling comprehensive battery storage assessments.