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This article analyzes the economic impact of price forecast errors on the optimal operation schedules of distributed (battery) storage systems. The presented simulation model extends a linear optimization model that achieves up to 17% annual savings for a storage system in an environment with dynamically changing electricity prices and under the assumptions of ex-ante known load and price data. The main contribution of this paper is to replace the deterministic load and price curves by imperfect forecasts of which the effect of price forecast errors is systematically analyzed. All results are benchmarked against the optimal result of the basic model.The main finding is that the underlying storage optimization model performs with a high robustness against price forecast errors. E.g., up to 10% Mean Absolute Percentage Error (MAPE) for day-ahead price forecasts lead to less than 10% deviation from the optimal result. I.e., the storage model yields up to 15% annual savings vs. 17% in the optimal case.

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