Abstract
Grid operators are charged not only by their total energy demand, but also by their highest power demand from the superior grid level. The maximum demand charge is usually imposed on the peak power point of the monthly load profile, hence, shaving demand at peak times is of main concern for the aforesaid stakeholders. In this paper, we present an approach for peak shaving in a distribution grid using a battery energy storage. The developed algorithm is applied and tested with data from a real stationary battery installation at a Swiss utility. This paper proposes a battery storage control scheme that can be used for peak shaving of the total grid load under realistic conditions. Particularly, a rule-based approach combined with a deep-learning load forecasting model is developed and its performance is compared with the theoretical optimum based on real data from the field. The analysis includes both technical and economical results from a simulated storage operation and significant outcomes are given for the application of this method.
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