Reduction of battery energy storage degradation in peak shaving operation through load forecast dependent energy management

One application for the increasing number of battery energy storage systems is the reduction of demand charges for industrial consumers through peak shaving. Commonly used lithium-ion batteries are subject to degradation due to a multitude of cell-internal aging processes that can have significant impact on the economics of a system. In this contribution, we propose a rule-based operation strategy to reduce battery degradation during peak shaving through the use of load forecasting. Since load forecasting methods include significant inaccuracies, the operation strategy focuses on means to handle forecast errors. The performance of this operation strategy is assessed through time series based simulations and comparison with reference scenarios. A state of health of 89.7 % is remaining with the proposed strategy after five operating years. This is a reduction of capacity loss of 4.9 % percentage points compared to an often implemented naive peak shaving strategy with 84.8 % remaining state of health, while achieving the same performance in terms of reducing load peaks successfully.