Development of a Charging Profitability Function for the Optimization of Energy Storage Systems

The increasing volatility in electrical energy markets, driven by renewable energy expansion, necessitates flexibility measures to reduce costs, cut emissions, and improve grid stability. An effective measure is charging energy storages when energy prices are low and using stored energy when they are high. However, the terms “high” and “low” are not easily quantifiable. This article introduces a method for applying the so-called “Charging Profitability Function” (CPF), which evaluates the current energy price relative to a weighted moving average and weighted moving standard deviation. The CPF provides a normalized valuation of current prices, with a configurable reference time window and weighting functions. Unlike existing methods with binary price classifications, the CPF offers a continuous output, quantifying the intensity of these terms. The CPF should serve as a cost function in optimization problems primarily aimed at minimizing electricity costs and CO 2 emissions, thus supporting climate targets. The CPF’s continuous output and configurability make it particularly suitable for rule-based optimization. Due to the positive correlation between electricity prices and CO 2 emission factors, there is no conflict of objectives for the optimization variable. An exemplary use case demonstrates the CPF’s configuration, using it in an energy-flexible fuzzy controller to optimize the operation of a manufacturing plant’s ventilation system. This application achieved cost savings of 28.9 % and emission reductions of 21.5 % compared to the conventional operation.