The economic viability of energy storage systems is essential for their adoption in commercial and industrial sectors. This poster presents findings on the profitability of peak shaving and self-consumption optimization, analyzed both individually and in combination using simulations based on real load data from 50 small- and medium-sized companies in Germany. A key innovation of our research is the integration of a realistic forecasting model with a detailed economic model, enhancing the practical applicability of our results.
We employed AI-based forecasting methods for load and PV generation to model real-world conditions. Different controllers were developed for each use case and their combinations, utilizing a sequential approach. Our analysis revealed that peak shaving is profitable for load profiles with usage hours around or exceeding 2500 hours. In contrast, self-consumption optimization was not found to be profitable for any of the analyzed companies.
Through a 10-year simulation using the NRGISE framework, we conducted sizing studies which showed that a multi-use controller improved the median net present value by 27.59% compared to the single-use peak shaving controller. However, this advantage diminished to a median increase of 4.96% when applying realistic forecasts. These results highlight the substantial difference between ideal and realistic forecasting, emphasizing the significance of forecasts in evaluating energy storage system profitability.
In summary, this research demonstrates that combining multiple applications can enhance the economic viability of energy storage systems. This highlights the importance of such strategies in promoting the acceptance of energy storage systems within the industrial sector.