In the rapidly evolving landscape of distributed energy systems, the optimal scheduling of battery energy storage systems (BESS) is a critical factor in ensuring stability and cost-efficiency. However, the non-linear characteristics of equipment like BESS and power converters can lead to discrepancies between planned and actual operations, posing significant challenges for energy providers. A groundbreaking study published in the journal *Nature Scientific Reports* by Anastasiia Zhadan of St. Petersburg State University offers a promising solution to this problem through innovative linearization techniques.
The research focuses on mitigating errors in the BESS charging and discharging planning process by linearizing the non-linear efficiency curves of equipment. “Non-linear equipment characteristics can lead to errors in stored energy between the schedule and actual operation,” explains Zhadan. “Our research proposes a technique to address these errors, ensuring more accurate and efficient energy management.”
The study implements and compares three linearization techniques: Special Ordered Set type 1 (SOS1), Special Ordered Set type 2 (SOS2), and the Taylor method. These techniques are applied to model and control the charging and discharging of BESS, as well as DC/AC and AC/DC converters. The research also provides heuristics for selecting initial points on the efficiency curves, enhancing the effectiveness of the control methods.
The experimental results presented in the study confirm the effectiveness of the proposed control approaches in solving operational problems caused by non-linear equipment characteristics. This research has significant implications for the energy sector, particularly in optimizing the performance of distributed energy systems.
“By linearizing the non-linear characteristics of equipment, we can achieve more accurate scheduling and control, leading to reduced costs and improved system stability,” says Zhadan. This innovation could revolutionize how energy providers manage their storage systems, ultimately benefiting consumers through lower costs and more reliable service.
The findings of this research are not only academically significant but also hold substantial commercial potential. As the energy sector continues to evolve, the ability to accurately model and control non-linear equipment characteristics will be crucial in meeting the demands of a more distributed and decentralized energy landscape.
In the words of Zhadan, “This research opens up new avenues for optimizing energy storage systems, paving the way for more efficient and cost-effective energy management solutions.” As the energy sector looks to the future, the insights gained from this study could shape the development of next-generation energy storage technologies and strategies.
With the publication of this research in *Nature Scientific Reports*, the energy community now has a robust framework to address the challenges posed by non-linear equipment characteristics, ensuring a more stable and cost-effective energy future.