In a groundbreaking study published in ‘Energy Conversion and Economics’, Yizhe Xie from the School of Automation at Nanjing University of Science and Technology has unveiled a novel approach to tackle the pressing issue of network losses in distribution systems, particularly during peak load periods driven by seasonal demands. This research is poised to significantly impact the energy sector, especially for industries reliant on seasonal loads, such as cereal baking and aquaculture processing.
As seasonal loads can lead to pronounced voltage fluctuations and increased peak demands, traditional methods for mitigating network losses are proving inadequate. Xie’s study addresses this challenge head-on by proposing an optimization model that integrates mobile energy storage, switching capacitors, and tie lines. “Our collaborative deployment model not only minimizes annual network losses but also enhances the explainability and feasibility of energy management strategies,” Xie explained.
The innovative approach involves reformulating the proposed model into a mixed-integer linear programming framework, utilizing the inscribed regular dodecagon approximation method. This transformation allows for more effective tracking by advanced solvers, making it a practical solution for real-world applications. The case studies conducted on a unique 55-bus distribution system in East China, which experiences substantial seasonal variations in aquaculture loads, demonstrate the effectiveness of this model. “The results indicate that by deploying multiple reinforcement methods, we can significantly reduce network losses, which is vital for maintaining efficiency in energy distribution,” Xie noted.
This research not only provides a pathway to enhance the reliability of energy systems but also offers commercial benefits for businesses that operate under fluctuating seasonal demands. By reducing network losses, companies can lower operational costs, improve service reliability, and ultimately enhance customer satisfaction. The implications extend beyond immediate financial benefits; they pave the way for more sustainable energy practices that can adapt to the complexities of modern energy consumption.
As the energy sector continues to evolve, the insights from this study may inspire further developments in collaborative energy management strategies, particularly in regions facing similar challenges with seasonal loads. The potential for widespread application of these findings could lead to a more resilient and efficient energy infrastructure, aligning with global sustainability goals.
For more information about Yizhe Xie and his work, visit School of Automation Nanjing University of Science and Technology.
