In the dynamic world of energy distribution, ensuring a stable and efficient power supply is paramount. Researchers have long sought ways to optimize the operation of active distribution networks (ADNs), which are the backbone of modern power systems. A recent breakthrough, led by Shumin Sun of the State Grid Shandong Electric Power Research Institute, promises to revolutionize how we manage these networks. The study, published in the journal Scientific Reports, introduces a novel approach to multi-objective collaborative optimization using an improved particle swarm optimization algorithm.
The research addresses a critical issue: ADNs are susceptible to disturbances, leading to degraded power supply quality and compromised operational safety. Sun and his team have developed a sophisticated method to tackle these challenges. By constructing an objective function for multi-objective collaborative optimization, they have created a framework that enhances both the frequency and time domain operations of ADNs.
The improved particle swarm optimization algorithm is at the heart of this innovation. This algorithm optimizes the collaborative configuration of ADN operations, ensuring that the system can handle peak power demands more effectively. “The experimental results show that during peak periods, the system’s load capacity is only twice that of before optimization or other situations, achieving stable power supply for peak power demand,” Sun explains. This means that during times of high demand, the system can maintain stability without overloading, a significant advancement for energy providers.
The implications for the energy sector are vast. With the ability to manage load grades and optimize both reactive and active power, this research could lead to more efficient and reliable power distribution. For commercial entities, this translates to reduced downtime, lower operational costs, and enhanced service quality. “Multi-objective collaborative optimization in frequency domain and time domain has the best effect,” Sun notes, highlighting the comprehensive nature of their approach.
The architecture of the simulation platform for cooperative operation of ADN, constructed by the team, further underscores the practical applicability of their findings. By dividing the load grades of the distribution system and managing loads hierarchically, the researchers have demonstrated a method that can be integrated into existing infrastructure. This hierarchical management ensures that the system remains robust and adaptable to varying demands.
As the energy sector continues to evolve, the need for advanced optimization techniques becomes increasingly apparent. This research, published in the journal Scientific Reports, offers a glimpse into the future of ADN management. By leveraging improved particle swarm optimization, energy providers can achieve unprecedented levels of efficiency and reliability. The work of Shumin Sun and his team at the State Grid Shandong Electric Power Research Institute sets a new benchmark for ADN operation, paving the way for future developments in the field.
