In the rapidly evolving energy landscape, where the “dual carbon” goals—carbon peaking and neutrality—are steadily advancing and distributed energy resources are becoming increasingly prevalent, the traditional methods of power flow calculation are facing significant challenges. A recent study published in the journal *Power System Technology* (Dianli jianshe) addresses these very issues, offering a novel approach to optimize power flow in integrated transmission and distribution networks while considering carbon emissions.
Led by Dr. Zhang Xianglong from the State Grid Economic and Technological Research Institute Co., Ltd., and his team, including researchers from Zhejiang University, the study introduces a collaborative distributed optimal power flow model. This model leverages a heterogeneous decomposition algorithm to enhance calculation accuracy and efficiency, which are often compromised due to the changing power flow distributions in modern networks.
One of the standout features of this research is the incorporation of carbon flow theory to calculate node carbon emission intensity. “By integrating carbon emissions into the optimization process, we can achieve a more holistic approach to power system operation,” explained Dr. Zhang. This multi-objective optimal power flow model aims to balance economic costs and carbon emissions, a critical consideration for the energy sector as it strives to meet environmental goals without compromising financial viability.
The team proposed a multi-objective linear weighted processing method based on a satisfaction function, which significantly reduces the solving time of the collaborative transmission and distribution power flow by over 50%. This efficiency gain is a game-changer for energy providers, enabling faster decision-making and more responsive grid management.
Dr. Yuan Zhaoxiang, a co-author from Zhejiang University, highlighted the practical implications: “Our model ensures information security and calculation accuracy, which are paramount for the stable operation of transmission and distribution networks. Moreover, it improves the convergence of optimal power flow calculations, making it a robust tool for real-world applications.”
The study’s findings demonstrate that the proposed model can substantially reduce system carbon emissions while maintaining acceptable power generation costs. This balance is crucial for the energy sector, as it navigates the complex interplay between economic and environmental objectives.
The research also addresses a key challenge in the field: the difficulty of constructing an extended Lagrange function for max-min class objective functions using heterogeneous decomposition algorithms. By tackling this issue, the study paves the way for more advanced and efficient optimization techniques in power system operations.
As the energy sector continues to evolve, the integration of distributed energy resources and the pursuit of carbon reduction goals will demand innovative solutions. This research offers a promising approach to meet these challenges, ensuring that the power grid remains efficient, secure, and environmentally responsible.
In the words of Dr. Zhang, “Our work is a step towards achieving a more sustainable and efficient energy future. By optimizing power flow and considering carbon emissions, we can support the energy sector in its transition towards a greener and more resilient grid.”
The study, published in *Power System Technology*, represents a significant advancement in the field of power system optimization, with implications for both commercial and environmental aspects of energy management. As the energy sector continues to innovate, such research will be instrumental in shaping the future of power distribution and transmission.