In the ever-evolving landscape of power grid management, a novel approach has emerged that promises to enhance the efficiency and reliability of power flow calculations, particularly in large, heavily loaded grids. This innovation comes from Yi Zhang, a researcher at the College of Electrical Engineering and Automation, Fuzhou University, whose work has been recently published in the English-language journal “International Journal of Electrical Power & Energy Systems.”
Zhang’s research focuses on improving the holomorphic embedding method (HEM), a recursive algorithm known for its independence from initial value selection and its provision of analytical solutions. However, HEM has faced challenges in practical applications due to numerical problems that arise when calculating high-order power series coefficients, leading to convergence issues in power flow calculations for large grids.
To address these challenges, Zhang proposes a dynamic power restart holomorphic embedding method (DPRHEM). This method leverages the initial value flexibility of the fast and flexible holomorphic embedding power flow method (FFHEM) and introduces a dynamic update restart mechanism based on changes in power residuals. “The dynamic update restart mechanism allows the power state to constantly approach the target power state, ensuring stable and reliable convergence during power flow calculation in large power grids with heavy loads,” Zhang explains.
The significance of this research lies in its potential to improve the efficiency of power flow calculations, a critical aspect of power grid management. By restricting the power series coefficients of the analytic function to low orders, DPRHEM ensures that the power state continually approaches the target state, enhancing the overall stability and reliability of the grid.
Zhang’s work compares the performance of DPRHEM with other methods, including the Newton-Raphson method (NRM) and various restart-based HEMs, using the case9 and case13659pegase systems of matpower8.0b1, as well as the actual East China regional power grid. The results demonstrate that DPRHEM exhibits better convergence and higher computational efficiency in large, heavily loaded grids.
The implications of this research for the energy sector are substantial. As power grids continue to grow in size and complexity, the need for efficient and reliable power flow calculations becomes increasingly critical. DPRHEM offers a promising solution to these challenges, potentially shaping the future of power grid management and contributing to the stability and efficiency of energy distribution.
In the words of Yi Zhang, “This method not only improves the convergence characteristics of HEM but also enhances the efficiency of power flow calculation, which is crucial for the stable operation of large power grids.” As the energy sector continues to evolve, innovations like DPRHEM will play a pivotal role in meeting the demands of a rapidly changing landscape.