Laurent_Power_Flow

Laurent Power Flow

Conventional power flow calculations often rely on iterative methods like the Newton-Raphson algorithm. This becomes a computational bottleneck, especially in the context of training DRL agents, which requires numerous evaluations of power flow. In the proposed framework, we address the computational bottleneck associated with traditional power flow calculations by incorporating a Laurent power flow algorithm. This efficiency approach is achieved by linearizing the power flow equations using a Laurent series expansion, which simplifies the nodal current calculations in the distribution network. By doing so, we facilitate frequent power flow evaluations necessary for training RL agents, without the computational burden.

The Laurent power flow method we employ considers both constant power and constant impedance loads, integrating the ZIP load model directly into the power flow analysis. This approach allows for the inclusion of various types of loads and renewable energy sources without the need for iterative approximation methods typically used in traditional power flow analysis. As a result, our algorithm achieves rapid convergence and permits a more streamlined and scalable RL training process. The elimination of iterative computation not only expedites the power flow assessment but also enhances the RL agent's ability to quickly adapt and learn, thereby improving the overall efficiency and effectiveness of the framework.

For more detailed information on the Laurent power flow method and its application in power systems, please refer to the related paper: A fixed-point current injection power flow for electric distribution systems using Laurent series.