Reliability Assessment of Power Systems with High Penetration of Renewable Energy Integration—A Data-Assisted Intelligent Model

With the advancement of the “dual carbon” goals, a high proportion of renewable energy sources are being widely integrated into the power grid, resulting in a power system characterized by numerous uncertainties, rapid changes in supply and demand, and strong multi-dimensional randomness. These characteristics pose new challenges to the safe and stable operation of the power grid. However, traditional power system reliability assessment methods are constrained by computational complexity, making it difficult to meet the demands for rapid assessment. To address this issue, this paper designs a data-assisted intelligent model to achieve rapid and accurate reliability assessment of power systems under the context of high-penetration renewable energy integration. Firstly, based on the coupling relationship between the unavailability rate of transmission lines and their operating conditions as well as aging effects, this paper establishes a model for the unavailability rate of transmission lines and proposes a method for selecting key components in the power grid. Subsequently, an analytical model for power grid reliability indicators concerning the reliability parameters of key components is constructed. By utilizing this analytical model to generate a large number of data samples, a Physics-Informed Neural Network (PINN) model is constructed and trained to enable rapid calculation of reliability indicators. Finally, the effectiveness and feasibility of the proposed method are validated through the IEEE standard test system. After analysis, the single-evaluation time of the proposed method is approximately 10 ms, representing a computational efficiency improvement of up to 63.2% compared to existing artificial intelligence models such as MGAT, TCN-BiGRU, etc.