An enhanced critical operating constraint forecasting (COCF) for power grids with large scale wind generating resources

The installation of renewable energy sources, especially wind energy, is rapidly increasing as a response to issues pertaining to carbon emission. As wind power has high intermittency and volatility, the integration of large-scale wind generating resources undermines power system reliability, and forecasting of power flow through transmission lines is a significant issue. Herein, we propose the use of the improved forecasting model termed critical operating constraint forecast (COCF), which integrates wind power, for the forecasting of wind output using a convolutional neural network and long short-term memory algorithm in Case Study 1. In addition, time- and season-based analysis is performed in Case Study 2 to further enhance the proposed forecasting methodology. The enhanced COCF technique calculates the transmission line flows and identifies constraint-violated transmission lines. This methodology was applied to the power system of Jeju Island in Korea, which was declared a Carbon-Free Island 2030, by incorporating wind power integrated scenarios. As a result, Case Study 1 shows that the average loading of the critical lines (Lines 9 and 10) was 70.4 %, reaching a maximum of 89.6 %, while in Case Study 2, the average loading increased to 88.3 %, with Lines 39 and 40 reaching maximum overloads of 113.2 % and 107.7 %, respectively. Furthermore, the highest line flows were observed 19:00 to 23:00, and seasonal analysis revealed that during summer, the loading nearly reached 100 %, indicating a significantly increased risk of transmission constraints during peak demand periods. For this study, transmission line constraint violations in Jeju Island were successfully identified based on the transmission line flow calculation.