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

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  • Yu, Solui
  • Hur, Jin

Abstract

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.

Suggested Citation

  • Yu, Solui & Hur, Jin, 2025. "An enhanced critical operating constraint forecasting (COCF) for power grids with large scale wind generating resources," Energy, Elsevier, vol. 331(C).
    • Handle: RePEc:eee:energy:v:331:y:2025:i:c:s0360544225026945
    DOI: 10.1016/j.energy.2025.137052
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