Wind power ramp event detection with a hybrid neuro-evolutionary approach

A novel approach for power ramps classification in wind generation

Ramp events are characterized by large power changes in a short period and increase with increasing renewable generation. Even with hourly forecasts, their predictions are still unreliable. Thus, grid operators should classify these power ramps to understand their expected occurrence periods and range to balance them. Previous research was based on a binary classification of ramp events, which assumed that ramp events were similar to one another, which is not true. Some other studies used randomization and non-causative classification methods. Hence, a more accurate method is still needed. The paper presents two new methods for ramp event classification. The first method depends on the standard deviation score, and the second method assigns a score to each ramp, which depends on the maximum value of the historical power ramps that occurred within the studied time period. The new classification methods are applied to the output power of Belgium's aggregated wind farms from 2015 to 2019, and the relative frequency of each ramp category is determined. The results revealed that, even though the capacity of wind installations has doubled, ramping behaviour is nearly the same in all years.

A Practical Metric to Evaluate the Ramp Events of Wind Generating Resources to Enhance the Security of Smart Energy Systems

The energy industry, primarily based on the use of fossil fuels (e.g., coal and oil) is rapidly shifting toward renewable energy for securing sustainable resources. Thus, preparing for large wind power ramp events is essential to retain reliable and secure power systems. This study proposed a new statistical approach to predict wind power ramp events, and evaluated the performance of prediction. The empirical data, which is the observed wind power output data and wind speed data from Taebaek (South Korea) were used for analyzing ramp events and for evaluation. Based on the data analysis, a practical metric for evaluating the performance of wind power ramp events forecasting was developed and presented in detail. Notably, the accuracy of forecasting was evaluated through various metrics, whereas the normalized mean absolute error (NMAE) analysis demonstrated ≤ 10% values for all the analyzed months. In addition, a system review was conducted to check if the methodology suggested in this study has helped enhance the security of power systems. The results show that evaluating and considering the ramp events can improve the accuracy of wind power output forecasting which can secure the smart energy systems.