Active power control strategy for wind farms based on power prediction errors distribution considering regional data.

Area of Science:

• Renewable Energy Systems
• Power Systems Engineering
• Computational Intelligence

Background:

• Wind energy is a crucial renewable resource for grid integration.
• Wind power exhibits inherent randomness and volatility, challenging grid stability.
• Inaccurate wind power prediction hinders effective power control and grid management.

Purpose of the Study:

• To develop an optimal control strategy for wind farm active power.
• To enhance the accuracy of wind power forecasting.
• To address challenges posed by prediction errors and confidence intervals in wind power generation.

Main Methods:

• Utilized historical data to analyze wind power prediction error distributions and confidence intervals.
• Developed an active power optimization model for wind farms incorporating confidence interval constraints.
• Implemented a strategy for active power distribution and complementary prediction error management among wind farms with asymmetric error distributions.

Main Results:

• The proposed optimal control strategy effectively manages active power in wind farms.
• The method demonstrates improved accuracy in wind power prediction.
• Simulation results validated the strategy's rationality and effectiveness using real-world data.

Conclusions:

• The developed optimal control strategy enhances wind farm active power management.
• Accurate wind power forecasting is critical for stable grid integration.
• The strategy offers a viable solution for mitigating challenges in wind energy utilization.