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

• Published in: PloS one 2022-08, Vol.17 (8), p.e0273257-e0273257
• Main Authors: Kader, Mst Sharmin, Mahmudh, Riyadzh, Xiaoqing, Han, Niaz, Ashfaq, Shoukat, Muhammad Usman
• Format: Article
• Language: English
• Subjects: Accuracy; Active control; Alternative energy sources; Clean energy; Computer and Information Sciences; Confidence intervals; Earth Sciences; Electric power distribution; Electric power production; Energy resources; Energy sources; Engineering and Technology; Errors; Management; Neural networks; Optimal control; Optimization; Optimization models; Physical Sciences; Power control; Power plants; Predictions; R&D; Renewable energy; Research & development; Research and Analysis Methods; Skewed distributions; Wind farms; Wind power; Wind power generation
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0273257

One of the renewable energy resources, wind energy is widely used due to its wide distribution, large reserves, green and clean energy, and it is also an important part of large-scale grid integration. However, wind power has strong randomness, volatility, anti-peaking characteristics, and the problem of low wind power prediction accuracy, which brings serious challenges to the power system. Based on the difference of power prediction error and confidence interval between different new energy power stations, an optimal control strategy for active power of wind farms was proposed. Therefore, we focus on solving the problem of wind power forecasting and improving the accuracy of wind power prediction. Due to the prediction error of wind power generation, the power control cannot meet the control target. An optimal control strategy for active power of wind farms is proposed based on the difference in power prediction error and confidence interval between different new energy power stations. The strategy used historical data to evaluate the prediction error distribution and confidence interval of wind power. We use confidence interval constraints to create a wind power active optimization model that realize active power distribution and complementary prediction errors among wind farms with asymmetric error distribution. Combined with the actual data of a domestic (Cox’s Bazar, Bangladesh) wind power base, a simulation example is designed to verify the rationality and effectiveness of the proposed strategy.