Discrepancies between estimated and actual wind power generation in the U.S. and China

A Harvard-China Project Research Seminar with Dr. Haiyang Lin, Postdoctoral Fellow, Harvard-China Project

Abstract: The urgency of addressing climate change is evident, and wind energy plays a vital role in global strategies to reduce carbon emissions and transition toward a sustainable energy future. Accurate assessments of wind resources are crucial for this transition. However, current wind energy development and research heavily rely on meteorological datasets, which, despite their widespread use, exhibit significant discrepancies both internally and in comparison to actual wind power generation. These discrepancies, though not widely recognized, can lead to ineffective decision-making, resulting in substantial economic and energy losses. This talk will examine these issues by comparing estimates from multiple datasets to real-world wind power generation across 1,276 wind farm sites in the United States and 10,032 sites in China.

In the U.S., our analysis reveals significant regional discrepancies, particularly in coastal areas where actual generation far exceeds estimates. For example, in some regions, models project only one-third of the actual generation observed. While incorporating factors such as air density and wake loss into assessments could reduce these gaps, it may introduce additional biases at the plant level, complicating the accuracy of future predictions. Wind farm attributes, such as the operation year, show strong correlations with estimation accuracy, emphasizing the decline in turbine performance with age. Moreover, comparisons among models reveal that capacity factors amplify wind speed differences by 2-3 times, highlighting the need to leverage accessible wind generation data to enhance meteorological products and improve predictive accuracy.

In China, the discrepancy between theoretical and real-world outcomes is equally significant, with regions like Inner Mongolia showing an estimated capacity factor (CF) of 35%-60%, yet only achieving an actual CF of 25.9% in 2023—surprisingly lower than Yunnan's 32.4%. Such large gaps can lead to misguided planning and ineffective strategies for wind power expansion. China’s main issue lies in the underperformance of wind farms, with much of the wind energy potential remaining untapped. Integration of energy storage across different time scales, and adapting the grid to serve emerging loads like hydrogen production, AI computing, and electric vehicles can help to address this issue and accelerate China’s path to carbon neutrality.

Speaker Bio: Dr. Haiyang Lin is a Postdoctoral Fellow with the Harvard-China Project at Harvard University. He received his Ph.D. in Power Engineering and Thermophysics from Shandong University in 2021. In 2018, he was a visiting student at the Future Energy Center, Mälardalen University in Sweden, and has been a Visiting Fellow with the Harvard-China Project since 2019.

Dr. Lin’s research at Harvard focuses on the dynamic optimization of green fuel supply chains to integrate renewable energy and decarbonize hard-to-abate sectors. By utilizing high-resolution meteorological and geographical data, advanced climate models, and cross-sectoral energy system models, he aims to develop sustainable energy solutions for the US, China, India, and beyond. His expertise spans low-carbon energy system modeling, energy supply network optimization, energy consumption and demand response simulation, and coordinated multi-energy demand-supply matching.