Development of wind power in China, a key measure to mitigate China's carbon emissions and achieve global climate goals, faces serious curtailment issues. In an interlinked electric power system, a strategic allocation of wind power capacities offers an important means to take advantage of geographical smoothing effects and enhance accommodation of wind power into the electric power system. Applying K-means clustering algorithm with assimilated meteorological data, we differentiate seven wind zones in the Chinese mainland. The zonal variation features of wind power are characterized and associated with influencing weather systems and geographical conditions. A multi-objective optimization model is developed to identify the optimal allocation of wind power capacity across the seven zones in 2030, which demonstrates further improvement of wind power quality from the current distribution case in terms of high power outputs (+0.2%), low short-term variation (−10.3%) and high firm capacity (+3.7%). Northern China, where existing large-scale wind power bases are located, and southeastern regions are identified as key areas for future deployment of wind power. These findings are expected to offer a new perspective for decision makers in the construction of power grid systems and spatial development strategy for wind power in China.
Bibliographical noteFunding Information:
This work was supported by the National Key R&D Program of China (2016YFC0208901), the National Natural Science Foundation of China (No. 72025401, 71722003, 71974108, 71690244, and 62073300), and Tsinghua University-INDITEX Sustainable Development Fund.
© 2021 Elsevier Ltd
Copyright 2021 Elsevier B.V., All rights reserved.
- Cluster analysis
- Geographical smoothing effect
- Multi-objective optimization
- Wind energy
ASJC Scopus subject areas
- Building and Construction
- Mechanical Engineering
- Management, Monitoring, Policy and Law