Deep reinforcement learning for multi-objective location optimization of onshore wind power stations: a case study of Guangdong Province, China

Deep reinforcement learning for multi-objective location optimization of onshore wind power stations: a case study of Guangdong Province, China

IntroductionWind energy development faces challenges such as low utilization of wind resources, underdevelopment of suitable areas, and imbalanced electricity demand coverage. To address these issues, this study formulates a multi-objective maximal covering location problem (MO-MCLP) for onshore win...

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Bibliographic Details
Main Authors: Yanna Gao, Hong Dong, Liujun Hu, Fanhong Zeng, Yuqun Gao, Zhuonan Huang, Shaohua Wang
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-07-01
Series:Frontiers in Energy Research
Subjects:
onshore wind power station
spatial analysis
location problem
deep reinforcement learning
multi-objective optimization
Online Access:https://www.frontiersin.org/articles/10.3389/fenrg.2025.1596471/full
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