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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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2025.1596471/full |
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| author | Yanna Gao Hong Dong Liujun Hu Fanhong Zeng Yuqun Gao Zhuonan Huang Shaohua Wang Shaohua Wang |
| author_facet | Yanna Gao Hong Dong Liujun Hu Fanhong Zeng Yuqun Gao Zhuonan Huang Shaohua Wang Shaohua Wang |
| author_sort | Yanna Gao |
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| description | 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 wind power station (OWPS) siting, aiming to improve resource utilization, expand development in promising regions, and balance demand coverage in spatial planning.MethodsA MO-MCLP model is developed that simultaneously maximizes wind energy utilization, promotes development in suitable areas, and balances electricity demand coverage. To solve this model at large scale, a deep reinforcement learning (DRL) algorithm is designed and implemented. The DRL approach is benchmarked against a traditional optimization implementation using the Gurobi solver. Computational experiments focus on wind-rich coastal regions of Guangdong Province, evaluating both solution quality (coverage and utilization metrics) and computational efficiency under varying problem sizes.ResultsThe DRL algorithm achieves objective values comparable to or better than those from the Gurobi-based method, while substantially reducing computation time for large problem instances. As the number of candidate sites and demand points increases, DRL demonstrates superior scalability. In the Guangdong case study, DRL attains similar or improved coverage and utilization within a fraction of the runtime required by Gurobi, enabling faster iteration for scenario analysis.DiscussionThe findings indicate that DRL offers an efficient alternative to traditional solvers for complex spatial optimization in wind farm siting. Faster computation and better scalability facilitate exploration of multiple planning scenarios, sensitivity analyses, and rapid decision support under practical time constraints. Integrating richer environmental and socioeconomic data, extending to multi-stage planning, or combining DRL with heuristic solvers may further enhance performance. Overall, the MO-MCLP model with DRL solution provides actionable insights for sustainable energy infrastructure planning by delivering high-quality site allocations efficiently. |
| format | Article |
| id | doaj-art-2d724845d17f4978bcf14f54089a7f43 |
| institution | Kabale University |
| issn | 2296-598X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Energy Research |
| spelling | doaj-art-2d724845d17f4978bcf14f54089a7f432025-08-20T03:29:57ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2025-07-011310.3389/fenrg.2025.15964711596471Deep reinforcement learning for multi-objective location optimization of onshore wind power stations: a case study of Guangdong Province, ChinaYanna Gao0Hong Dong1Liujun Hu2Fanhong Zeng3Yuqun Gao4Zhuonan Huang5Shaohua Wang6Shaohua Wang7Guangzhou Power Supply Bureau, Guangdong Power Grid Co. Ltd., Guangzhou, ChinaGuangzhou Power Supply Bureau, Guangdong Power Grid Co. Ltd., Guangzhou, ChinaGuangzhou Power Supply Bureau, Guangdong Power Grid Co. Ltd., Guangzhou, ChinaGuangzhou Power Supply Bureau, Guangdong Power Grid Co. Ltd., Guangzhou, ChinaGuangzhou Power Supply Bureau, Guangdong Power Grid Co. Ltd., Guangzhou, ChinaSchool of Information Engineering, China University of Geosciences (Beijing), Beijing, ChinaState Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaUniversity of Chinese Academy of Sciences, Beijing, ChinaIntroductionWind 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 wind power station (OWPS) siting, aiming to improve resource utilization, expand development in promising regions, and balance demand coverage in spatial planning.MethodsA MO-MCLP model is developed that simultaneously maximizes wind energy utilization, promotes development in suitable areas, and balances electricity demand coverage. To solve this model at large scale, a deep reinforcement learning (DRL) algorithm is designed and implemented. The DRL approach is benchmarked against a traditional optimization implementation using the Gurobi solver. Computational experiments focus on wind-rich coastal regions of Guangdong Province, evaluating both solution quality (coverage and utilization metrics) and computational efficiency under varying problem sizes.ResultsThe DRL algorithm achieves objective values comparable to or better than those from the Gurobi-based method, while substantially reducing computation time for large problem instances. As the number of candidate sites and demand points increases, DRL demonstrates superior scalability. In the Guangdong case study, DRL attains similar or improved coverage and utilization within a fraction of the runtime required by Gurobi, enabling faster iteration for scenario analysis.DiscussionThe findings indicate that DRL offers an efficient alternative to traditional solvers for complex spatial optimization in wind farm siting. Faster computation and better scalability facilitate exploration of multiple planning scenarios, sensitivity analyses, and rapid decision support under practical time constraints. Integrating richer environmental and socioeconomic data, extending to multi-stage planning, or combining DRL with heuristic solvers may further enhance performance. Overall, the MO-MCLP model with DRL solution provides actionable insights for sustainable energy infrastructure planning by delivering high-quality site allocations efficiently.https://www.frontiersin.org/articles/10.3389/fenrg.2025.1596471/fullonshore wind power stationspatial analysislocation problemdeep reinforcement learningmulti-objective optimization |
| spellingShingle | Yanna Gao Hong Dong Liujun Hu Fanhong Zeng Yuqun Gao Zhuonan Huang Shaohua Wang Shaohua Wang Deep reinforcement learning for multi-objective location optimization of onshore wind power stations: a case study of Guangdong Province, China Frontiers in Energy Research onshore wind power station spatial analysis location problem deep reinforcement learning multi-objective optimization |
| title | Deep reinforcement learning for multi-objective location optimization of onshore wind power stations: a case study of Guangdong Province, China |
| title_full | Deep reinforcement learning for multi-objective location optimization of onshore wind power stations: a case study of Guangdong Province, China |
| title_fullStr | Deep reinforcement learning for multi-objective location optimization of onshore wind power stations: a case study of Guangdong Province, China |
| title_full_unstemmed | Deep reinforcement learning for multi-objective location optimization of onshore wind power stations: a case study of Guangdong Province, China |
| title_short | Deep reinforcement learning for multi-objective location optimization of onshore wind power stations: a case study of Guangdong Province, China |
| title_sort | deep reinforcement learning for multi objective location optimization of onshore wind power stations a case study of guangdong province china |
| topic | onshore wind power station spatial analysis location problem deep reinforcement learning multi-objective optimization |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2025.1596471/full |
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