A Capacity Allocation Method for Long-Endurance Hydrogen-Powered Hybrid UAVs Based on Two-Stage Optimization
Due to the challenges associated with the application of existing two-stage optimization methods in energy system capacity configuration, such as uncertainty scenario generation, multi-timescale coupling, and balancing economic and environmental benefits, this paper proposes a two-stage optimization...
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| Format: | Article |
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MDPI AG
2025-04-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/18/4/196 |
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| author | Haitao Li Chenyu Wang Shufu Yuan Hui Zhu Li Sun |
| author_facet | Haitao Li Chenyu Wang Shufu Yuan Hui Zhu Li Sun |
| author_sort | Haitao Li |
| collection | DOAJ |
| description | Due to the challenges associated with the application of existing two-stage optimization methods in energy system capacity configuration, such as uncertainty scenario generation, multi-timescale coupling, and balancing economic and environmental benefits, this paper proposes a two-stage optimization configuration method based on Particle Swarm Optimization (PSO) for the capacity configuration of long-endurance hydrogen-powered hybrid unmanned aerial vehicles (UAVs). By constructing a hydrogen-powered hybrid UAV energy system model, an uncertainty model for the energy system, and multi-timescale comprehensive evaluation indicators and corresponding objective functions, the capacity configuration is determined using a two-stage stochastic programming model solved by CPLEX in MATLAB. The two-stage stochastic programming model consists of the first stage, which involves capacity optimization through PSO, and the second stage, which employs Monte Carlo method for random wind field sampling. The research provides a theoretical foundation for the application of the two-stage optimization capacity configuration method in the field of long-endurance hydrogen-powered hybrid UAVs. |
| format | Article |
| id | doaj-art-b869cded51ef41f28b8a98d034ca504b |
| institution | OA Journals |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-b869cded51ef41f28b8a98d034ca504b2025-08-20T02:24:43ZengMDPI AGAlgorithms1999-48932025-04-0118419610.3390/a18040196A Capacity Allocation Method for Long-Endurance Hydrogen-Powered Hybrid UAVs Based on Two-Stage OptimizationHaitao Li0Chenyu Wang1Shufu Yuan2Hui Zhu3Li Sun4State Grid Changzhou Power Supply Company, Changzhou 213200, ChinaNational Engineering Research Center of Power Generation Control and Safety, Liyang Research Institute, Southeast University, Liyang 213300, ChinaNational Engineering Research Center of Power Generation Control and Safety, Liyang Research Institute, Southeast University, Liyang 213300, ChinaState Grid Changzhou Power Supply Company, Changzhou 213200, ChinaNational Engineering Research Center of Power Generation Control and Safety, Liyang Research Institute, Southeast University, Liyang 213300, ChinaDue to the challenges associated with the application of existing two-stage optimization methods in energy system capacity configuration, such as uncertainty scenario generation, multi-timescale coupling, and balancing economic and environmental benefits, this paper proposes a two-stage optimization configuration method based on Particle Swarm Optimization (PSO) for the capacity configuration of long-endurance hydrogen-powered hybrid unmanned aerial vehicles (UAVs). By constructing a hydrogen-powered hybrid UAV energy system model, an uncertainty model for the energy system, and multi-timescale comprehensive evaluation indicators and corresponding objective functions, the capacity configuration is determined using a two-stage stochastic programming model solved by CPLEX in MATLAB. The two-stage stochastic programming model consists of the first stage, which involves capacity optimization through PSO, and the second stage, which employs Monte Carlo method for random wind field sampling. The research provides a theoretical foundation for the application of the two-stage optimization capacity configuration method in the field of long-endurance hydrogen-powered hybrid UAVs.https://www.mdpi.com/1999-4893/18/4/196two-stage optimizationparticle swarm optimizationMonte Carlo methodcapacity configurationhybrid UAVs |
| spellingShingle | Haitao Li Chenyu Wang Shufu Yuan Hui Zhu Li Sun A Capacity Allocation Method for Long-Endurance Hydrogen-Powered Hybrid UAVs Based on Two-Stage Optimization Algorithms two-stage optimization particle swarm optimization Monte Carlo method capacity configuration hybrid UAVs |
| title | A Capacity Allocation Method for Long-Endurance Hydrogen-Powered Hybrid UAVs Based on Two-Stage Optimization |
| title_full | A Capacity Allocation Method for Long-Endurance Hydrogen-Powered Hybrid UAVs Based on Two-Stage Optimization |
| title_fullStr | A Capacity Allocation Method for Long-Endurance Hydrogen-Powered Hybrid UAVs Based on Two-Stage Optimization |
| title_full_unstemmed | A Capacity Allocation Method for Long-Endurance Hydrogen-Powered Hybrid UAVs Based on Two-Stage Optimization |
| title_short | A Capacity Allocation Method for Long-Endurance Hydrogen-Powered Hybrid UAVs Based on Two-Stage Optimization |
| title_sort | capacity allocation method for long endurance hydrogen powered hybrid uavs based on two stage optimization |
| topic | two-stage optimization particle swarm optimization Monte Carlo method capacity configuration hybrid UAVs |
| url | https://www.mdpi.com/1999-4893/18/4/196 |
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