Optimization of Multi-Energy Grid Integration and Energy Storage in Low-Carbon Power Systems Based on the TCM-MBZOA Algorithm: A Case Study of Yunnan Province
With the rapid transition toward green and low-carbon energy systems, efficient scheduling of power systems is crucial for improving energy utilization and reducing carbon emissions. However, existing converter stations still rely heavily on single-source energy models, which limit the integration o...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11097322/ |
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| author | Yang Li Guoen Zhou Jiaqi Xue Junwei Yang Shi Yin |
| author_facet | Yang Li Guoen Zhou Jiaqi Xue Junwei Yang Shi Yin |
| author_sort | Yang Li |
| collection | DOAJ |
| description | With the rapid transition toward green and low-carbon energy systems, efficient scheduling of power systems is crucial for improving energy utilization and reducing carbon emissions. However, existing converter stations still rely heavily on single-source energy models, which limit the integration of renewables and lead to high carbon footprints. To address this limitation, this paper proposes a multi-source coordinated optimization strategy based on a bi-level programming model and an improved tent chaotic mapping-memory backtracking zebra optimization algorithm (TCM-MBZOA). The outer model maximizes the annual revenue of third-party energy storage operators, while the inner model minimizes operational costs, carbon emissions, and renewable energy curtailment within multiple virtual power plants. The TCM-MBZOA enhances the algorithm’s performance through Tent Chaotic Mapping for diverse initialization, a Memory-Backtracking Strategy for adaptive exploration, and an Adaptive T-Distribution for improved convergence. Applied to a real converter station in Yunnan Province, the proposed method achieves a 10.51% reduction in total energy consumption and significantly improves photovoltaic utilization and energy storage efficiency, outperforming benchmark algorithms. The results demonstrate the practical effectiveness and engineering feasibility of the proposed strategy in enabling low-carbon, high-efficiency operation of modern power systems. |
| format | Article |
| id | doaj-art-c474fbcf1f0c4ba786d342f264352e78 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c474fbcf1f0c4ba786d342f264352e782025-08-20T03:40:59ZengIEEEIEEE Access2169-35362025-01-011313566913568810.1109/ACCESS.2025.359297011097322Optimization of Multi-Energy Grid Integration and Energy Storage in Low-Carbon Power Systems Based on the TCM-MBZOA Algorithm: A Case Study of Yunnan ProvinceYang Li0Guoen Zhou1Jiaqi Xue2Junwei Yang3https://orcid.org/0009-0007-1953-3316Shi Yin4https://orcid.org/0000-0001-6634-1711Dali Bureau, China Southern Power Grid Ultra High Voltage Transmission Company, Dali, Yunnan, ChinaDali Bureau, China Southern Power Grid Ultra High Voltage Transmission Company, Dali, Yunnan, ChinaDali Bureau, China Southern Power Grid Ultra High Voltage Transmission Company, Dali, Yunnan, ChinaLongxin Science and Technology Group Company Ltd., Wuxi, Jiangsu, ChinaFaculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Kunming, Yunnan, ChinaWith the rapid transition toward green and low-carbon energy systems, efficient scheduling of power systems is crucial for improving energy utilization and reducing carbon emissions. However, existing converter stations still rely heavily on single-source energy models, which limit the integration of renewables and lead to high carbon footprints. To address this limitation, this paper proposes a multi-source coordinated optimization strategy based on a bi-level programming model and an improved tent chaotic mapping-memory backtracking zebra optimization algorithm (TCM-MBZOA). The outer model maximizes the annual revenue of third-party energy storage operators, while the inner model minimizes operational costs, carbon emissions, and renewable energy curtailment within multiple virtual power plants. The TCM-MBZOA enhances the algorithm’s performance through Tent Chaotic Mapping for diverse initialization, a Memory-Backtracking Strategy for adaptive exploration, and an Adaptive T-Distribution for improved convergence. Applied to a real converter station in Yunnan Province, the proposed method achieves a 10.51% reduction in total energy consumption and significantly improves photovoltaic utilization and energy storage efficiency, outperforming benchmark algorithms. The results demonstrate the practical effectiveness and engineering feasibility of the proposed strategy in enabling low-carbon, high-efficiency operation of modern power systems.https://ieeexplore.ieee.org/document/11097322/Energy optimization modellow-carbon power systemmulti-source cooperative schedulingZebra optimization algorithm |
| spellingShingle | Yang Li Guoen Zhou Jiaqi Xue Junwei Yang Shi Yin Optimization of Multi-Energy Grid Integration and Energy Storage in Low-Carbon Power Systems Based on the TCM-MBZOA Algorithm: A Case Study of Yunnan Province IEEE Access Energy optimization model low-carbon power system multi-source cooperative scheduling Zebra optimization algorithm |
| title | Optimization of Multi-Energy Grid Integration and Energy Storage in Low-Carbon Power Systems Based on the TCM-MBZOA Algorithm: A Case Study of Yunnan Province |
| title_full | Optimization of Multi-Energy Grid Integration and Energy Storage in Low-Carbon Power Systems Based on the TCM-MBZOA Algorithm: A Case Study of Yunnan Province |
| title_fullStr | Optimization of Multi-Energy Grid Integration and Energy Storage in Low-Carbon Power Systems Based on the TCM-MBZOA Algorithm: A Case Study of Yunnan Province |
| title_full_unstemmed | Optimization of Multi-Energy Grid Integration and Energy Storage in Low-Carbon Power Systems Based on the TCM-MBZOA Algorithm: A Case Study of Yunnan Province |
| title_short | Optimization of Multi-Energy Grid Integration and Energy Storage in Low-Carbon Power Systems Based on the TCM-MBZOA Algorithm: A Case Study of Yunnan Province |
| title_sort | optimization of multi energy grid integration and energy storage in low carbon power systems based on the tcm mbzoa algorithm a case study of yunnan province |
| topic | Energy optimization model low-carbon power system multi-source cooperative scheduling Zebra optimization algorithm |
| url | https://ieeexplore.ieee.org/document/11097322/ |
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