Integrated energy trading algorithm for source-grid-load-storage energy system based on distributed machine learning
Abstract The highly integrated source-grid-load-storage energy system has received increasing attention in energy transformation strategies. However, the current static network isomorphism algorithm for distributed machine learning cannot meet the energy exchange needs of the integrated energy syste...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-12-01
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| Series: | Energy Informatics |
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| Online Access: | https://doi.org/10.1186/s42162-024-00451-y |
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| _version_ | 1841559094696083456 |
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| author | Zhiwei Cui Changming Mo Qideng Luo Chunli Zhou |
| author_facet | Zhiwei Cui Changming Mo Qideng Luo Chunli Zhou |
| author_sort | Zhiwei Cui |
| collection | DOAJ |
| description | Abstract The highly integrated source-grid-load-storage energy system has received increasing attention in energy transformation strategies. However, the current static network isomorphism algorithm for distributed machine learning cannot meet the energy exchange needs of the integrated energy system. To better solve the energy loss problem caused by energy trading in the power system, prevent the clean energy loss, and ensure the stable operation of the power system, a distributed dynamic network heterogeneous algorithm is designed on the basis of distributed machine learning. The proposed method uses a dynamic network to balance communication load among servers while solving the hidden state vector errors that cannot be corrected timely due to static network isomorphism. Compared with other methods with a sensitivity of 25%, the sensitivity level of the improved algorithm was above 75%. When the accuracy of other algorithms was 50%, the improved algorithm was above 80%. In the application experiment, the temperature reached 50℃ with the increase of the power. The humidity value always remained above 20. Therefore, the proposed algorithm has superior performance and good application effects, providing new ideas for energy trading in source-grid-load-storage energy systems. |
| format | Article |
| id | doaj-art-c702daa204374d7190d0c2f393da7ce7 |
| institution | Kabale University |
| issn | 2520-8942 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Energy Informatics |
| spelling | doaj-art-c702daa204374d7190d0c2f393da7ce72025-01-05T12:48:03ZengSpringerOpenEnergy Informatics2520-89422024-12-017111610.1186/s42162-024-00451-yIntegrated energy trading algorithm for source-grid-load-storage energy system based on distributed machine learningZhiwei Cui0Changming Mo1Qideng Luo2Chunli Zhou3Guangxi Power Grid CoTianjin Tianda Qiushi Electric Power High Technology CO LtdGuangxi Power Grid CoGuangxi Power Grid CoAbstract The highly integrated source-grid-load-storage energy system has received increasing attention in energy transformation strategies. However, the current static network isomorphism algorithm for distributed machine learning cannot meet the energy exchange needs of the integrated energy system. To better solve the energy loss problem caused by energy trading in the power system, prevent the clean energy loss, and ensure the stable operation of the power system, a distributed dynamic network heterogeneous algorithm is designed on the basis of distributed machine learning. The proposed method uses a dynamic network to balance communication load among servers while solving the hidden state vector errors that cannot be corrected timely due to static network isomorphism. Compared with other methods with a sensitivity of 25%, the sensitivity level of the improved algorithm was above 75%. When the accuracy of other algorithms was 50%, the improved algorithm was above 80%. In the application experiment, the temperature reached 50℃ with the increase of the power. The humidity value always remained above 20. Therefore, the proposed algorithm has superior performance and good application effects, providing new ideas for energy trading in source-grid-load-storage energy systems.https://doi.org/10.1186/s42162-024-00451-yDistributed algorithmSource-grid-load-storage energy systemStatic network isomorphismDynamic network heterogeneityEnergy trading |
| spellingShingle | Zhiwei Cui Changming Mo Qideng Luo Chunli Zhou Integrated energy trading algorithm for source-grid-load-storage energy system based on distributed machine learning Energy Informatics Distributed algorithm Source-grid-load-storage energy system Static network isomorphism Dynamic network heterogeneity Energy trading |
| title | Integrated energy trading algorithm for source-grid-load-storage energy system based on distributed machine learning |
| title_full | Integrated energy trading algorithm for source-grid-load-storage energy system based on distributed machine learning |
| title_fullStr | Integrated energy trading algorithm for source-grid-load-storage energy system based on distributed machine learning |
| title_full_unstemmed | Integrated energy trading algorithm for source-grid-load-storage energy system based on distributed machine learning |
| title_short | Integrated energy trading algorithm for source-grid-load-storage energy system based on distributed machine learning |
| title_sort | integrated energy trading algorithm for source grid load storage energy system based on distributed machine learning |
| topic | Distributed algorithm Source-grid-load-storage energy system Static network isomorphism Dynamic network heterogeneity Energy trading |
| url | https://doi.org/10.1186/s42162-024-00451-y |
| work_keys_str_mv | AT zhiweicui integratedenergytradingalgorithmforsourcegridloadstorageenergysystembasedondistributedmachinelearning AT changmingmo integratedenergytradingalgorithmforsourcegridloadstorageenergysystembasedondistributedmachinelearning AT qidengluo integratedenergytradingalgorithmforsourcegridloadstorageenergysystembasedondistributedmachinelearning AT chunlizhou integratedenergytradingalgorithmforsourcegridloadstorageenergysystembasedondistributedmachinelearning |