Optimization algorithm of power system line loss management using big data analytics
Abstract As global energy demand continues to rise and renewable energy sources develop rapidly, the operational efficiency and stability of power systems have emerged as primary challenges in energy management. Line loss within these systems is a critical factor for both energy efficiency and econo...
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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/s42162-024-00434-z |
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| author | Yang Li Danhong Zhang Ming Tang |
| author_facet | Yang Li Danhong Zhang Ming Tang |
| author_sort | Yang Li |
| collection | DOAJ |
| description | Abstract As global energy demand continues to rise and renewable energy sources develop rapidly, the operational efficiency and stability of power systems have emerged as primary challenges in energy management. Line loss within these systems is a critical factor for both energy efficiency and economic performance. This study leverages an electric energy data management platform that facilitates the sharing of archival information, the development of line loss calculation models, and the automated computation of electricity and line loss formulas. This ensures accurate and real-time calculations of line losses in the power grid, supporting multi-time scale analyses and providing timely, comprehensive data for effective line loss management. The platform utilizes theoretical line loss values to identify anomalies, which are categorized into five types: topological relationships, archival information, data collection, electricity metering, and consumption behavior. In response to the abnormal monthly power imbalance rate of 220 kV and 110 KV stations, and the − 3.684% exceeding the − 1% assessment limit, the designed line loss management system service layer does not need to go deep into the bottom layer of the power system. It hides the complexity of the power grid through middleware and provides data, application, and security services. |
| format | Article |
| id | doaj-art-3297bbb731b649e9829fa9498c03ae60 |
| institution | OA Journals |
| issn | 2520-8942 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Energy Informatics |
| spelling | doaj-art-3297bbb731b649e9829fa9498c03ae602025-08-20T02:20:45ZengSpringerOpenEnergy Informatics2520-89422024-12-017111710.1186/s42162-024-00434-zOptimization algorithm of power system line loss management using big data analyticsYang Li0Danhong Zhang1Ming Tang2Strategic Planning Department, Guangdong Power Grid Co., LtdStrategic Planning Department, Guangdong Power Grid Co., LtdStrategic Planning Department, Guangdong Power Grid Co., LtdAbstract As global energy demand continues to rise and renewable energy sources develop rapidly, the operational efficiency and stability of power systems have emerged as primary challenges in energy management. Line loss within these systems is a critical factor for both energy efficiency and economic performance. This study leverages an electric energy data management platform that facilitates the sharing of archival information, the development of line loss calculation models, and the automated computation of electricity and line loss formulas. This ensures accurate and real-time calculations of line losses in the power grid, supporting multi-time scale analyses and providing timely, comprehensive data for effective line loss management. The platform utilizes theoretical line loss values to identify anomalies, which are categorized into five types: topological relationships, archival information, data collection, electricity metering, and consumption behavior. In response to the abnormal monthly power imbalance rate of 220 kV and 110 KV stations, and the − 3.684% exceeding the − 1% assessment limit, the designed line loss management system service layer does not need to go deep into the bottom layer of the power system. It hides the complexity of the power grid through middleware and provides data, application, and security services.https://doi.org/10.1186/s42162-024-00434-zLine loss managementLine loss calculationElectrical energy data managementOptimization algorithm |
| spellingShingle | Yang Li Danhong Zhang Ming Tang Optimization algorithm of power system line loss management using big data analytics Energy Informatics Line loss management Line loss calculation Electrical energy data management Optimization algorithm |
| title | Optimization algorithm of power system line loss management using big data analytics |
| title_full | Optimization algorithm of power system line loss management using big data analytics |
| title_fullStr | Optimization algorithm of power system line loss management using big data analytics |
| title_full_unstemmed | Optimization algorithm of power system line loss management using big data analytics |
| title_short | Optimization algorithm of power system line loss management using big data analytics |
| title_sort | optimization algorithm of power system line loss management using big data analytics |
| topic | Line loss management Line loss calculation Electrical energy data management Optimization algorithm |
| url | https://doi.org/10.1186/s42162-024-00434-z |
| work_keys_str_mv | AT yangli optimizationalgorithmofpowersystemlinelossmanagementusingbigdataanalytics AT danhongzhang optimizationalgorithmofpowersystemlinelossmanagementusingbigdataanalytics AT mingtang optimizationalgorithmofpowersystemlinelossmanagementusingbigdataanalytics |