Impact and optimization of vehicle charging scheduling on regional clean energy power supply network management
Abstract Driven by the global energy transition, the widespread use of electric vehicles has profoundly reshaped the transportation landscape and thrown many problems to the power system, and coordinating their charging needs with renewable energy generation has become a key part of ensuring the sta...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-01-01
|
| Series: | Energy Informatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s42162-025-00476-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832571266581659648 |
|---|---|
| author | Penghui Xu Xiaobo Wang Zhichao Li |
| author_facet | Penghui Xu Xiaobo Wang Zhichao Li |
| author_sort | Penghui Xu |
| collection | DOAJ |
| description | Abstract Driven by the global energy transition, the widespread use of electric vehicles has profoundly reshaped the transportation landscape and thrown many problems to the power system, and coordinating their charging needs with renewable energy generation has become a key part of ensuring the stable operation of regional clean energy power supply networks. This study focuses on the problem of vehicle charging dispatch to make a breakthrough, deeply analyzes the effect and efficiency of the clean energy grid, and then proposes a series of targeted measures to effectively improve the operational efficiency and reliability of the energy system. The comprehensive model integrates electric vehicle charging stations, distributed photovoltaic power generation systems, wind farms, and battery energy storage devices and enables the charging process to be accurately controlled with real-time monitoring and intelligent algorithms. In particular, the demand forecasting model based on machine learning effectively solves the dilemma of matching the charging load with a clean energy supply. Experimental data strongly confirms that the optimization strategy has led to a 15% reduction in peak load on the grid, a 23% increase in the proportion of clean energy consumption, and a 10% reduction in total electricity consumption. For policymakers, these achievements can be used as a guide to help formulate energy policies and build a framework for adapting to the development of new energy. For practitioners, they serve as a guide to energy planning, grid dispatch, and technology research and development to improve effectiveness. The research promotes the growth of green energy, optimizes the energy structure, lays the foundation for a low-carbon and environmentally friendly society, affects the economy, environment, culture, and other fields, and becomes a key force driving sustainable development. |
| format | Article |
| id | doaj-art-c8e37cba9c6546e6b8ae13f17b931070 |
| institution | Kabale University |
| issn | 2520-8942 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Energy Informatics |
| spelling | doaj-art-c8e37cba9c6546e6b8ae13f17b9310702025-02-02T12:44:40ZengSpringerOpenEnergy Informatics2520-89422025-01-018111610.1186/s42162-025-00476-xImpact and optimization of vehicle charging scheduling on regional clean energy power supply network managementPenghui Xu0Xiaobo Wang1Zhichao Li2Xinxiang Vocational and Technical CollegeXinxiang Vocational and Technical CollegeHenan Institute of Economics and Trade TechniciansAbstract Driven by the global energy transition, the widespread use of electric vehicles has profoundly reshaped the transportation landscape and thrown many problems to the power system, and coordinating their charging needs with renewable energy generation has become a key part of ensuring the stable operation of regional clean energy power supply networks. This study focuses on the problem of vehicle charging dispatch to make a breakthrough, deeply analyzes the effect and efficiency of the clean energy grid, and then proposes a series of targeted measures to effectively improve the operational efficiency and reliability of the energy system. The comprehensive model integrates electric vehicle charging stations, distributed photovoltaic power generation systems, wind farms, and battery energy storage devices and enables the charging process to be accurately controlled with real-time monitoring and intelligent algorithms. In particular, the demand forecasting model based on machine learning effectively solves the dilemma of matching the charging load with a clean energy supply. Experimental data strongly confirms that the optimization strategy has led to a 15% reduction in peak load on the grid, a 23% increase in the proportion of clean energy consumption, and a 10% reduction in total electricity consumption. For policymakers, these achievements can be used as a guide to help formulate energy policies and build a framework for adapting to the development of new energy. For practitioners, they serve as a guide to energy planning, grid dispatch, and technology research and development to improve effectiveness. The research promotes the growth of green energy, optimizes the energy structure, lays the foundation for a low-carbon and environmentally friendly society, affects the economy, environment, culture, and other fields, and becomes a key force driving sustainable development.https://doi.org/10.1186/s42162-025-00476-xVehicle charging schedulingClean energyPower supply networkSystem optimization |
| spellingShingle | Penghui Xu Xiaobo Wang Zhichao Li Impact and optimization of vehicle charging scheduling on regional clean energy power supply network management Energy Informatics Vehicle charging scheduling Clean energy Power supply network System optimization |
| title | Impact and optimization of vehicle charging scheduling on regional clean energy power supply network management |
| title_full | Impact and optimization of vehicle charging scheduling on regional clean energy power supply network management |
| title_fullStr | Impact and optimization of vehicle charging scheduling on regional clean energy power supply network management |
| title_full_unstemmed | Impact and optimization of vehicle charging scheduling on regional clean energy power supply network management |
| title_short | Impact and optimization of vehicle charging scheduling on regional clean energy power supply network management |
| title_sort | impact and optimization of vehicle charging scheduling on regional clean energy power supply network management |
| topic | Vehicle charging scheduling Clean energy Power supply network System optimization |
| url | https://doi.org/10.1186/s42162-025-00476-x |
| work_keys_str_mv | AT penghuixu impactandoptimizationofvehiclechargingschedulingonregionalcleanenergypowersupplynetworkmanagement AT xiaobowang impactandoptimizationofvehiclechargingschedulingonregionalcleanenergypowersupplynetworkmanagement AT zhichaoli impactandoptimizationofvehiclechargingschedulingonregionalcleanenergypowersupplynetworkmanagement |