电动汽车聚合下的微能源互联网优化调度策略

doi:10.11930/j.issn.1004-9649.202211034

中国电力 ›› 2023, Vol. 56 ›› Issue (5): 80-88.DOI: 10.11930/j.issn.1004-9649.202211034

• 双碳目标下的新型电力系统 • 上一篇下一篇

电动汽车聚合下的微能源互联网优化调度策略

安佳坤¹, 杨书强¹, 王涛¹, 贺春光¹, 张菁¹, 袁超², 窦春霞²

1. 国网河北省电力有限公司经济技术研究院, 河北石家庄 050011;
2. 南京邮电大学自动化学院人工智能学院, 江苏南京 210023

收稿日期:2022-11-11 修回日期:2023-01-28 出版日期:2023-05-28 发布日期:2023-05-27
作者简介:安佳坤(1988-),男,高级工程师,从事配电网规划与柔性负荷控制技术研究,E-mail:anjiakun_work@126.com;袁超(1997-),男,硕士研究生,从事人工智能在电网中的应用研究,E-mail:2521543656@qq.com;窦春霞(1968-),女,通信作者,教授,从事配电网规划与柔性负荷控制技术研究,E-mail:cxdou@ysu.edu.cn
基金资助:
国家自然科学基金资助项目（61933005）

Optimal Scheduling Strategy for Micro Energy Internet Under Electric Vehicles Aggregation

AN Jiakun¹, YANG Shuqiang¹, WANG Tao¹, HE Chunguang¹, ZHANG Jing¹, YUAN Chao², DOU Chunxia²

1. State Grid Hebei Electric Power Co., Ltd. Economic and Technological Research Institute, Shijiazhuang 050011, China;
2. College of Automation & College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

Received:2022-11-11 Revised:2023-01-28 Online:2023-05-28 Published:2023-05-27
Supported by:
This work is supported by National Natural Science Foundation of China (No.61933005).

摘要/Abstract

摘要： 随着可再生能源规模化接入电网，需配备更多的储能设备以减少峰谷差，这使得投入成本大大提高。电动汽车聚合的负荷特性类似储能，其主动参与能源互联网能量优化调度，将减少储能设备的成本，进而提高微能源互联网的经济效益。提出了电动汽车聚合下的微能源互联网优化调度策略。首先，基于AP（affinity propagation）数据挖掘技术的电动汽车负荷聚类分析，提出了基于极限学习机预测模型的电动汽车短期预测方法。其次，提出电动汽车聚合下的微能源互联网优化调度策略，利用电价激励电动汽车有序充电以减小负荷峰谷差进而降低系统发电成本。最后，仿真验证该优化调度策略的有效性。

关键词: 微能源互联网, 电动汽车聚合, 能量优化调度, AP数据挖掘技术, 极限学习机预测模型

Abstract: With the continuous access of renewable energy to the power grid, more energy storage equipment is required to reduce the peak-to-valley difference, which greatly increases the power generation cost. Since electric vehicles aggregation has similar load characteristics to that of energy storage, its active participation in energy optimization scheduling of energy Internet will reduce the cost of energy storage equipment, subsequently improving the economic benefits of micro energy Internet. To this end, a micro energy Internet optimization scheduling strategy under electric vehicles aggregation is proposed. Firstly, based on the electric vehicles load clustering analysis with affinity propagation (AP) data mining technology, a short-term prediction method for electric vehicles is proposed based on the extreme learning machine prediction model. Furthermore, an optimal scheduling strategy for micro energy Internet under electric vehicles aggregation is proposed. The electricity tariff is used to incentivize the orderly charging of electric vehicles to reduce the peak-to-valley load difference, which in turn reduces the system generation cost. Finally, simulations are conducted to verify the effectiveness of the proposed optimal dispatching strategy.

Key words: micro energy Internet, electric vehicles aggregation, energy optimal scheduling, AP data mining technology, extreme learning machine prediction model

安佳坤, 杨书强, 王涛, 贺春光, 张菁, 袁超, 窦春霞. 电动汽车聚合下的微能源互联网优化调度策略[J]. 中国电力, 2023, 56(5): 80-88.

AN Jiakun, YANG Shuqiang, WANG Tao, HE Chunguang, ZHANG Jing, YUAN Chao, DOU Chunxia. Optimal Scheduling Strategy for Micro Energy Internet Under Electric Vehicles Aggregation[J]. Electric Power, 2023, 56(5): 80-88.

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电动汽车聚合下的微能源互联网优化调度策略

Optimal Scheduling Strategy for Micro Energy Internet Under Electric Vehicles Aggregation

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