Optimal Scheduling Strategy for Micro Energy Internet Under Electric Vehicles Aggregation

doi:10.11930/j.issn.1004-9649.202211034

Abstract

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

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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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202211034

https://www.electricpower.com.cn/EN/Y2023/V56/I5/80

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