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In order to accurately predict the V2G charging and discharging load of electric vehicles, so as to regulate the peak to valley difference of power grid load and ensure power supply stability, this paper proposed a spatiotemporal distribution prediction method for V2G charging and discharging loads of electric vehicles based on collaborative optimization of supply and demand sides. A collaborative optimization objective model for both supply and demand sides was built, the Whale Optimization Algorithm was used for iterative solution to obtain the optimal charging and discharging load curve, and the optimal charging and discharging period was determined. The influencing indicators of charging and discharging loads within the optimal time periods in different spatial regions were collected, serving as inputs for constructing a prediction model based on multiple linear regression, thus achieving the prediction of spatial-temporal distribution of electric vehicle V2G charging and discharging loads. The experimental results show that the load prediction model obtained with the proposed method has a relatively large coefficient of determination, indicating that the prediction results of this research method are closer to the actual load, and have high prediction accuracy.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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