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Energy storage has the characteristics of strong flexibility and fast response, which can effectively alleviate load fluctuations, voltage instability and other problems caused by new energy access. This paper proposes a doublelayer power distribution based on an improved manta ray foraging optimization algorithm. The network energy storage site selection and capacity strategy aims to minimize energy storage investment costs, daily voltage fluctuations and daily load fluctuations, establish a twolayer site selection and capacity model, and introduce elite reverse learning strategies and adaptive tumbling factor improvements. The manta ray foraging optimization algorithm solution model was used, and the proposed method was simulated and verified using the connected new energy IEEE33 node distribution network as an example. The results showed that the proposed site selection and capacity optimization scheme can significantly reduce system voltage and load fluctuations, effectively reducing system investment costs.
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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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