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Outline
A two-layer optimization algorithm based on a microgrid load fluctuation suppression strategy is proposed, aiming to address the uncertainties in renewable energy generation and random load fluctuations. Through the modeling of operating states of generation devices and power allocation modes, the upper-layer model mainly considers the micro-resource output power constraints and related constraints on microgrid operations, so as to ensure the scheduling priorities of generation devices while reducing the operating cost of microgrid. The lower-layer model minimizes the equivalent load fluctuations in microgrid while using the values predicted by LSTM-Attention neural network to further constrain the output power of generation devices. The two-layer model is transformed into a single-layer model by applying the KKT condition, thus reducing the complexity in solving the model. Two different load scenarios are designed, and the two-layer optimization algorithm is used to perform simulations under the two scenarios, respectively. Simulation results show that the proposed algorithm can dynamically adjust the output power of generation devices and improve the consumption proportion of renewable energy. In addition, it can change the microgrid’s operating state according to different load scenarios and thus mitigate the impact of load fluctuations on microgrid operations.
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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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