As the proportion of new energy power generation continues to increase, the stability of grid frequency is severely challenged, and the role of conventional thermal power units in grid frequency regulation has become increasingly prominent. However, the adjustment rate and accuracy of some thermal power units are difficult to meet the demand of grid load fluctuations. Therefore, a response performance optimization strategy for flywheel-thermal power system automatic generation control based on load forecasting was proposed. Firstly, the load is predicted, using the tree-based pipeline optimization tool TPOT library to automatically machine learning to match and train the load regression prediction model, and the automatic generation control day-ahead planned value is introduced into the training data to reduce the prediction error. Then, according to the load prediction value and the current flywheel system, with the optimization goal of minimizing the regulation rate of thermal power units, the flywheel energy storage system is acted firstly in load distribution, and the state of charge of the flywheel is adjusted meanwhile. Finally, a simulation experiment is carried out based on the actual operation data of a power plant in Hubei, and the experimental results prove that the proposed method can effectively improve the frequency modulation performance of thermal power units.
| 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 |