The current conventional source-load intelligent tracking algorithm of distribution network mainly realizes active power control by calculating the regulation amount of output power, which leads to poor tracking effect due to the lack of intra-day scheduling optimization of distribution network. In this regard, the fuzzy prediction-based distribution grid source-load intelligent tracking algorithm is proposed. The multi-scenario technology is used to model the power output of distribution network power devices and the power load, and build the tracking scenario model; the intra-day optimization model is built, and the MPC control method is combined to realize the control of the power output and load situation of the distribution network; finally, the power fluctuation index is introduced to characterize the source-load tracking situation. In the experiments, the power control performance of the proposed method is verified. The experimental results show that the maximum power fluctuation value is low when the proposed method is used for source-load tracking, and it has a better power control performance.
The traditional energy-saving and load matching strategies for distribution networks have the problem of low accuracy in predicting the capacity of power equipment. Therefore, a new intelligent energy load matching strategy is proposed, which uses deep learning algorithms and K-means clustering algorithms to process and standardize power data, extract data features, and construct a capacity prediction model for energy storage devices in distribution networks. By finetuning the model structure network, the load condition of the distribution device is predicted, and the dynamic matching of source and load is achieved. Experimental verification shows that the matching effect of this strategy is superior to traditional methods, with a significant reduction in unit output and a high source load matching rate. This method has good application prospects in improving the energy utilization efficiency and reliability of distribution networks.
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