The distributed co-evolution model of cloud-edge-device distribution network structure combined with artificial intelligence under the new energy situation

The complex electricity consumption situation on the user demand side and the large-scale power generation of renewable energy have gradually shifted the mode of source following load in the power system to the mode of source and load interaction. At present, the voltage regulation methods all require a large amount of computing resources to accurately predict the fluctuating load in the face of the new energy structure. However, with the development of artificial intelligence and cloud computing, the processing of huge databases and the release of computing resources have become possible. This paper proposes a new method for user-end power analysis based on the combination of traditional mathematical statistics and machine learning methods to make up for the deficiencies of non-intrusive load detection methods and construct a distributed optimization of cloud-edge-device distribution networks based on user requirements. Aiming at problems such as overfitting and the demand for accurate short-term renewable power generation power prediction, it is proposed to use the long short-term memory method to extract data information, and combine the deep neural network to construct a coupling algorithm to obtain the output prediction of renewable energy under the collaboration of cloud-edge-device. The R2 value of the coupling algorithm reaches 0.991, while the values of RMSE, MAPE and MAE are 1347.2, 5.36 and 199.4 respectively. Predicted power prediction cannot completely eliminate errors. It is necessary to combine the consistency algorithm to construct the regulation strategy. Under the control of the regulation strategy, stability can be achieved after 25 iterations. The cost increase rate is 0.241 yuan /kWh, and the optimal regulation powers of each cluster are 6.42, 8.3, 3.21, 0.67, 0.43 and 0.58 kW respectively. Finally, the cloud-edge-device distributed coevolution model of the power grid is obtained to achieve the economy and security of power grid voltage control.