The complex electricity consumption situation on the customer side and large-scale wind and solar power generation have gradually shifted the traditional “source-follow-load” model in the power system towards the “source-load interaction” model. At present, the voltage regulation methods require excessive computing resources to accurately predict the fluctuating load under the new energy structure. However, with the development of artificial intelligence and cloud computing, more methods for processing big data have emerged. This paper proposes a new method for electricity consumption analysis that combines traditional mathematical statistics with machine learning to overcome the limitations of non-intrusive load detection methods and develop a distributed optimization of cloud–edge–device distribution networks based on electricity consumption. Aiming at problems such as overfitting and the demand for accurate short-term renewable power generation prediction, it is proposed to use the long short-term memory method to process time series data, and an improved algorithm is developed in combination with error feedback correction. 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. Power prediction cannot completely eliminate errors. It is necessary to combine the consistency algorithm to construct the regulation strategy. Under the regulation strategy, stability can be achieved after 25 iterations, and the optimal regulation is obtained. Finally, the cloud–edge–device distributed coevolution model of the power grid is obtained to achieve the economy of power grid voltage control.

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.