To enhance the accuracy of short-term photovoltaic power output prediction and address issues such as insufficient spatial resolution of meteorological forecast data and weak generalization ability of models, this paper proposes a prediction method that integrates spatial downscaling meteorological data with a convolutional neural network (CNN)-iTransformer-long short-term memory (LSTM) model. First, the rime-optimized random forest regression algorithm (RIME-RF) is employed to perform spatial downscaling on numerical weather prediction (NWP) data, thereby improving its local applicability. Second, a CNN-iTransformer-LSTM hybrid prediction model is constructed. This model utilizes a CNN as a spatial feature extractor to capture local patterns in meteorological data, employs an iTransformer to model the global dependencies among multiple variables, and leverages an LSTM to enhance the learning of short-term temporal dynamic features, thereby achieving efficient collaborative mining of multi-scale features. Finally, experiments are conducted using actual data from a photovoltaic power station in Hebei, China, during various seasons and weather conditions. The results show that the proposed model outperforms the comparison models in terms of the root mean square error (RMSE), mean absoluteerror (MAE), and R², maintaining high prediction accuracy and stability even under complex weather conditions such as overcast and rainy days. The downscaling process further enhances the prediction performance, verifying the effectiveness and practicality of this method.

Power flow adjustment is a sequential decision problem. The operator makes decisions to ensure that the power flow meets the system's operational constraints, thereby obtaining a typical operating mode power flow. However, this decision-making method relies heavily on human experience, which is inefficient when the system is complex. In addition, the results given by the current evaluation system are difficult to directly guide the intelligent power flow adjustment. In order to improve the efficiency and intelligence of power flow adjustment, this paper proposes a power flow adjustment method based on deep reinforcement learning. Combining deep reinforcement learning theory with traditional power system operation mode analysis, the concept of region mapping is proposed to describe the adjustment process, so as to analyze the process of power flow calculation and manual adjustment. Considering the characteristics of power flow adjustment, a Markov decision process model suitable for power flow adjustment is constructed. On this basis, a double Q network learning method suitable for power flow adjustment is proposed. This method can adjust the power flow according to the set adjustment route, thus improving the intelligent level of power flow adjustment. The method in this paper is tested on China Electric Power Research Institute (CEPRI) test system.

In the background of the large-scale development and utilization of renewable energy, the joint operation of a variety of heterogeneous energy sources has become an inevitable development trend. However, the physical characteristics of different power sources and the inherent uncertainties of renewable energy power generation have brought difficulties to the planning, operation and control of power systems. For now, the utilization of multi-energy complementarity to promote energy transformation and improve the consumption of renewable energy has become a common understanding among researchers and the engineering community. This paper makes a review of the research on complementarity of new energy high proportion multi-energy systems from uncertainty modeling, complementary characteristics, planning and operation. We summarize the characteristics of the existing research and provide a reference for the further work.