This paper establishes a short-term decision model, based on robust optimization, for an electricity retailer to determine the electricity procurement and electricity retail prices. The electricity procurement process includes purchasing electricity from generation companies and from the spot market. The selling prices of electricity for the customers are based on time-of-use (TOU) pricing which is widely employed in modern electricity market as a demand response program. The objective of the model is to maximize the expected profit of the retailer through optimizing the electricity procurement strategy and electricity pricing scheme. A price elasticity matrix (PEM) is adopted to model the demand response. Also, uncertainty in spot prices is modeled using a robust optimization approach, in which price bounds are considered instead of predicted values. Using a robust optimization approach, the retailer can adjust the level of robustness of its decisions through a robust control parameter. A case study is presented to illustrate the performance of the model. The simulation results demonstrate that the developed model is effective in increasing the expected profit of the retailer and flattening the load profiles of customers.

Taking into account the orientation and distance characteristics of wind turbine stations in wind farms can improve the accuracy of wind power prediction. This paper proposed a deep learning spatio-temporal prediction method named orthogonal wind direction transformation spatio-temporal gradient Regression Activation Mapping (OWT-STGrad-RAM) for wind speed prediction. The model encodes the wind farm using an image, and each wind turbine is encoded as a point in the image. The spatio-temporal data related to wind turbines, such as wind speed, temperature, and air pressure, are integrated into fusion features through spatio-temporal fusion convolutional networks model for pre training to obtain a feature dataset. OWT is used to eliminate the effects of different prevailing winds, and STGrad-RAM is used to characterize the orientation and distance between wind turbine nodes and make the spatial features interpretable. The feature dataset is used for wind speed prediction. The experimental results show that the proposed method has achieved a significant improvement in wind speed prediction accuracy compared to the comparative models.

To maintain power system stability, accurate wind speed prediction is essential. Taking into account the temporal and spatial characteristics of wind speed in an integrated manner can improve the accuracy of wind speed prediction. Considering complex nonlinear spatial factors such as wake effects in wind farms, a deep residual network is valuable in predicting wind speed with a high degree of accuracy. Wind speed data are typically a time series that requires feature extraction and attribute modeling, while maintaining signal integrity. In order to measure the importance of different temporal attributes and effectively aggregate temporal and spatial features, we used a parameter fusion matrix. We introduce a deep spatial-temporal residual network (DST-ResNet) for wind speed prediction that extracts the spatial-temporal characteristics, which can forecast the future wind speed of a multi-site wind farm in a particular region. In this model, wind speed data's nearby property and periodic property are separately modeled using a residual network. The outputs of the two temporal components are dynamically aggregated using a parameter fusion matrix and then fused with additional meteorological features to achieve wind speed prediction. Based on wind data from the National Renewable Energy Laboratory, our experiments show that the proposed DST-ResNet improves prediction accuracy by 8.90%.