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• 1.1% of United States households could find it privately economic to grid defect. • Low load customers will find it easiest to defect from the grid. • Defection up to 7% is possible if customers accept slightly less reliable service. • Limiting inefficient load defection via fixed charges could lead to grid defection. The cost declines of solar and storage technologies have led to concerns about customers disconnecting from utility service and self-supplying electricity. Prior research addressing this issue focused on average electricity tariffs, solar profiles, and demand without considering detailed customer heterogeneity. This paper fills the gap by analyzing how electricity tariffs that shift cost recovery away from variable charges towards fixed charges influence a customer's decision to disconnect from utility service. A linear optimization model is developed to size an off-grid solar/storage system. Technology cost and reliability parameters of the optimization model are then adjusted to calculate a range of off-grid costs. Unique rate structure information for over 2000 utilities in the United States is then used to compare grid costs to off-grid costs. The results show limited ability for solar/storage systems to economically substitute for grid services. However, 1% of households might disconnect from utility service in a scenario that accounts for future off-grid costs and updated tariff designs. We find that 3% of households in the Southwest and California have private economics that favor defection in this scenario, rising to as much as 7% in Hawaii. Grid defection could increase from 1% to 7% of United States households in a slightly reduced reliability scenario. These results indicate that utilities and regulators seeking to limit rooftop solar adoption by lowering variable charges face a significant possibility that the corresponding increase in fixed charges could lead to inefficient grid defection. [ABSTRACT FROM AUTHOR]

• Temporal and spatial features are used for wind speed forecasting. • A novel probabilistic deep learning model is proposed for wind speed forecasting. • The proposed model shows the best performance compared with other models. • Temporal and spatial uncertainty of the wind speed are estimated based on the model. Reliable and accurate probabilistic forecasting of wind speed is of vital importance for the utilization of wind energy and operation of power systems. In this paper, a probabilistic spatiotemporal deep learning model for wind speed forecasting is proposed. The underlying wind turbines are embedded into a grid space, which fully expresses the spatiotemporal variation process of the airflow. Thus, advanced image recognition methods can be employed to solve the spatiotemporal wind speed forecasting problem. The proposed model is based on a spatial–temporal neural network (STNN) and variational Bayesian inference. The proposed STNN combines the convolutional GRU model and 3D Convolutional Neural Network and uses an encoding-forecasting structure to generate the spatiotemporal predictions. Variational Bayesian inference is employed to obtain the approximated posterior parameter distribution of the model and determine the probability of the prediction. The proposed model is applied in two real-world case studies in United States. The experimental results demonstrate that the proposed model significantly outperforms other models in both forecast skill and forecast reliability. The uncertainty estimation is also shown and it demonstrates that the proposed model is able to provide effective uncertainty estimation in both the time level and space level. [ABSTRACT FROM AUTHOR]