As the basis for the static security of the power grid, power load forecasting directly affects the safety of grid operation, the rationality of grid planning, and the economy of supply–demand balance. However, various factors lead to drastic changes in short-term power consumption, making the data more complex and thus more difficult to forecast. In response to this problem, a new hybrid model based on variational mode decomposition and long short-term memory with seasonal factors elimination and error correction is proposed in this article. Comprehensive case studies on four real-world load datasets from Singapore and the United States are employed to demonstrate the effectiveness and practicality of the proposed hybrid model. The experimental results show that the prediction accuracy of the proposed model is significantly higher than that of the contrast models.

Smart Grid 2.0 is the energy Internet based on advanced metering infrastructure and distributed systems that require an instantaneous two-way flow of energy information. Edge computing benefits from its proximity to the servers and edge nodes of the smart grid distributed systems, which can provide efficient and low latency information transmission to the smart grid. With the massive number of Internet of Things being used, the amount of real-time power usage information generated by that represents a huge challenge for edge computing. To improve the efficiency of information transmission and processing in power systems, this article combines different deep learning algorithms with edge computing to analyze and process distributed renewable energy generation and consumer power data in smart microgrid. Experiments on two real-world datasets from China and Belgium show that the proposed framework can obtain satisfactory prediction accuracy compared to existing approaches.

Nowadays, a large number of civilian unmanned aerial vehicles (UAVs) are increasingly being used in many of our daily applications. However, there are many kinds of UAVs that need to enhance their endurance due to their limited resources. In order to make the UAVs operate efficiently, it is necessary to schedule UAVs with charging requirements. In this paper, renewable energy production and storage equipment on the basis of traditional charging stations is adopted to reduce the power purchase from the distribution network as much as possible. An online algorithm based on Lyapunov optimization is proposed to schedule the charging of UAVs and the energy management of the charging station. Meanwhile, contract theory is used to design the optimal charging strategy in the case of information asymmetry. Hence, local renewable energy can be utilized to the greatest extent, and electricity purchase costs can be reduced. Through the incentive system, users can spontaneously charge at low peak times and avoid the risk of grid overload and high energy cost. The simulation results show that the algorithm can improve the efficiency of charging station operators, allowing users to avoid charging at peak times, and only use real-time information to schedule UAVs. Compared to other algorithms, the proposed scheme can bring good revenue to operators while ensuring long-lasting operations of charging stations.