Currently, the integration of distributed energy generators through virtual power plants in the Internet of Energy is a mainstream method. The complex structure of virtual power plants and the characteristics of distributed energy make it difficult to solve the economic dispatch problems of virtual power plants. In addition, the load of a virtual power plant is unstable and uncertain and thus requires a robust economic dispatch strategy. Because the selection of the set of uncertain conditions is conservative, the traditional robust economic dispatch strategies cannot effectively reduce the cost of virtual power plants. In addition, the traditional methods for solving robust strategies cannot directly solve nonlinear and nonconvex problems. In this paper, we propose a scenario-based robust economic dispatch strategy for virtual power plants, aiming to reduce the operational costs of virtual power plants. First, to reduce the conservatism of the strategy, scenario-based data augmentation is adopted for data generation. Through a generative adversarial network, a large amount of scene data is generated to extend the set of uncertain conditions. The scene data cannot only reduce the conservatism but also can be used in the determination of robust strategies. Second, deep reinforcement learning is adopted for historical data training, directly solving nonlinear and nonconvex problems to obtain a robust economic dispatch strategy. As experiments show, with the accurate generation of scene data, the proposed economic dispatch strategy is robust and effectively reduces the cost of virtual power plants.

Transient stability assessment (TSA) plays an important role to ensure the safe operation of the power system in Internet of Energy (IoE). Many time-domain simulation (TDS)-based and transient energy function (TEF)-based methods have been proposed to assess the transient stability of the power system. With the wide area measurement system (WAMS) and the phasor measure units (PMUs) applied to observe the real-time data, methods of TSA based on the machine learning and data-driven are continuously studied. These kinds of methods can only assess the transient stability of the power system when subjected to large disturbances. However, these kinds of methods cannot infer the type of event which leads to the collapse of the power system. In this article, the gated graph neural network (GGNN) is applied to assess the power system transient stability and infer the type of event leading the instability of the power system. First, conditional generative adversarial network (CGAN) is applied to generate unstable samples making the training data more balanced. With the balanced data graph-structured and used to train the GGNN-based TSA model, the GGNN-based TSA model achieves better performances. Finally, the real-time data is input into the trained TSA model and the transient stability of the power system is assessed. When the power system is considered unstable, the proposed TSA model can also infer the type of event leading the instability of the power system, classifying the unstable state to the corresponding event. Simulations performed on the New England 39-bus system verify the effectiveness of the proposed method.

With the development of the Internet of energy, more and more participants share data by different types of edge devices. However, such multi-source heterogenous data typically contain low-quality data, e.g., missing values, which may result in potential risks. Besides, resource-constrained devices incur large latency in edge computing networks. To alleviate such latency, distributed task offloading schemes are designed to share the computation burden between edge nodes and nearby servers. However, there are three main drawbacks of such schemes. First, low-quality data are not carefully evaluated by constraints under scenarios, which may result in slow convergence in distributed computation. Second, multi-source data including sensitive information are computed and shared among edge nodes without privacy protection. Third, distributed tasks on low-quality data may result in low-quality results even with an optimal offloading scheme. To address the problems above, a task offloading framework for edge computing based on consortium blockchain and distributed reinforcement learning is proposed in this paper, which can provide high-quality task offloading policies with data privacy protected. This framework consists of three key components: data quality evaluation (DQ) with multiple data quality dimensions, data repairing (DR) with a repairing algorithm based on a novel repairing consensus mechanism and distributed reinforcement learning for task arrangement (DELTA) with a distributed reinforcement learning algorithm based on a novel low-quality data distributing strategy. Numeric results are presented to illustrate the effectiveness and efficiency of the proposed task offloading framework for edge computing on low-quality data in the IoE.

Nowadays, microgrids (MG) have attracted much attention, as a key technology of the internet of energy (IoE). A great deal of research have shown that the hierarchical microgrid is a more novel structure of IoE. Although the hierarchical microgrid model solves the problem of weak power scheduling capability across microgrids, it suffers from severe communications uncertainty, which can lead to communication delay and fluctuation. To obtain the accurate result of the renewable energy accommodation assessment capacity, a hierarchical microgrid model considering communication uncertainty is proposed in this paper. The solution to solve the problem of the assessment renewable energy accommodation capacity for hierarchical microgrids is a hybrid control based on distribution deep reinforcement learning. The temporal difference generation adversarial network (TD-GAN) is proposed as a value based method. Compared with the policy based method, it can better solve the distributed problem in hybrid control with a generation adversarial network (GAN). Moreover, the challenge that the method cannot handle a continuous action space is solved by using a normalized advantage function (NAF). The method similar with the temporal difference (TD) error method is employed to train the GAN network. Simulation results using real power grid data demonstrate the effectiveness and accuracy of the proposed method.

In passive radar, the waveform is not controlled by the system, so strong echoes usually produce high sidelobes in the correlation function. Since the sidelobes can mask the weak targets, strong echo cancellation methods are required. The generalized adaptive notch filter (GANF) is an efficient method compared with the extensive cancellation algorithm. However, the GANF estimates different frequencies separately (in parallel or series), and a point-by-point iterative operation is adopted, which leads to heavy computational burden. This letter presents a multifrequency estimation notch filter which is a simplified GANF based on the signal model. Furthermore, an adaptive block notch filter is proposed to reduce the processing time. The efficiency of the block notch filter is verified by simulations.