Modern energy systems are experiencing the transition towards renewable-powered ones. Some conventional thermal units based on synchronous machines are gradually decommissioned and replaced by power electronics interfaced renewables. Thus, the lack of natural inertia and governor damping, which are the features of synchronous machines, raises significant concern about system frequency stability, including the faster rate of change and lower nadir point of frequency. Meanwhile, with the rapid development of communication and Internet of Things technologies, distributed energy resources can be aggregated as a virtual power plant to help balance real-time electricity demand and supply. However, the capability of utilizing the whole virtual power plant to provide adjustable inertia support has not been explored yet. In this paper, we propose a framework of the synchronous virtual power plant based on grid-forming inverter interfaced distributed energy resources. By coordinating the parameter settings of grid-forming inverters, the virtual power plant provides inertia support. Also, we design an online learning-based parameter settings method that makes the inertia of the virtual power plant adjustable. A case study in IEEE 34 nodes system illustrates the effectiveness of the proposed method.

This paper proposes an image-based algorithm for detecting and cleaning the wind turbine abnormal data based on wind power curve (WPC) images. The abnormal data are categorized into three types, negative points, scattered points and stacked points. The proposed algorithm includes three steps, data pre-cleaning, normal data extraction and data marking. The negative abnormal points, whose wind speed are greater than cut-in speed and power are below zero, are first filtered in the data pre-cleaning step. The scatter figure of the rest wind power data forms the WPC image and corresponding binary image. In the normal data extraction step, the principle part of the WPC binary image, representing the normal data, is extracted by the mathematical morphology operation (MMO). The optimal parameter setting of MMO is determined by minimizing the dissimilarity between the extracted principle part and the reference WPC image based on Hu moments. In the data mark step, the pixel points of scattered and stacked abnormal data are successively identified. The mapping relationship between the wind power points and image pixel points is built to mark the wind turbine normal and abnormal data. The proposed image-based algorithm is compared with k-means, local outlier factor (LOF), combined algorithm based on change point grouping algorithm and quartile algorithm (CA). Numerous experiments based on 33 wind turbines from two wind farms are conducted to validate the effectiveness, efficiency and universality of the proposed method.

With the advance in communication facilities and information technologies, the state estimation (SE) of distribution networks is subject to intensified cybersecurity threats caused by false data injection attacks (FDIAs). To address the issues, this paper proposes a novel FDIA detection strategy for unbalanced distribution networks. The SE model and corresponding general imperfect FDIA are introduced first to emulate the attacking behavior in practice. To achieve FDIA detection, we propose a square-root unscented Kalman filter (SR-UKF) based forecasting-aided SE (FASE) to generate estimation results. By modifying the filtering step of the proposed FASE into a redundant linear regression form, random outliers can be effectively detected and suppressed by leveraging the projection statistics (PS). Afterward, based on the acquired SE results, a generalized likelihood ratio test (GLRT) is designed to detect FDIAs on consecutive snapshots. In the GLRT, the dynamic time warping (DTW) distance between two innovation sequences is set as the test variable, which is compared with the offline determined detection threshold under a specific false alarm rate. The feasibility of the proposed general imperfect FDIA and the effectiveness of the proposed FDIA detection strategy are validated through extensive numerical simulations.

The increasing penetration of electric vehicles (EVs) presents an opportunity for demand response (DR) programs in the power network to improve overall operation efficiency. However, in the transportation network (TN), the potential of EV charging stations (EVCS) participating in DR needs to be more effectively harnessed. This paper proposes a tri-level DR framework to improve the EVCS flexibility via power distribution network (PDN) designing an incentive mechanism for EVCS in the coupled power and transportation network (CPTN). The proposed framework encompasses the distribution network operators (DNOs), EVCS and EVs, while ensuring information security and independent operation within each level. This framework integrates heterogeneous strategies of different participants aimed at profits maximization or operation safety, towards a dynamic equilibrium. A deep reinforcement learning-based (DRL) framework is proposed to obtain the DR strategies in the tri-level framework. The dynamic equilibrium between EVCS and DNOs is approximated through iterative training of safe DRL. The effectiveness of the proposed tri-level framework is validated through a real-world test system from Nanjing City, China, consisting of 39 nodes and 10 EVCSs. Numerical results confirm that the proposed DR program enhances the operational flexibility of EVCSs compared to traditional charging load regulation methods.

Edge intelligence (EI) is an emerging interdiscipline to advance the coordination of artificial intelligence and edge computing. EI sinks the computation and decision-making process from centralized clouds to the edge node in proximity to terminal devices, which is robust to the unacceptable communication delay or disconnection. In this paper, we propose an EI-based framework for online scheduling of soft open points with energy storage (ES-SOPs), a novel power electronic device, to enhance both spatial and temporal flexibility in power distribution networks. The proposed framework empowers the edge computing via hybrid deep reinforcement learning (HDRL), which seamlessly combines advantages of both data-driven deep neural networks and physics-based ES-SOPs model. Inside the edge computing node, a deep neural network first learns a set of parameters from the historical data and ES-SOPs local status. Then, the outputs of the deep neural network are fed into a physics-based ES-SOPs model to construct its objective function, where rigorous operation constraints are included. Finally, this model is solved to obtain near-optimal ES-SOPs online scheduling. Case studies on a modified IEEE 33-node system demonstrate the effectiveness of the proposed framework under different levels of uncertainties and its superiority over safe DRL and model predictive control-based methods.