Electricity theft detection (ETD) plays a crucial role in reducing nontechnical losses of power transmission and distribution in smart grids. However, accurate ETD remains challenging due to the presence of missing values and highly unbalanced data. To further improve the accuracy of ETD, an advanced electricity theft detection system (ETDS) containing five modules is newly proposed. In detail, the data imputation module is designed to intelligently recover the missing information in the studied dataset; the feature extraction module is to eliminate redundant noise and extract the key components from the dataset; the deep learning module is developed based on the newly proposed regularized attention mechanism-based bidirectional gate recurrent unit; and the parameter optimization module is dedicated to acquiring the optimal parameters. Experiment results based on the performance evaluation module indicate that the ETDS not only outperforms its benchmarks in forecasting accuracy but also exhibits higher convergence rate and robustness.

A large number of electric vehicles, distributed solar, and/or wind turbine generators connected to distribution systems lead to frequent and sharp voltages fluctuations. The action rates of conventional adjustable devices and smart inverters are very different. In this context, a novel dual-timescale voltage control scheme is proposed by organically combining data-driven with physics-based optimization. On fast timescale, a quadratic programming for balanced and unbalanced distribution systems is developed based on branch flow equations. The optimal reactive power of renewable distributed generators and static VAR compensators is configured on several minutes or seconds. Whereas, on slow timescale, a data-driven Markovian decision process is developed, in which the charge/discharge power of energy storage systems, statuses/ratios of switchable capacitors reactors, and voltage regulators are configured hourly to minimize long-term discounted squared voltages magnitudes deviations using an adapted deep deterministic policy gradient deep reinforcement learning algorithm. The capabilities of the proposed method are validated with IEEE 33-bus balanced and 123-bus unbalanced distribution systems.

Customer baseline load (CBL) reconstruction is a critical problem in residential demand response. The difficulty of residential CBL lies in the variability of both irregular consumption and on-site distributed energy resources. Targeting the CBL reconstruction of residential prosumers, a regression-based estimation scheme is proposed using stacked autoencoders (SAEs) under the federated learning (FL) framework. In the FL framework, each residential unit (RU) stores and trains data locally without sharing them with neighboring RUs or the independent third party (ITP) responsible for CBL reconstruction. Local updates containing no load information are exchanged with the ITP (server) for the training improvement. The FL framework can, thus, protect the privacy of customers. Experimental results show that the proposed FL-based cascaded SAE outperforms the baseline on all tests and achieves up to 62.5% improvement in reducing reconstruction error. Moreover, it has enhanced privacy-preserving knowledge-sharing ability, higher efficiency, and better stability.

Power system state estimation (PSSE) is the foundation of energy management system applications. Hence, operators impose stringent requirements on PSSE data integrity. False data injection attacks (FDIAs) can cause risks to PSSE data-driven operations and demand mitigation. In this article, we present a two-step FDIA detector design. In step one, we study a novel stealthy attack policy by simultaneously considering the attacker’s cost reduction and damage production. In step two, with the aid of a deep autoencoding Gaussian mixture model (DAGMM), we design an unsupervised detection scheme to detect the stealthy attack. The DAGMM-based detector can meet the requirement of rapidity, unsupervisedness, and data imbalance tolerance. Eventually, we simulate and validate the stealthy attack policy and the corresponding detector using the benchmark IEEE 39-bus and 118-bus systems.

The progression of modern computing technologies assists the development of cyber-physical systems, which are transforming the legacy electrical power systems into smarter ones. The informationalization of the grid poses potential vulnerabilities concerning cyberattacks. With dynamic variations over time, cyberattacks can cause significant impacts on the secondary frequency control with various attack scenarios. In this article, by divulging the characteristics of dynamic attacks, the stability and dynamic responses of secondary frequency control systems are analyzed. The complete attack models considering dynamic load altering attack and dynamic false data injection attack are both derived first. Then the system stability is evaluated with different attack models through mathematical analysis. Eventually, the simulation studies against two benchmark power system models validate the evaluation results.

Unexpected large power surges will cause instantaneous grid shock and, thus, emergency control plans must be implemented to prevent the system from collapsing. In this article, with the aid of reinforcement learning, novel model-free control (MFC)-based emergency control schemes are presented. First, multi-Q-learning-based emergency plans are designed for limited emergency scenarios by using offline-training-online-approximation methods. To solve the more general multiscenario emergency control problem, a deep deterministic policy gradient (DDPG) algorithm is adopted to learn near-optimal solutions. With the aid of deep Q network, DDPG-based strategies have better generalization abilities for unknown and untrained emergency scenarios, and thus are suitable for multiscenario learning. Through simulations using benchmark systems, the proposed schemes are proven to achieve satisfactory performances.