Due to the aging of electric infrastructures, conventional power grid is being modernized towards smart grid that enables two-way communications between consumer and utility, and thus more vulnerable to cyber-attacks. However, due to the attacking cost, the attack strategy may vary a lot from one operation scenario to another from the perspective of adversary, which is not considered in previous studies. Therefore, in this paper, scenario based two-stage sparse cyber-attack models for smart grid with complete and incomplete network information are proposed. Then, in order to effectively detect the established cyber-attacks, an interval state estimation (ISE) based defense mechanism is developed innovatively. In this mechanism, the lower and upper bounds of each state variable are modeled as a dual optimization problem that aims to maximize the variation intervals of the system variable. At last, a typical deep learning, i.e., stacked auto-encoder (SAE), is designed to properly extract the nonlinear and non-stationary features in electric load data. These features are then applied to improve the accuracy for electric load fore-casting, resulting in a more narrow width of state variables. The uncertainty with respect to forecasting errors is modeled as a parametric Gaussian distribution. The validation of the proposed cyber-attack models and defense mechanism have been demonstrated via comprehensive tests on various IEEE benchmarks.

Protecting cybersecurity is a non‐negotiable task for smart grids (SG) and has garnered significant attention in recent years. The application of artificial intelligence, particularly deep learning (DL), holds great promise for enhancing the cybersecurity of SG. Nevertheless, previous surveys and review articles have failed to comprehensively investigate the intersection between DL and SG cybersecurity. To address this gap, this study presents a survey of the latest advancements in DL technology and their relevance to SG cybersecurity. First, the functional mechanisms and scope of application of common DL techniques are explored. Subsequently, SG cyberthreats are categorised into distinct types of cyberattacks that have not been systematically examined in previous surveys. Based on this, a thorough review of the application of DL techniques in addressing each cyberthreat along with recommendations and a generalised framework for enhancing cyberattack detection using DL is offered. Finally, insights are provided into the emerging challenges presented by DL applications in SG cybersecurity that are yet to be widely acknowledged, and potential research avenues are proposed to address or alleviate these challenges.

Applying large language models (LLMs) to modern power systems presents a promising avenue for enhancing decision-making and operational efficiency. However, this action may also incur potential security threats, which have not been fully recognized so far. To this end, this article analyzes potential threats incurred by applying LLMs to power systems, emphasizing the need for urgent research and development of countermeasures.

Designing customized dynamic pricing is a promising way to incent consumers to adjust their daily en-ergy consumption behaviors. It helps manage flexible de-mand response resources on peak load. However, it is insuf-ficiently investigated in previous studies from the individual behavior perspective. To tackle the gap, this paper proposes a graph deep learning-based retail dynamic pricing mecha-nism. First, a graph attention network-based temporal price elasticity perceptron model is proposed. It explores a novel path to learn price elasticity by using graph deep learning, and can accurately assess consumers energy consumption behaviors under different prices. Then, to avoid unfair eval-uation of demand response, two indexes are proposed as auxiliary measures to assess energy consumption behavior learning models. At last, a customized dynamic pricing model based on the temporal price elasticity perceptron model is proposed. It can develop consumers time-varying demand response potential. This potential is first defined in this paper to measure what potentials of shifting/curtailing energy during a period a consumer has. By the pricing, the consumer could be incented to engage in demand response. The numerical studies validate the feasibility and superior-ity of the proposed methods, meanwhile price risks from the price change can be hedged effectively.

Developing the deep learning (DL) technique is a promising way to improve renewable energy forecasting accuracy and offset the negative impacts of renewable energy on the power system. However, the application of the DL technique brings novel cyberthreats to the renewable energy forecast, and its cybersecurity has not received enough attention in previous literatures. To fill the gap, the vulnerability of renewable energy forecasting is, among the first, studied in-depth in this article. First, a novel cyberattack named adversarial learning attack (ALA) is proposed. The ALA is achieved by tampering with the meteorological data obtained by online weather forecasts from external application programming interfaces to undermine the renewable energy forecasting performance, which jeopardizes the power system operation. Then, an iterative algorithm is proposed to solve the ALA-based optimization problem. As the DL model is involved as optimization constraints, the optimization problem is nonconvex and NP-hard, which is unable to be solved by traditional approaches. The proposed algorithm utilizes the proximal gradient descent principle and is effective in iteratively exploring the near-optimal solution. At last, the impact of the ALA strategy on the power system operation is assessed, which considers the economic loss incurred and the potential hazards. The feasibility and efficacy of the ALA strategy are validated by conducting comprehensive and extensive experiments on the IEEE 30-bus benchmarks. The simulation results reveal that the ALA is able to impose severe economic losses on the operation and even induces disastrous hazards, such as power system collapse.

Developing the deep learning (DL) technique is a promising way to enhance smart grid (SG) cybersecurity. However, previous DL methods require massive attack samples for cyberattack correlation learning, whilst the real-world SG is incapable of providing such a large dataset. Moreover, existing work commonly focuses on extracting temporal features from power grid data for cyberattack detection, while the spatial features are insufficiently investigated. To address these limitations, a spatiotemporal graph deep learning (STGDL)-based scheme is proposed to detect cyberattacks without requiring attack samples. First, the graph convolution and temporal gated convolution are orchestrated to extract spatiotemporal features jointly. Then, a quantile regression training strategy is adopted to give normally operational bounds of state variables in state estimation (SE). It gets rid of limitations on needing attack samples, and the state bounds can indicate cyberattack anomalies. At last, a super-resolution perception (SRP) network is proposed. The SRP network is able to reconstruct the high-frequent data of estimated states from low-frequent SE results, so as to improve the temporal learning ability in the STGDL model. The feasibility and effectiveness of the proposed scheme are validated by conducting comprehensive and extensive experiments on the IEEE 30-bus and 118-bus benchmarks.