In a false data injection attack (FDIA), an adversary stealthily compromises measurements from electricity grid sensors in a coordinated fashion, with a view to evading detection by the power system bad data detection module. A successful FDIA can cause the system operator to perform control actions that compromise either the physical or economic operation of the power system. In this letter, we consider some implications for FDIAs arising from the late 2015 Ukraine Blackout event.

With rapid advances in sensor, computer, and communication networks, modern power systems have become complicated cyber-physical systems. Assessing and enhancing cyber-physical system security is, therefore, of utmost importance for the future electricity grid. In a successful false data injection attack (FDIA), an attacker compromises measurements from grid sensors in such a way that undetected errors are introduced into estimates of state variables such as bus voltage angles and magnitudes. In evading detection by commonly employed residue-based bad data detection tests, FDIAs are capable of severely threatening power system security. Since the first published research on FDIAs in 2009, research into FDIA-based cyber-attacks has been extensive. This paper gives a comprehensive review of state-of-the-art in FDIAs against modern power systems. This paper first summarizes the theoretical basis of FDIAs, and then discusses both the physical and the economic impacts of a successful FDIA. This paper presents the basic defense strategies against FDIAs and discusses some potential future research directions in this field.

The cyber security of modern power systems has drawn increasing attention in both academia and industry. Many detection and defense methods for cyber-attacks have therefore been proposed to enhance robustness of modern power systems. In this paper, we propose a new, distributed blockchain-based protection framework to enhance the self-defensive capability of modern power systems against cyber-attacks. We present a comprehensive discussion on how blockchain technology can be used to enhance the robustness and security of the power grid, by using meters as nodes in a distributed network which encapsulates meter measurements as blocks. Effectiveness of the proposed protection framework is demonstrated via simulation experiments on the IEEE-118 benchmark system.

The prevalence of distributed energy resources encourages the concept of an electricity ‘Prosumer (Producer and Consumer)’. This paper proposes a distributed electricity trading system to facilitate the peer-to-peer electricity sharing amongst prosumers. The proposed system includes two layers. In the first layer, a Multi-Agent System (MAS) is designed to support the prosumer network, and an agent coalition mechanism is proposed to ena-ble the prosumers to form coalitions and negotiate electricity trading. In the second layer, a Blockchain (BC) based transaction settlement mechanism is proposed to enable the trusted and secure settlement of electricity trading transactions formed in the first layer. Simulations are conducted based on the Java Agent Development Environment (JADE) to validate the proposed electricity trading process.

With extensive pretrained knowledge and high-level general capabilities, large language models (LLMs) emerge as a promising avenue to augment reinforcement learning (RL) in aspects, such as multitask learning, sample efficiency, and high-level task planning. In this survey, we provide a comprehensive review of the existing literature in LLM-enhanced RL and summarize its characteristics compared with conventional RL methods, aiming to clarify the research scope and directions for future studies. Utilizing the classical agent–environment interaction paradigm, we propose a structured taxonomy to systematically categorize LLMs’ functionalities in RL, including four roles: information processor, reward designer, decision-maker, and generator. For each role, we summarize the methodologies, analyze the specific RL challenges that are mitigated and provide insights into future directions. Finally, the comparative analysis of each role, potential applications, prospective opportunities, and challenges of the LLM-enhanced RL are discussed. By proposing this taxonomy, we aim to provide a framework for researchers to effectively leverage LLMs in the RL field, potentially accelerating RL applications in complex applications, such as robotics, autonomous driving, and energy systems.

Modern power systems are rapidly evolving into complex cyber-physical systems. The increasingly complex interaction among different energy entities calls for a secure, efficient, and robust cyber infrastructure. As an emerging distributed computing technology, Blockchain provides a secure environment to support such interactions. This paper gives a prospective on using Blockchain as a secure, distributed cyber infrastructure for the future grid. Firstly, the basic principles of Blockchain and its state-of-the-art are introduced. Then, a Blockchain based smart grid cyber-physical infrastructure model is proposed. Afterwards, some promising application domains of Blockchain in future grids are presented. Following this, some potential challenges are discussed.