Smart Grid Resilience Research Articles

Zero-Trust Zero-Communication Defence against Hybrid Cyberattacks in Distributed Energy Resources Using Mean Field Reinforcement Leaning

As the evolution of smart grids accelerates, distributed energy resources (DERs) emerge as key elements in the transformation of global energy systems. However, the integration of these technologies introduces significant cybersecurity vulnerabilities, notably false data injection (FDI) and a direct load-altering attack (DLAA). Traditional load-altering attacks require a huge attack load and, thus, are not practical to implement. In contrast, in modern DER environments where households become “prosumers” with high-power energy generation, the implications of such attacks are substantially amplified. This paper considers a hybrid cyberattack that includes both FDI and a DLAA, and presents a hierarchical, optimal load adjustment framework that addresses these security concerns. A centralized optimizer first calculates the ideal load-shedding strategies for each substation, which are then securely broadcast to households. To address the complexities at the individual household level, we introduce a novel reinforcement learning algorithm termed Mean Field Deep Deterministic Policy Gradients (MF-DDPG). This algorithm employs mean-field game theory to enable decentrally coordinated decision-making among each household, making it particularly effective in zero-trust scenarios. Through this multifaceted approach, we offer a robust countermeasure against load-altering attacks, thereby enhancing the resilience and stability of advanced smart grids.

Security with Wireless Sensor Networks in Smart Grids: A Review

Smart Grids are an area where next-generation technologies, applications, architectures, and approaches are utilized. These grids involve equipping and managing electrical systems with information and communication technologies. Equipping and managing electrical systems with information and communication technologies, developing data-driven solutions, and integrating them with Internet of Things (IoT) applications are among the significant applications of Smart Grids. As dynamic systems, Smart Grids embody symmetrical principles in their utilization of next-generation technologies and approaches. The symmetrical integration of Wireless Sensor Networks (WSNs) and energy harvesting techniques not only enhances the resilience and reliability of Smart Grids but also ensures a balanced and harmonized energy management system. WSNs carry the potential to enhance various aspects of Smart Grids by offering energy efficiency, reliability, and cost-effective solutions. These networks find applications in various domains including power generation, distribution, monitoring, control management, measurement, demand response, pricing, fault detection, and power automation. Smart Grids hold a position among critical infrastructures, and without ensuring their cybersecurity, they can result in national security vulnerabilities, disruption of public order, loss of life, or significant economic damage. Therefore, developing security approaches against cyberattacks in Smart Grids is of paramount importance. This study examines the literature on “Cybersecurity with WSN in Smart Grids,” presenting a systematic review of applications, challenges, and standards. Our goal is to demonstrate how we can enhance cybersecurity in Smart Grids with research collected from various sources. In line with this goal, recommendations for future research in this field are provided, taking into account symmetrical principles.

The efficacy of a cellular approach to resilience-oriented operation of distribution grids based on eigenvectors of the Laplacian matrix

This study aims to address the following research query: In the event of an imminent disaster poised to impact distribution grids, what constitutes the optimal course of action for the distribution system operators to keep the lights on? To address this challenge, we propose a cost-efficient cellular model for enhancing the resilience of smart distribution grids. This model prioritizes resilience in the face of natural disasters or other disruptions that could impact service delivery. This method benefits both grid operators and consumers by ensuring reliable power supply while minimizing energy costs. Furthermore, the model's scalability allows it to be applied to distribution systems of varying sizes. The proposed method utilizes an innovative approach to form optimal cellular network configurations within the grid. As the first step in the formation of cellular topology for the grid, the eigenvectors of the Laplacian matrix of the grid will be used to decide on the optimal configurations. Subsequently, a bi-level mixed-integer linear programming model is proposed to decrease the network costs while simultaneously consider potential power transfer scenarios between the cells and the upstream network during both normal and emergency conditions. The researchers validated the effectiveness of the proposed method through simulations on an IEEE 33-bus test system. The results demonstrate outstanding performance, with a significant increase in the resilience index (96 %) and a substantial reduction in load-shedding costs (80 %), making the network considerably more robust.

A hybrid physical and co-simulation modern adaptive power protection testbed for testing the resilience of smart grids under cyber-physical threats

Power protection systems play a critical role in ensuring the safe and reliable operation of modern power grids. With the increasing complexity of grid topologies and the integration of Distributed Energy Resources (DERs), traditional protection schemes face challenges in maintaining effective coordination among relay systems. This paper presents a new adaptive Overcurrent Relay (OCR) protection and investigates the resilience of different traditional and new adaptive OCR protection schemes against various cyber-physical threats in a smart grid environment. Key contributions include the implementation of a novel adaptive protection scheme designed to dynamically adjust OCR settings based on real-time grid conditions, improving flexibility and responsiveness in managing grid variations induced by DER integration and operational changes. The proposed scheme is validated and implemented using the Multifunction Protection Relay SIEMENS 7SJ62, following to the programming standards of IEC 61131–3, ensuring the reliability and practical applicability of the adaptive OCR scheme in real-world scenarios. Additionally, a dedicated testbed is developed to simulate real-time cyber-physical interactions, incorporating hardware components such as the SIEMENS 7SJ62 and OMICRON-256 test device, along with high-performance computers and communication networks to facilitate comprehensive evaluations of adaptive OCR schemes under varying operational conditions. The study also examines the implications of real-time cyber-attacks on power system operations and the effectiveness of adaptive OCR protection schemes, addressing cybersecurity concerns and proposing mitigation strategies to the system resilience.

A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids

Smart grids are a cornerstone of the transition to a decentralised, low-carbon energy system, which offer significant benefits, including increased reliability, improved energy efficiency, and seamless integration of renewable energy sources. However, ensuring the security and resilience of smart grids is paramount. Cyber attacks, physical disruptions, and other unforeseen threats pose a significant risk to the stability and functionality of the grid. This paper identifies the research gaps and technical hurdles that hinder the development of a robust and secure smart grid infrastructure. This paper addresses the critical gaps in smart grid security research, outlining the technical challenges and promising avenues for exploration by both the industry and academia. A novel framework designed to enhance the reliability and security of smart grids was proposed against cyber attacks, considering the interconnectedness of the physical and cyber components. The paper further explores future research trends and identifies the key open issues in the ongoing effort to strengthen the security and resilience of smart grids.

Cyber resilience methods for smart grids against false data injection attacks: categorization, review and future directions

For a more efficient monitoring and control of electrical energy, the physical components of conventional power systems are continuously integrated with information and communication technologies, converting them into smart grids. However, energy digitalization exposes power systems into a wide range of digital risks. The term cyber resilience for electrical grids expands the conventional resilience of power systems, which mainly refers to extreme weather phenomena. Since this is a relatively new term, there is a need for the establishment of a solid conceptual framework. This paper analyzes and classifies the state-of-the-art research methodologies proposed for strengthening the cyber resilience of smart grids. To this end, the proposed work categorizes the cyberattacks against smart grids, identifies the vulnerable spots of power system automation and establishes a common ground about the cyber resilience. The paper concludes with a discussion about the limitations of the proposed methods in order to extract useful suggestions for future directions.

Enhancing Smart Grid Resilience: An Educational Approach to Smart Grid Cybersecurity Skill Gap Mitigation

Cybersecurity competencies are critical in the smart grid ecosystem, considering its growing complexity and expanding utilization. The smart grid environment integrates different sensors, control systems, and communication networks, thus augmenting the potential attack vectors for cyber criminals. Therefore, interdisciplinary competencies are required from smart grid cybersecurity specialists. In the meantime, there is a lack of competence models that define the required skills, considering smart grid job profiles and the technological landscape. This paper aims to investigate the skill gaps and trends in smart grid cybersecurity and propose an educational approach to mitigate these gaps. The educational approach aims to provide guidance for competence-driven cybersecurity education programs for the design, execution, and evaluation of smart grids.

Secure Consumer-Centric Demand Response Management in Resilient Smart Grid as Industry 5.0 Application With Blockchain-Based Authentication

Secure Consumer-Centric Demand Response Management in Resilient Smart Grid as Industry 5.0 Application With Blockchain-Based Authentication

Secure and Sustainable Energy Distribution through Blockchain Technology in Smart Grids

The incorporation of blockchain technology into smart grids is seen as a revolutionary method to bolster security and sustainability in energy distribution. This study offers a thorough examination of how blockchain, acting as a decentralized ledger, can be used within smart grids to facilitate secure energy transactions, manage distributed energy resources, and support peer-to-peer energy trading. The research investigates how blockchain's architectural deployment can mitigate cyber security risks, lower operational costs, and enhance transparency in energy transactions. It also explores how blockchain's ability to maintain immutable records aligns with smart grid technologies, suggesting a new framework that utilizes smart contracts to automate energy distribution protocols. Simulation findings indicate that integrating blockchain significantly boosts energy distribution efficiency while safeguarding data privacy and integrity. Additionally, the paper discusses the environmental benefits, illustrating how blockchain can optimize renewable energy sources within smart grids, ultimately reducing carbon emissions. The proposed model addresses the scalability challenge in blockchain networks, ensuring that the advantages of this integration are achieved without sacrificing smart grid operations' performance. This research sets the stage for future investigations into the resilience and adaptability of blockchain-enabled smart grids in meeting evolving energy needs.

Reinforcement learning-based energy storage management in smart grids

This study investigates the use of reinforcement learning (RL) techniques as a dynamic control mechanism to enhance the management of energy storage in smart grid systems. The research aims to optimize the efficiency of energy storage operations by analyzing collected data from different time intervals in a simulated smart grid scenario. An evaluation of the energy storage status reveals a consistent upward trend in the quantity of stored energy, with a 30% cumulative growth across time intervals. An examination of the demand and supply of the grid indicates a persistent insufficiency of energy, with an average shortfall of 15% in meeting the requirements of the system. Through the use of reinforcement learning (RL) methodologies, the system exhibits a remarkable 450% improvement in cumulative rewards, providing substantiation of its capacity to acquire knowledge and adjust its behavior over time. The system's actions indicate a purposeful shift in strategy, with 75% of instances involving charging procedures, emphasizing a commitment to energy preservation and the buildup of stored energy. Despite a shift in approach, persistent disparities between grid demand and supply need the implementation of more accurate technologies for effective energy management. The findings highlight the effectiveness of using reinforcement learning (RL) for managing energy storage in smart grids. This approach improves energy reserves and optimizes energy storage by altering actions accordingly. These insights contribute to the advancement of adaptive energy management strategies, resulting in the development of sustainable and resilient smart grid infrastructures.

Smart Grid Resilience for Grid-Connected PV and Protection Systems under Cyber Threats

In recent years, the integration of Distributed Energy Resources (DERs) and communication networks has presented significant challenges to power system control and protection, primarily as a result of the emergence of smart grids and cyber threats. As the use of grid-connected solar Photovoltaic (PV) systems continues to increase with the use of intelligent PV inverters, the susceptibility of these systems to cyber attacks and their potential impact on grid stability emerges as a critical concern based on the inverter control models. This study explores the cyber-threat consequences of selectively targeting the components of PV systems, with a special focus on the inverter and Overcurrent Protection Relay (OCR). This research also evaluates the interconnectedness between these two components under different cyber-attack scenarios. A three-phase radial Electromagnetic Transients Program (EMTP) is employed for grid modeling and transient analysis under different cyber attacks. The findings of our analysis highlight the complex relationship between vulnerabilities in inverters and relays, emphasizing the consequential consequences of affecting one of the components on the other. In addition, this work aims to evaluate the impact of cyber attacks on the overall performance and stability of grid-connected PV systems. For example, in the attack on the PV inverters, the OCR failed to identify and eliminate the fault during a pulse signal attack with a short duration of 0.1 s. This resulted in considerable harmonic distortion and substantial power losses as a result of the protection system’s failure to recognize and respond to the irregular attack signal. Our study provides significant contributions to the understanding of cybersecurity in grid-connected solar PV systems. It highlights the importance of implementing improved protective measures and resilience techniques in response to the changing energy environment towards smart grids.

Cyber-constrained load shedding for smart grid resilience enhancement

With the further development of information and communication technology and their widespread use in power systems, our networks are moving towards cyber-physical systems. This paper proposes an optimal load flow method considering cyber constraints for the emergency response of smart networks to improve resilience. In the proposed method, not only the effect of cyber systems on physical systems but also the effect of physical systems on cyber systems is considered mutually. The mutual effect is that if the cyber nodes are interrupted, the cyber system can no longer send the information of generators and loads to the central controller. Also, if the load reduction in the bus exceeds a percentage in the physical network, it is practically no longer possible to feed the cyber system in that bus. In the following, the objective function is considered as the minimization of the sum of the cost of load shedding and generator production. Also, fast voltage stability index is used to determine weak buses. Finally, considering that the above model is non-linear, for this purpose, the problem is modeled by the PSO algorithm. Finally, a standard IEEE test system is used to evaluate the effectiveness of the proposed policy.

Power generation allocation of cyber–physical power systems from a defense–attack–defense perspective

Cyber–physical power systems have emerged as intelligent devices integrate into traditional power systems. These systems, featuring an integration of the physical and cyber components, are exposed to compounded risks from both realms. This paper explores the optimization of generation node allocation with a defense–attack–defense perspective to bolster the system’s resistance to external deliberate attacks. A trilevel optimization model is introduced, addressing the complications arising from communication line failures. This model encompasses upper-level defenders orchestrating generation allocation, middle-level attackers executing deliberate attacks, and lower-level operators optimizing system performance. The model leverages the Column-and-Constraint Generation (C&CG) approach to derive the optimal generation allocation strategy. Empirical validation using two IEEE test cases demonstrates the model’s effectiveness. Results highlight a significant escalation in total load loss upon communication line failures, and underscore the critical role of decentralized generation allocation in enhancing system resilience. These findings hold the potential to inform strategies for safeguarding cyber–physical power systems and reinforcing the architecture of resilient smart grids.

Cybersecurity and Confidentiality in Smart Grid for Enhancing Sustainability and Reliability

Ensuring cybersecurity and confidentiality in smart grids is crucial for enhancing sustainability and reliability in today's technology-driven world. With the increasing reliance on smart grid technologies, it is imperative to address the potential cybersecurity risks and protect the confidentiality of sensitive data. This research focuses on exploring the challenges and strategies associated with cybersecurity and confidentiality in smart grids. It examines the importance of safeguarding smart grid infrastructure from cyber threats to maintain sustainable and reliable energy delivery systems. The study investigates various techniques and technologies, including encryption, authentication, intrusion detection, and secure communication protocols, that can be employed to enhance the cybersecurity and confidentiality of smart grids. By highlighting the significance of a robust cybersecurity framework and the integration of privacy-preserving measures, this research aims to contribute to the development of secure and resilient smart grid systems. The findings and recommendations presented in this work provide valuable insights for policymakers, industry professionals, and researchers involved in the design and implementation of secure smart grid solutions, ultimately leading to the advancement of sustainable and reliable energy infrastructures.

Enhancing smart grid resilience with deep learning anomaly detection prior to state estimation

State estimation is a critical process in modern power system control centers that operate under strict real-time requirements. Among its key post-processing components are bad data detection and identification, with the latter being more complex and potentially compromising the system’s real-time performance. This paper proposes a novel component in the state estimation process called Anomaly Detection and Identification Module (ADIM), whose goal is to detect anomalies, or erroneous data points, prior to the state estimation process. Our work presents a deep learning-based method that exhibits a commendable level of accuracy in detecting anomalies within a continuous stream of measurements. ADIM’s capabilities are demonstrated through comprehensive experiments on a set of test cases that effectively encompass various network configurations, transformer types, and load scenarios. It is shown that ADIM can detect and identify anomalies effectively, significantly reducing the need for the bad data detection component and improving overall state estimation responsiveness. Our work lays the foundation for developing a detection- and identification-based anomaly system for power system measurements.

A Storage-Based Fixed-Time Frequency Synchronization Method for Improving Transient Stability and Resilience of Smart Grid

A storage-based distributed fixed-time frequency synchronization method is developed to enhance the frequency and transient stability of smart grid. The multi-agent cyber-physical model of power system is the foundation of the proposed control method which utilizes data on the relative angle and frequency between the local agent and its neighbor agents. The control method mainly consists of frequency control based on energy storage system (ESS), P-ω droop control of synchronous generator (SG), and P-θ droop control of converter-based generator (CBG), which is designed by the backstepping method and Lyapunov theory. In addition, to hasten frequency synchronization, the nonlinear voltage control which is compatible with frequency control is presented. Meanwhile, the control method is not only applied to the homogeneous power systems only include SGs, but also deals with the challenge of heterogeneous bus dynamics introduced by the coexistence of SGs and CBGs. The distributed fixed-time frequency control can achieve self-protecting from denial-of-service (DoS) attacks by transforming into decentralized control. Comparative simulations show that the designed storage-based frequency synchronization method is preferable in improving the transient stability and resilience of smart grid.

Robust inter-reliant resilience of cyber-physical smart grids

Nowadays, information and cyber technologies have been integrated into traditional energy systems to enhance their energy performance. While this integration can improve the cost-benefit of smart grids, it may not fully capture the benefits in extreme conditions or during cascading outages.Electrical and cyber-physical energy systems have become more interconnected due to integration, which has also resulted in electrical energy systems (EESs) being more vulnerable to extreme natural disasters. The other system can be infiltrated by an outage in one system, leading to a cascading outage process. To tackle these challenges, a robust model of cyber-physical energy systems constrained by resilience considerations has been proposed to model the interdependences of EESs and cyber-physical systems (CPS) (i.e., EES & CPSs) under extreme natural disasters. The objective function of the proposed model is to improve cost-benefit and energy performance under extreme natural disasters. In this paper, it is explained how the energy efficiency for electrical energy systems can be improved under extreme natural disasters by the proposed operation processes for bulk integrated EESs & CPS. However, the highly nonlinear integrated EESs & CPS model is linearized to moderate the model complexity. Finally, the effectiveness of the proposed integrated EESs & CPS model is evaluated by investigating the modified IEEE-30 bus energy system.

Fortified-Grid: Fortifying Smart Grids through the Integration of the Trusted Platform Module in Internet of Things Devices

This paper presents a hardware-assisted security primitive that integrates the Trusted Platform Module (TPM) into IoT devices for authentication in smart grids. Data and device security plays a pivotal role in smart grids since they are vulnerable to various attacks that could risk grid failure. The proposed Fortified-Grid security primitive provides an innovative solution, leveraging the TPM for attestation coupled with standard X.509 certificates. This methodology serves a dual purpose, ensuring the authenticity of IoT devices and upholding software integrity, an indispensable foundation for any resilient smart grid security system. TPM is a hardware security module that can generate keys and store them with encryption so they cannot be compromised. Formal security verification has been performed using the random or real Oracle (ROR) model and widely accepted AVISPA simulation tool, while informal security verification uses the DY and CK adversary model. Fortified-Grid helps to validate the attested state of IoT devices with a minimal network overhead of 1984 bits.

Strengthening Smart Grid Cybersecurity: An In-Depth Investigation into the Fusion of Machine Learning and Natural Language Processing

Smart grid technology has transformed electricity distribution and management, but it also exposes critical infrastructures to cybersecurity threats. To mitigate these risks, the integration of machine learning (ML) and natural language processing (NLP) techniques has emerged as a promising approach. This survey paper analyses current research and applications related to ML and NLP integration, exploring methods for risk assessment, log analysis, threat analysis, intrusion detection, and anomaly detection. It also explores challenges, potential opportunities, and future research directions for enhancing smart grid cybersecurity through the synergy of ML and NLP. The study's key contributions include providing a thorough understanding of state-of-the-art techniques and paving the way for more robust and resilient smart grid defences against cyber threats.

Hydrogen‐powered smart grid resilience

AbstractWith an increasing frequency of natural disasters and security attacks, the safe and stable operation of smart grid has been challenged unprecedently. To reduce the economic loss and social impact caused by power outage accidents, it is urgent to develop and improve the smart grid technology, and strengthen the disaster resistance and recovery capability of smart grids when faced with extreme events. As an efficient and flexible secondary energy source, hydrogen is crucial in improving the resilience of smart grid and supporting energy security. To further promote the deep integration of hydrogen systems and smart grid and improve the energy system resilience, the resilience of smart grids supported by hydrogen is assessed in this study. First, a technical framework of hydrogen‐powered smart grid resilience is established, and the value of hydrogen‐powered smart grid resilience is analysed considering different time frames (before, during and after an extreme event) of smart grids facing extreme events. Then, hydrogen‐powered smart grid resilience is investigated from perspectives of pre‐prevention regulation, in‐process correction regulation, and post‐recovery regulation. Finally, future direction for hydrogen‐powered smart grid resilience is investigated and related policy suggestions are provided.