Smart Grid Network Research Articles

HMLB: Holonic multi-agent approach for preventive controllers load-balancing in SDN-enabled smart grid

Smart grid networks present advantages like improving reliability, security, scalability, etc. However, designing an efficient communication infrastructure for smart grid networks is a great challenge. This is because of its dependency on proprietary protocols and specific vendors. Software-defined-enabled smart grid (SDN-SG) tackles this problem by incorporating diverse protocols and standards including open source platforms. One of the most important questions in Software-defined Networking (SDN) is the controller placement problem being NP-Hard in nature. Therefore, the predominant goal of this paper is to diminish the time complexity by modeling the controller placement problem based on the holonic multi-agent system. The hierarchical structure of a holonic organization improves the computational complexity through the divide and conquer mechanism. Such an idea also decreases the distributed controllers' synchronization overhead which is an issue in the realm of SDN. On the other hand, the proper functioning of the smart grid has a strict dependency on time-critical services. Accordingly, the controller placement is supposed to be a Quality of Service-aware (QoS-aware) one. Also, intermittent topology changes in the smart grid and the occasional joining and leaving of members result in an unsteady traffic pattern and dynamicity of controller load. This research is a pioneer in providing a QoS-aware and dynamic controller placement mechanism for SDN-SG. Experimental results certify the preponderance of the approach over similar ones concerning computational complexity, packet loss, controllers’ synchronization overhead, and also load-balancing overhead.

Game Theory Applications in Smart Grid Energy Management

Diversion hypothesis has ended up a valuable way to portray and make strides complicated connections in numerous zones, such as savvy framework vitality administration. When it comes to savvy lattice operations, the truth that vitality generation, exchange, and utilize are all energetic and separated makes huge issues that are difficult to fathom with standard controlled strategies. This outline looks at how amusement hypothesis can be utilized to assist individuals make better decisions and make the leading utilize of assets in savvy framework settings. The most objective is to figure out how game-theoretic models can offer assistance with productive vitality management by looking at how diverse parties, like clients, providers, and lattice administrators, will act in numerous circumstances. Regularly, these bunches have objectives that are at chances with each other, like minimizing costs, making as much cash as possible, and ensuring the environment. Amusement hypothesis lets us see at these trades as in case they were arranged recreations, with each individual attempting to get the foremost out of the diversion by altering their claim choices and the activities of others. Key thoughts from diversion hypothesis, like Nash harmony, agreeable and non-cooperative diversions, and component plan, are utilized to come up with keen framework operations procedures that are both reasonable and proficient. Nash balance could be a key thought for a arrangement where no individual can pick up by changing their arrange on their possess. This provides security in independent decision-making. In addition, the abstract talks about a number of case studies and uses where game theory has been used successfully in smart grids. Some of these are demand response programs, energy trade markets, and making the best use of integrating green energy. By making these events into games, parties can plan for and deal with problems like grid instability, price changes, and traffic jams. The outline also talks about problems that are still being researched and where the field might go in the future. These include making game-theoretic models work on large-scale smart grid networks, using real-time data analytics to help people make better decisions, and using machine learning to make predictions more accurate.

Node Allocation Strategy for Low Latency Neighborhood Area Networks in Smart Grid

Node Allocation Strategy for Low Latency Neighborhood Area Networks in Smart Grid

Anomaly Detection Based on Temporal Convolutional Networks in Smart Grid

Abstract Abnormal power consumption detection is an important means to detect power emergencies in time. It can effectively reduce power loss and residential losses due to electrical failures. This paper presents an anomaly detection method based on temporal convolutional networks and statistics. The predicted power consumption results of subsequent periods are compared with the actual results, and statistical methods are used to determine whether abnormal power consumption exists. If an abnormal result occurs, the system will issue a warning, search the fault location in the database established by the user, and determine the initial fault type. Simulation results show that this method can effectively detect abnormal power consumption.

Comprehensive review of energy management strategies: Considering battery energy storage system and renewable energy sources

AbstractThe transformation of power system networks is slowly taking shape, the advent of interruptive technological platforms dealing with digitalization and real‐time trading of power has gained attention based on incorporation of more renewables into the grid. The stochastic nature of renewables pauses security of supply challenges and other related stability concerns, and for this reason efficient methods are investigated in this review to build an understanding of microgrid energy management system (MG‐EMS) and distribution‐based energy management strategies aimed at transforming the conventional grid network into smart grid network. In essence, propagating a technological shift to microgrids which have proven to be ideal distribution networks for residential and commercial loads, have become indispensable in handling distributed energy resources (DER), such as solar, PV, wind, battery energy storage systems (BESS) and small‐scale microgrids, for example in case of excess supply, energy storage system (ESS) has been formulated as a solution to curb excess supply and can offer ancillary services to the grid network. Within the perspective of electricity generation and distribution, microgrid control methodologies, distribution network (DN) management approaches and incumbent optimization strategies used to coordinate and manage grid‐level uncertainties are investigated. In addition, this study proposes distributionally robust optimization (DRO), to manage and mitigate risks associated to shortage or oversupply of power from RESs.

Network Security Challenges and Countermeasures for Software-Defined Smart Grids: A Survey

The rise of grid modernization has been prompted by the escalating demand for power, the deteriorating state of infrastructure, and the growing concern regarding the reliability of electric utilities. The smart grid encompasses recent advancements in electronics, technology, telecommunications, and computer capabilities. Smart grid telecommunication frameworks provide bidirectional communication to facilitate grid operations. Software-defined networking (SDN) is a proposed approach for monitoring and regulating telecommunication networks, which allows for enhanced visibility, control, and security in smart grid systems. Nevertheless, the integration of telecommunications infrastructure exposes smart grid networks to potential cyberattacks. Unauthorized individuals may exploit unauthorized access to intercept communications, introduce fabricated data into system measurements, overwhelm communication channels with false data packets, or attack centralized controllers to disable network control. An ongoing, thorough examination of cyber attacks and protection strategies for smart grid networks is essential due to the ever-changing nature of these threats. Previous surveys on smart grid security lack modern methodologies and, to the best of our knowledge, most, if not all, focus on only one sort of attack or protection. This survey examines the most recent security techniques, simultaneous multi-pronged cyber attacks, and defense utilities in order to address the challenges of future SDN smart grid research. The objective is to identify future research requirements, describe the existing security challenges, and highlight emerging threats and their potential impact on the deployment of software-defined smart grid (SD-SG).

Real-Time Anomaly Detection in Smart Grid Networks Using Deep Learning with Cross-Domain Generalization and Multi-Task Learning

Real-Time Anomaly Detection in Smart Grid Networks Using Deep Learning with Cross-Domain Generalization and Multi-Task Learning

Quantum‐safe Lattice‐based mutual authentication and key‐exchange scheme for the smart grid

AbstractThe smart grid network (SGN) is expected to leverage advances in the Internet of Things (IoT) to enable effective delivery and monitoring of energy. By integrating communication, computing, and information tools like smart sensors and meters to facilitate the process of monitoring, predictions, and management of power usage, the SGN can improve competence of power‐grid architecture. However, the effective deployment of IoT‐powered SGNs hinges on the deployment of strong security protocols. With the advent of quantum computers, classic cryptographic algorithms based on integer factorization and the Diffie‐Hellman assumptions may not be suitable to secure the sensitive data of SGNs. Therefore, in this paper, a secure quantum‐safe mutual authentication and key‐exchange (MAKe) mechanism is proposed for SGNs, that make use of the hard assumptions of small integer solution and inhomogeneous small integer solution problems of lattice. The proposed protocol is intended to offer confidentiality, anonymity, and hashed‐based mutual authentication with a key‐exchange agreement. Similarly, this scheme allows creation and validation of the mutual trust among the smart‐meters (SMs) and neighbourhood‐area network gateway over an insecure wireless channel. A random oracle model is then used to perform the formal security analysis of the proposed approach. A thorough formal analysis demonstrates proposed algorithm's ability to withstand various known attacks. The performance analysis shows that the proposed approach outperforms other comparative schemes with respect to at least 22.07% of minimal energy utilization, 51.48% effective storage and communications costs, as well as 76.28% computational costs, and thus suitable for resource‐constrained SGNs.

End-to-End Delay Performance Analysis for Delay-Sensitive Services in Polymorphic Networks

Abstract In the polymorphic network for smart grid, the end-to-end latency performance analysis of delay-sensitive power control services such as differential protection and distributed power regulation is a crucial component in the intelligent control and management of power network slicing. In this paper, based on a detailed analysis of end-to-end delay factors of power control service, the distributed power regulation pilot project is taken as a typical application scenario of power control service, and the end-to-end delay of such service is theoretically derived and analyzed in detail by using network calculus theory. Numerical verification shows that the end-to-end delay performance of the power control service is positively correlated with the number of concurrent data streams and the number of subsystems and negatively correlated with the system service rate. Compared with the burst degree of the current data streams, the burst degree of other data streams significantly impacts the end-to-end delay calculation results of the current data flow.

Intelligent Integration of Vehicle-to-Grid (V2G) and Vehicle-for-Grid (V4G) Systems: Leveraging Artificial Neural Networks (ANNs) for Smart Grid

This paper presents a groundbreaking control strategy for a bidirectional battery charger that allows power to be injected into the smart grid while simultaneously compensating for the grid’s reactive power using an electric vehicle battery. An artificial neural network (ANN) controller is utilized for precise design to ensure optimal performance with minimal error. The ANN technique is applied to generate sinusoidal pulse width modulation (SPWM) for a bidirectional AC–DC inverter, with the entire algorithm simulated in MATLAB Simulink.The core innovation of this study is the creation of the ANN algorithm, which supports grid compensation using electric vehicle batteries, an approach termed “vehicle-for-grid”. Additionally, the paper details the PCB circuit design of the system controlled by the DSP F28379D board, which was tested on a three-phase motor. The total harmonic distortion (THD) of the proposed ANN algorithm is approximately 1.85%, compared to the MPC algorithm’s THD of about 2.85%. This indicates that the proposed algorithm is more effective in terms of the quality of the power injected into the grid. Furthermore, it demonstrates effective grid compensation, with the reactive power effectively neutralized to 0KVAR in the vehicle-for-grid mode

Detecting False Data Injection Attacks Using Machine Learning-Based Approaches for Smart Grid Networks

Current electricity sectors will be unable to keep up with commercial and residential customers’ increasing demand for data-enabled power systems. Therefore, next-generation power systems must be developed. It is possible for the smart grid, an advanced power system of the future, to make decisions, estimate loads, and execute other data-related jobs. Customers can adjust their needs in smart grid systems by monitoring bill information. Due to their reliance on data networks, smart grids are vulnerable to cyberattacks that could compromise billing data and cause power outages and other problems. A false data injection attack (FDIA) is a significant attack that targets the corruption of state estimation vectors. The primary goal of this paper is to show the impact of an FDIA attack on a power dataset and to use machine learning algorithms to detect the attack; to achieve this, the Python software is used. In the experiment, we used the power dataset from the IoT server of a 10 KV solar PV system (to mimic a smart grid system) in a controlled laboratory environment to test the effect of FDIA and detect this anomaly using a machine learning approach. Different machine learning models were used to detect the attack and find the most suitable approach to achieve this goal. This paper compares machine learning algorithms (such as random forest, isolation forest, logistic regression, decision tree, autoencoder, and feed-forward neural network) in terms of their effectiveness in detecting false data injection attacks (FDIAs). The highest F1 score of 0.99 was achieved by the decision tree algorithm, which was closely followed by the logistic regression method, which had an F1 score of 0.98. These algorithms also demonstrated high precision, recall, and model accuracy, demonstrating their efficacy in detecting FDIAs. The research presented in this paper indicates that combining logistic regression and decision tree in an ensemble leads to significant performance enhancements. The resulting model achieves an impressive accuracy of 0.99, a precision of 1, and an F1 score of 1.

MODERNIZAÇÃO DA DISTRIBUIÇÃO DE ENERGIA NO AMAZONAS: A REVOLUÇÃO DAS SMART GRIDS

The Smart Grids network has introduced a proven power system based on a modernized power supply system with the introduction of advanced data information and communication technologies. Therefore, the general objective of the research is to highlight the benefits of modernizing the energy measurement process in Amazonas through the implementation of the smart grids device. Data production occurred through documentary and bibliographical research, thus the study was classified as qualitative, in addition, it was necessary to carry out documentary research carried out in the public information database of the National Electric Energy Agency (Aneel) and the Brazilian Association of Electricity Distributors (ABRADEE). It is concluded that the SG system are highly integrated intended to provide reliable and quality energy in an economical and sustainable way, include better quality of energy transmission/distribution from power generation to end users, with energy flow and optimized efficiency. In addition to modern automation, it offers two-way communications, advanced monitoring and control to optimize power quality issues.

A contrived dataset of substation automation for cybersecurity research in the smart grid networks based on IEC61850

A contrived dataset of substation automation for cybersecurity research in the smart grid networks based on IEC61850

Retraction Note: Hybrid traffic security shaping scheme combining TAS and CQSF of time-sensitive networks in smart grid

Retraction Note: Hybrid traffic security shaping scheme combining TAS and CQSF of time-sensitive networks in smart grid

An Improved Analysis of Distributed Resource Access Location and Capacity on Voltage Stability of Distribution Networks in Smart Grid

Concerns about how Distributed Energy Resources (DERs) may affect distribution network voltage stability have been brought up by the widespread usage of DERs in the elegant Grid. A Smart Grid connects a range of distributed energy resources (DERs) to the distribution network, including electric cars, wind turbines, and solar panels. This connection can alter the working conditions of the network. These changes can lead to voltage stability problems, which can affect the overall efficiency and reliability of the Smart Grid. Thus, there is a need for an improved analysis of the effects of Distributed Resource Access Location and Capacity (DRALC) on voltage strength in distribution networks. Here, we propose an improved analysis of the impact of DRALC on voltage stability in distribution networks in the Smart Grid. Our analysis considers the location and capacity of DERs and their effect on voltage stability using a combination of analytical and simulation methods. We first develop a mathematical model to represent the distribution network, which includes the DERs as voltage sources. Next, we use the voltage stability index and load flow analysis to evaluate the voltage stability of the network under different DRALC scenarios. Finally, we validate our analysis by performing simulations on a real-world distribution network.

CyberGrid: an IEC61850 protocol-based substation automation virtual cyber range for cybersecurity research in the smart grid

ABSTRACT A cyber range provides a controlled environment for simulating mission-critical systems for cybersecurity research following the high risks involved in conducting experiments in real-life systems. Existing cyber ranges do not provide close emulation of Cyber-Physical Systems (CPS) in the Smart Grid Networks (SGNs), with emphasis on industry-based proprietary and IED protocols. This paper presents CyberGrid Testbed – a virtual Cyber-Range developed by the CyberSCADA Research Laboratory at Obafemi Awolowo University, Ile-Ife, Nigeria, which emulates Substation Automation processes with IEC61850 and Modbus/TCP protocols and support for MMS, and IED. This was built on OpenPLC6185, simulating Twelve attack patterns, and four attack types.

Wireless Charging System for Electric Vehicles

This paper presents a comprehensive review of wireless electric vehicle (EV) charging technologies, focusing on the advancements, challenges, and prospects in this domain. With the increasing adoption of electric vehicles globally, the demand for efficient and convenient charging solutions has grown significantly. Wireless charging offers promising opportunities to enhance the user experience by eliminating the need for physical connections between the charger and the vehicle. This review discusses the underlying principles of various wireless charging technologies, including inductive charging, resonant charging, and capacitive charging. Furthermore, it examines the key components and system architectures involved in wireless EV charging systems. The paper also addresses the technical challenges such as efficiency, alignment, safety, and interoperability that must be overcome to realize the widespread deployment of wireless charging infrastructure. Additionally, the regulatory and standardization efforts governing wireless EV charging are discussed, along with their implications for market adoption. Finally, the review provides insights into future research directions and potential applications of wireless EV charging technology, including dynamic charging systems and integration with smart grid networks. Overall, this paper serves as a valuable resource for researchers, engineers, and policymakers interested in understanding the current state and future prospects of wireless electric vehicle charging.

The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Survey

The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Survey

Fault Diagnosis Based on Tensor Computing and Meta-Learning for Smart Grid and Power Communication Network

Fault diagnosis (FD) is a critical challenge for the smart grid and the power communication network, especially when both heterogeneous networks are exponentially becoming enormous and complicated. Consequently, some conventional FD schemes based on labor seem inefficient, even disabled, because they usually cannot efficiently utilize multi-dimensional and heterogeneous big data from both networks. To deal with this challenging technical problem, a novel FD scheme based on tensor computing and meta-learning is proposed for the smart grid and the power communication network. In the proposed scheme, tensor computing is used to process tensor big data from both networks, and a new data fusion scheme is designed to complete and analyze the incomplete and sparse big data. Based on the fused data, a meta-learning approach is used to construct the FD scheme, especially when the target fault samples are inadequate and sparse. In meta-learning, the convolutional neural network is employed as a base learner to generate an FD training model, and the model-agnostic meta-learning algorithm is utilized to fine-tune and further train the pre-trained model. Simulation results and theoretical analysis indicate that the proposed DF scheme based on tensor computing can efficiently process sparse and heterogeneous big data from both networks. Furthermore, the meta-learning-based FD scheme provides an efficient way to diagnose faults with inadequate target samples. The proposed FD scheme based on tensor computing and meta-learning provides a novel solution to detect and analyze the potential faults for smart grid and power communication networks.

PF-AKA: PUF-FSM based Authentication and Key Agreement Framework for IoT based Smart Grid Networks

PF-AKA: PUF-FSM based Authentication and Key Agreement Framework for IoT based Smart Grid Networks