Smart Grid Network Research Articles

Hen maternal care inspired optimization framework for attack detection in wireless smart grid network

In the power grid, communication networks play an important role in exchanging smart grid-based information. In contrast to wired communication, wireless communication offers many benefits in terms of easy setup connections and low-cost high-speed links. Conversely, wireless communications are commonly more vulnerable to security threats than wired ones. All power equipment devices and appliances in the smart distribution grid (SDG) are communicated through wireless networks only. Most security research focuses on keeping the SDG network from different types of attacks. The denial-of-service (DoS) attack is consuming more energy in the network leads to a permanent breakdown of memory. This work proposes a new metaheuristic optimization inspired by maternal care of hen to their children called hen maternal care (HMCO) inspired optimization. The HMCO algorithm mimics the care shown by hen for their children in nature. The mother hen is always watchful and protects its chicks against predators. All chickens utilize different calls to designate flying predators like falcons and owls from ground seekers like foxes and coyotes, showing that they can both survey a danger and advise different chickens how to set themselves up. Our method shows greater performance among other standard algorithms.

Renewable energy management in smart grid with cloud security analysis using multi agent machine learning model

Machine learning makes it possible for challenging jobs to be finished totally on their own. Computers as well as mobile devices may make it simpler to regulate interior temperature, keep an eye on security, and carry out periodic maintenance in a smart grid (SG). The smart grids' capacity to detect cyber attacks is crucial for determining the availability and dependability of the operation. Integrity of bogus data cyber attacks in physical layers of smart grids (SGs) is highlighted in this article. This study suggests a unique method for managing the energy usage of smart grids while analysing the security of data transfer. Here, the energy efficiency of the smart grid network is evaluated using renewable energy sources (SM_RES), and the network has been watched for security evaluation in cloud computing. Multi agent Markov reinforcement learning (MAMRL) is used to analyse the security of the cloud network in the smart grid. In terms of scalability, QoS, energy efficiency, power consumption, prediction accuracy, RMSE, average precision, and data integrity, experimental study is conducted for energy management and network security. Proposed technique attained prediction accuracy 95 %, packet loss rate 77 %, RMSE 88 %, Average precision 85 %, network security analysis 97 %; scalability 95.8 %, QoS 86 %, Power consumption 35 % and energy efficiency 96 %.

A survey on lattice‐based security and authentication schemes for smart‐grid networks in the post‐quantum era

SummaryThe present scenario witnesses “the second quantum revolution,” which has enabled the development of revolutionary novel quantum tools. Quantum computing endeavors to establish higher computing standards that can potentially solve complex structures. Post‐quantum cryptography (PQC) has emerged as one of the new domains of cryptography, which is resilient to quantum attacks owing to the revolution in quantum computing. To ensure quantum security, the lattice‐based cryptosystem (LB‐cryptosystem) is one of the promising tools of PQC to address quantum‐based threats. The traditional security algorithms, such as RSA and Diffie–Hellman (DH), are strong enough to resist present security threats. However, it has been predicted that quantum technologies have the capability to break the security of most traditional algorithms whose security is based on prime factorization and DH‐type hard problems. Therefore, research is currently focused on addressing the security and privacy threats by using LB‐cryptosystems to secure various applications, organizations' data, and information infrastructure in the quantum era. The purpose of this article is to investigate recent advances in LB‐cryptosystems that may allow the design of secure models for smart‐grid networks (SGNs) against existing and future quantum attacks. SGNs have been explored as the bi‐directional assimilation of communication in terms of electricity generation, transmission, allocation, and utilization. This survey provides a comprehensive overview of LB‐cryptosystems, as well as their potential applications in securing SGNs. Lastly, the article summarizes the various PQC primitives, NIST selected algorithms, open‐source tools along with their packages, and various PQC industrial initiatives, and also compares traditional cryptographic schemes with other PQC.

Data-driven approach for day-ahead System Non-Synchronous Penetration forecasting: A comprehensive framework, model development and analysis

This article presents a comprehensive, innovative, and data-driven approach for predicting System Non-Synchronous Penetration (SNSP) levels. It consists of iterative steps that involve data analytics and forecasting model development to overcome the challenges associated with forecasting, such as data mining or overfitting. The approach starts by defining the problem domain and identifying relevant features using the Pearson correlation method. The framework ensures that all forecasting models carry out data pre-processing uniformly. The hyperparameters, understood as adjustable external factors not learned during the training process that affect the performance and predictive ability of the forecasting model are optimized using the random search algorithm to enhance the models’ performance. The study compares the performance of classical models, such as Random Forest and Light Gradient Boosting, with advanced machine learning-based models, such as Feed-forward, Gate Recurrent Unit, Short-Term Long Memory, and Convolutional Neural Network. Data from the Irish power system is chosen as a case study. The results indicate that the Feed-forward model produces the lowest errors. It has a Mean Absolute Error of about 4.09, a Root Mean Squared Error of 5.37 and a Mean Absolute Percentage Error of 18.17% respectively. This systematic and practical approach can be applied to other regions with similar challenges. This study also highlights the potential of advanced machine learning-based models in improving SNSP forecasting accuracy. The approach is beneficial for network and market operators, and ancillary service providers in smart grid network operations, with a 15-minute resolution. It provides a promising direction for future research in this area.

Electric vehicle charging navigation strategy in coupled smart grid and transportation network: A hierarchical reinforcement learning approach

Most of the existing electric vehicle (EV) charging navigation methods do not simultaneously take into account the electric vehicle charging destination optimization and path planning. Moreover, they are unable to provide online real-time decision-making under a variety of uncertain factors. To address these problems, this paper first establishes a bilevel stochastic optimization model for EV charging navigation considering various uncertainties, and then proposes an EV charging navigation method based on the hierarchical enhanced deep Q network (HEDQN) to solve the above stochastic optimization model in real-time. The proposed HEDQN contains two enhanced deep Q networks, which are utilized to optimize the charging destination and charging route path of EVs, respectively. Finally, the proposed method is simulated and validated in two urban transportation networks. The simulation results demonstrate that compared with the Dijkstra shortest path algorithm, single-layer deep reinforcement learning algorithm, and traditional hierarchical deep reinforcement learning algorithm, the proposed HEDQN algorithm can effectively reduce the total charging cost of electric vehicles and realize online real-time charging navigation of electric vehicles, that shows excellent generalization ability and scalability.

Transmission line switching spread modelling of synchrophasor smart grid network with load uncertainty inspired by epidemic model: a study of reliability and vulnerability

ABSTRACT Monitoring and controlling of transmission lines become easier in real time with synchrophasor measurements technology. Phasor Measurement Units (PMUs) provide the time-stamped synchrophasor data of the system buses and transmission lines with a high reporting rate. In this paper, a mathematical model is implemented that allows the simulation of the characteristics of the spread of transmission line switching with load variations. In the model, the transmission lines are divided into three parts defined as susceptible, infected and recovered transmission lines inspired from epidemic model. In this paper, Reliability Test System (RTS − 1979) is divided into three zones to simulate the line switching spread behaviour with load variation for reliability and vulnerability analysis of the synchrophasor smart grid network. The anomaly exists with synchrophasor measuring devices due to cyber intrusion in communication network or failure of devices makes the system observable with the characterization of vulnerable behaviour of transmission lines.

Toward Wireless Smart Grid Communications: An Evaluation of Protocol Latencies in an Open-Source 5G Testbed

Fifth-generation networks promise wide availability of wireless communication with inherent security features. The 5G standards also outline access for different applications requiring low latency, machine-to-machine communication, or mobile broadband. These networks can be advantageous to numerous applications that require widespread and diverse communications. One such application is found in smart grids. Smart grid networks, and Operational Technology (OT) networks in general, utilize a variety of communication protocols for low-latency control, data monitoring, and reporting at every level. Transitioning these network communications from wired Wide Area Networks (WANs) to wireless communication through 5G can provide additional benefits to their security and network configurability. However, introducing these wireless capabilities may also result in a degradation of network latency. In this paper, we propose utilizing 5G for smart grid communications, and we evaluate the latency impacts of encapsulating GOOSE, Modbus, and DNP3 for transmission over a 5G network. The OpenAirInterface open-source library is utilized to deploy an in-lab 5G Core Network and gNB for testing with off-the-shelf User Equipment (UE). This creates an effective 5G test platform for experimenting with different OT protocols such as GOOSE. The results are validated by measuring two different Intelligent Electronic Devices’ contact closure times for each network configuration. These tests are also conducted for varying packet sizes in order to isolate different sources of network latency. Our study outlines the latency impact of communication over 5G for time-critical and non-critical applications regarding their transition toward private 5G-based OT network implementations. The conducted experiments illustrate that in the case of GOOSE packets, simple encapsulation may exceed the protocol’s time-critical nature, and, therefore, additional measures must be taken to ensure a viable transition of GOOSE to 5G services. However, non-critical applications are shown to be viable for migration to 5G.

DDoS attack detection in smart grid network using reconstructive machine learning models.

Network attacks pose a significant challenge for smart grid networks, mainly due to the existence of several multi-directional communication devices coupling consumers to the grid. One of the network attacks that can affect the smart grid is the distributed denial of service (DDoS), where numerous compromised communication devices/nodes of the grid flood the smart grid network with false data and requests, leading to disruptions in smart meters, data servers, and the state estimator, ultimately effecting the services for end-users. Machine learning-based strategies show distinctive benefits in resolving the challenge of securing the network from DDoS attacks. Regardless, a notable hindrance in deploying machine learning-based techniques is the requirement of model retraining whenever new attack classes arise. Practically, disrupting the normal operations of smart grid is really discouraged. To handle this challenge effectively and detect DDoS attacks without major disruptions, we propose the deployment of reconstructive deep learning techniques. A primary benefit of our proposed technique is the minimum disruption during the introduction of a new attack class, even after complete deployment. We trained several deep and shallow reconstructive models to get representations for each attack type separately, and we performed attack detection by class-specific reconstruction error-based classification. Our technique experienced rigid evaluation via multiple experiments using two well-acknowledged standard databases exclusively for DDoS attacks, including their subsets. Later, we performed a comparative estimation of our outcomes against six methods prevalent within the same domain. Our outcomes reveal that our technique attained higher accuracy, and notably eliminates the requirement of a complete model retraining in the event of the introduction of new attack classes. This method will not only boost the security of smart grid networks but also ensure the stability and reliability of normal operations, protecting the critical infrastructure from ever-evolving network attacks. As smart grid is advancing rapidly, our approach proposes a robust and adaptive way to overcome the continuous challenges posed by network attacks.

Big Data and Neural Networks in Smart Grid - Part 2: The Impact of Piecewise Monotonic Data Approximation Methods on the Performance of Neural Network Identification Methodology for the Distribution Line and Branch Line Length Approximation of Overhead Low-Voltage Broadband over Powerlines Networks

Τhe impact of measurement differences that follow continuous uniform distributions (CUDs) of different intensities on the performance of the Neural Network Identification Methodology for the distribution line and branch Line Length Approximation (NNIM-LLA) of the overhead low-voltage broadband over powerlines (OV LV BPL) topologies has been assessed in [1]. When the αCUD values of the applied CUD measurement differences remain low and below 5dB, NNIM-LLA may internally and satisfactorily cope with the CUD measurement differences. However, when the αCUD values of CUD measurement differences exceed approximately 5dB, external countermeasure techniques against the measurement differences are required to be applied to the contaminated data prior to their handling by NNIM-LLA. In this companion paper, the impact of piecewise monotonic data approximation methods, such as L1PMA and L2WPMA of the literature, on the performance of NNIM-LLA of OV LV BPL topologies is assessed when CUD measurement differences of various αCUD values are applied. The key findings that are going to be discussed in this companion paper are: (i) The crucial role of the applied numbers of monotonic sections of the L1PMA and L2WPMA for the overall performance improvement of NNIM-LLA approximations as well as the dependence of the applied numbers of monotonic sections on the complexity of the examined OV LV BPL topology classes; and (ii) the performance comparison of the piecewise monotonic data approximation methods of this paper against the one of more elaborated versions of the default operation settings in order to reveal the most suitable countermeasure technique against the CUD measurement differences in OV LV BPL topologies.

Big Data and Neural Networks in Smart Grid - Part 1: The Impact of Measurement Differences on the Performance of Neural Network Identification Methodologies of Overhead Low-Voltage Broadband over Power Lines Networks

Until now, the neural network identification methodology for the branch number identification (NNIM-BNI) and the neural network identification methodology for the distribution line and branch line length approximation (NNIM-LLA) have approximated the number of branches and the distribution line and branch line lengths given the theoretical channel attenuation behavior of the examined overhead low-voltage broadband over powerlines (OV LV BPL) topologies [1], [2]. The impact of measurement differences that follow continuous uniform distribution (CUDs) of different intensities on the performance of NNIM-BNI and NNIM-LLA is assessed in this paper. The countermeasure of the application of OV LV BPL topology databases of higher accuracy is here investigated in the case of NNIM-LLA. The strong inherent mitigation efficiency of NNIM-BNI and NNIM-LLA against CUD measurement differences and especially against those of low intensities is the key finding of this paper. The other two findings that are going to be discussed in this paper are: (i) The dependence of the approximation Root-Mean-Square Deviation (RMSD) stability of NNIM-BNI and NNIM-LLA on the applied default operation settings; and (ii) the proposal of more elaborate countermeasure techniques from the literature against CUD measurement differences aiming at improving NNIM-LLA approximations.

Energy Efficient Voltage Scheduling Based Algorithm for Power Factor Maximization in Smart Grid Networks

Energy Efficient Voltage Scheduling Based Algorithm for Power Factor Maximization in Smart Grid Networks

Green Energy Storage Solutions: A Research

One of the key elements of decarbonizing global energy networks and integrating renewable energy sources is green energy storage technology. Energy Storage Systems (ESS), which store surplus produced electricity and make it available on demand, are essential for reducing fluctuations. Electromechanical, electromagnetic, thermodynamic, chemical and hybrid approaches have all been used in the development of energy storage technologies. A comprehensive list of current papers in the literature section is compiled to illustrate the range of advancements in this field. This paper reviews green energy storage systems, focusing on their primary uses. Power utilities will benefit from this thorough analysis of energy storage systems; the researchers choose the finest and newest energy storage technology based on its practicality and affordability. These days, several nations use energy storage systems to plan for future energy needs. Variations in solar radiation cause a solar photovoltaic generator to overproduce electricity. The implementation of a hybrid energy storage system would help to increase the reliability of solar-powered power generation. The microgrid is a crucial component of the smart grid network for solar installations. This study looks at the microgrid's energy storage system for photovoltaic systems. The topologies and storage system configurations of the microgrid are analyzed together with power electronic interference, control systems, and optimization of the energy storage system and renewable sources. a general technique for sizing the HESS of PV systems using design space as well as pinch analysis. HESS scales that link generator ratings to storage capacity are developed in the proper sizes by using pinch analysis to load and resources data.

DRL-Based Online Task Offloading and Energy Resource Aggregation for Edge Computing Empowered Smart Grid Networks

DRL-Based Online Task Offloading and Energy Resource Aggregation for Edge Computing Empowered Smart Grid Networks

A Reinforcement Learning Congestion Control Algorithm for Smart Grid Networks

A Reinforcement Learning Congestion Control Algorithm for Smart Grid Networks

PowerX: A Probabilistic Graph Model for Complex Smart Grid Networks

PowerX: A Probabilistic Graph Model for Complex Smart Grid Networks

SGAK: A Robust ECC based Authenticated Key Exchange Protocol for Smart Grid Networks

SGAK: A Robust ECC based Authenticated Key Exchange Protocol for Smart Grid Networks

Channel Access in Wireless Smart Grid Networks Operating under ETSI Frame-Based Equipment Rules

Smart grid operators seeking to extend their wireless network capacity can use unlicensed bands. However, devices in these shared bands must follow rules such as Listen Before Talk (LBT), standardized by ETSI. In this paper, we focus on the performance of the frame-based equipment (FBE) version of LBT channel access. We design, implement, and validate a fully functional FBE channel access simulator. Next, we conduct an extensive performance analysis of the FBE variants encountered in the literature, focusing on channel efficiency and fairness in upper-bound and coexistence scenarios. Our study leads to several conclusions about the operation of FBE-based devices, including the need for proper configuration of channel access parameters to ensure fairness and optimal performance. We also observe generally poor coexistence among FBE variants: the highest Jain’s fairness index was only 0.88, with an average normalized channel efficiency of 0.76. Therefore, we identify several open research areas in the field, such as the need for further development of parameter adaptation algorithms, the deployment of an external controller to update channel access parameters, and new FBE designs with better coexistence qualities.

Fault Detection and Isolation in Smart-Grid Networks of Intelligent Power Routers Modeled as Probabilistic Boolean Networks

A self-organizing complex-network modeling method, probabilistic Boolean networks, is presented as a model-based diagnostic system for detecting and isolating different types of faults, failures, and modes of operation in which a network of intelligent power routers is deployed over a standard power test case: the Western System Coordinating Council 9 Bus System. Such a system allows designers and engineering professionals to make educated decisions pertaining to the design of smart-grid systems endowed with intelligent power routers. There is a recurrent necessity to design reliable and fault-tolerant smart power systems, maintaining adequate operation and adherence to performance specifications, while keeping costs at the minimum. This diagnostics system will help achieve such goals: better design through thorough analysis of the conditions that lead to a fault on a smart grid, proper detection of these faults, and isolation of the respective assets.

Fog Assisted Personalized Dynamic Pricing for Smartgrid

Unit electricity pricing is of vital importance in an electric grid network. It is essential to charge the customers in a fair manner. Traditional pricing models are found to be inadequate in the ability to charge customers fairly due to a lack of support for real-time communication between customers and electricity providers. With the introduction of smart devices in the electric grid domain, the real-time gathering of information is a seamless process. Such an electric network that uses smart devices is called a smart grid. In a smart grid network, electricity providers can monitor the electricity usage pattern of customers in a real-time manner, which can then be analyzed to determine the appropriate prices. To analyze the customer’s history of usage and price the electricity in a real-time manner, the computation must be performed with minimal latencies. Adoption of a fog computing layer in the smart grids can aid in the attainment of this goal. In this article, we propose a novel method for the pricing of electricity. In our approach, the electric demand of a household is predicted based on their past usage patterns. Users are then clustered into different bins based on their demands, and an evolutionary algorithm is used to generate the prices for the users present in different bins in a real-time manner to ensure the maximum attainable profit to a service provider.

Container Migration Strategy Based on Multi-objective Optimization for Edge-Cloud Coordination enabled Smart Grids

<p>With the rapid development of information and communication technologies, new services and new applications continue to emerge, especially in smart grid network scenarios. Placing services in terms of containers on the network edge is a promising solution for guaranteeing low latency, large connections, and high bandwidth. In this paper, we propose a multi-objective container migration strategy (MOCMS). Firstly, the container needed to be migrated is selected according to the resource utilization and the energy consumption condition at the network edge. Secondly, in order to avoid the problem of resource fragmentation, the node coordination matrix model is established. Thirdly, in order to obtain the optimal container migration results, an improved Binary Grey Wolf Optimizer (BGWO) algorithm is designed. Finally, the simulation results show that the proposed container migration strategy can perform better than other existing schemes.</p> <p> </p>