Smart Grid Applications Research Articles

Hyperparameter Optimization with Genetic Algorithms and XGBoost: A Step Forward in Smart Grid Fraud Detection.

This study provides a comprehensive analysis of the combination of Genetic Algorithms (GA) and XGBoost, a well-known machine-learning model. The primary emphasis lies in hyperparameter optimization for fraud detection in smart grid applications. The empirical findings demonstrate a noteworthy enhancement in the model's performance metrics following optimization, particularly emphasizing a substantial increase in accuracy from 0.82 to 0.978. The precision, recall, and AUROC metrics demonstrate a clear improvement, indicating the effectiveness of optimizing the XGBoost model for fraud detection. The findings from our study significantly contribute to the expanding field of smart grid fraud detection. These results emphasize the potential uses of advanced metaheuristic algorithms to optimize complex machine-learning models. This work showcases significant progress in enhancing the accuracy and efficiency of fraud detection systems in smart grids.

Novel Meta-Fractal Wearable Sensors and Antennas for Medical, Communication, 5G, and IoT Applications

Future communication, 5G, medical, and IoT systems need compact, green, efficient wideband sensors, and antennas. Novel linear and dual-polarized antennas for 5G, 6G, medical devices, Internet of Things (IoT) systems, and healthcare monitoring sensors are presented in this paper. One of the major goals in the evaluation of medical, 5G, and smart wireless communication devices is the development of efficient, compact, low-cost antennas and sensors. Moreover, passive and active sensors may be self-powered by connecting an energy-harvesting unit to the antenna to collect electromagnetic radiation and charge the wearable sensor battery. Wearable sensors and antennas can be employed in smart grid applications that provide communication between neighbors, localized management, bidirectional power transfer, and effective demand response. A low-cost wearable antenna may be developed by etching the printed feed and matching the network on the same substrate in the printed antenna. Active modules may be placed on the same dielectric board. The antenna design parameters and a comparison between the computation and measured electrical performance of the antennas are presented in this paper. The electrical characteristics of the new compact antennas in the vicinity of the patient’s body were simulated by using electromagnetic simulation techniques. Fractal and metamaterial efficient antennas and sensors were evaluated to maximize the electrical characteristics of smart communication and medical devices. The dual- and circularly polarized antennas developed in this paper are crucial to the evaluation of wideband and multiband compact 5G, 6G, and IoT advanced systems. The new efficient sensors and antennas maximize the system’s dynamic range and electrical characteristics. The new efficient wearable antennas and sensors are compact, wideband, and low-cost. The operating resonant frequency of the metamaterial antennas with circular split-ring resonators (CSRRs) may be 5% to 9% lower than the resonant frequency of the sensor without CSRRs. The directivity and gain of the metamaterial fractal antennas with CSRRs may be up to 3 dB higher than the antennas without CSRRs. The directivity and gain of the metamaterial fractal passive sensors with CSRRs may be up to 8.5 dBi. This study presents new wideband active meta-fractal antennas and sensors. The bandwidth of the new sensors is around 9% to 20%. At 2.83 GHz, the receiving active sensor gain is 13.5 dB and drops to 8 dB at 3.2 GHz. The receiving module noise figure with TAV541 LNA is around 1dB.

Incremental Coding for Real-Time Remote Control over Bandwidth-Limited Channels and Its Applications in Smart Grids.

Remote control over communication networks with bandwidth-constrained channels has attracted considerable recent attention because it holds the promise of enabling a large number of real-time applications, such as autonomous driving, smart grids, and the industrial internet of things (IIoT). However, due to the limited bandwidth, the sub-packets or even bits have to be transmitted successively, thereby experiencing non-negligible latency and inducing serious performance loss in remote control. To overcome this, we introduce an incremental coding method, in which the actuator acts in real time based on a partially received packet instead of waiting until the entire packet is decoded. On this basis, we applied incremental coding to a linear control system to obtain a remote-control scheme. Both its stability conditions and average linear-quadratic-Gaussian-(LQG) cost are presented. Then, we further investigated a multi-user remote-control method, with a particular focus on its applications in the demand response of smart grids over bandwidth-constrained communication networks. The utility loss due to the bandwidth constraint and communication latency are minimized by jointly optimizing the source coding and real-time demand response. The numerical results show that the incremental-coding-aided remote control performed well in both single-user and multi-user scenarios and outperformed the conventional zero-hold control scheme significantly under the LQG metric.

Comparative Study of Current Controllers for Shunt Active Power Compensators used in Smart Grid Applications

This paper presents a comparative study among different current controllers implemented in a Shunt Active Power Compensator (SAPC) to be used in smart grid applications. During the operation of a smart grid, the SAPC should deliver sinusoidal or non-sinusoidal currents to the Point of Common Coupling (PCC), and this current exchange must be precisely controlled. Three current controllers are analysed in this paper. The two first controllers are a hysteresis band controller and a feedforward proportional controller. Both controllers are designed to work in stationary reference frame. The third one is a PI controller implemented in a synchronous reference frame. The results show that the synchronous reference frame PI controller is unable to track currents with a significant harmonic distortion. Conversely, the behavior of the two stationary reference frame controllers remains unaffected by a high harmonic content in the reference currents. Finally, the results also show that among them, the hysteresis bandwidth controller presents the best performance with similar power losses.

Characterization of Non Intentional Conducted Emissions Up to 500 kHz in Urban Environment

The current interest in Europe to extend the frequency range for Narrowband Power Line Communications up to 500 kHz requires a thorough characterization of the non intentional emissions in this frequency range. This paper presents results of field measurements carried out in a dense urban environment, where the electrical devices are quite numerous and located at a short distance from the smart meters, and therefore, the effects of the non-intentional emissions on Smart Grids communications may be noticeable. In the study, spectral and time characterization of the recorded data was performed according to CISPR specifications, and then compared to recommended limits for non-intentional emissions in this frequency range. Results show that the levels of the emissions above 150 kHz may exceed the recommended limits for spurious emissions in the frequency band, mainly near the smart metering devices, due to the nearness of different types of electrical devices connected to the grid. This work demonstrates the need of characterizing the non-intentional emissions up to 500 kHz for the proper performance of future Smart Grid applications in this frequency range.

Real-Time Co-Simulation for the Analysis of Cyber Attacks Impact on Distance Relay Backup Protection

Smart Grid is a cyber-physical system that incorporates Information and Communication Technologies (ICT) into the physical power system, which introduces vulnerabilities to the grid and opens the door to cyber attacks. Wide area protection is one of the most important smart grid applications that aims at protecting the power system against faults and disturbances, which makes it an attractive target to cyber attacks, aiming at compromising the reliability of the power system. Understanding the interaction between the cyber and physical components of the smart grid and analyzing the damage that cyber-attacks can do to wide area protection is very important as it helps in developing effective mitigation approaches. This paper evaluates the impact of cyber attacks on a wide area distance relay backup protection scheme in real-time, through the development of a co-simulation platform based on Real Time Digital Simulator (RTDS) and network simulator 3 (NS3) and using the IEEE-14 bus power system model.

A lightweight smart contracts framework for blockchain‐based secure communication in smart grid applications

AbstractEnergy is a crucial need in today's world for powering homes, businesses, transportation, and industrial processes. Fossil fuels, such as oil, coal, and natural gas, have been the primary sources of energy for decades. However, there is growing recognition of the negative environmental impact of fossil fuels and the need to transition to cleaner and more sustainable sources of energy. Distributed Energy Resources , such as wind and solar offer several benefits including, reducing energy costs, increasing resiliency, and decreasing carbon emissions. However, the integration of () into the grid requires advanced communication and secure control strategies to ensure a stable and reliable grid operations. In this regard, a blockchain‐based industrial wireless sensor network can provide secure and resilience data transmission to facilitate intelligent integration, monitoring, and control of in the smart grid. In this research, a smart contracts framework in Solana called Advanced Solana Blockchain () is proposed for in the smart grid. The proposed scheme enables resilient and secure real‐time control and monitoring of in smart grids. The performance evaluations and security analysis illustrated that this scheme is secure, reliable, and suitable in terms of lightweight data sharing between in smart grids.

RETINA: Distributed and secure trust management for smart grid applications and energy trading

The rapid adoption of smart grids demands robust security and efficiency measures due to their critical role in delivering electricity and their potential for customer-oriented benefits. This article presents an innovative framework, named RETINA, which provides a resilient and secure energy trading mechanism within smart grid systems. RETINA tackles the inherent security and infrastructure challenges in smart grids by establishing a trust-based security layer and facilitating energy transactions through blockchain technology. Our proposed solution integrates Public Key Infrastructure (PKI) and the Web of Trust (WoT) concepts, promoting decentralized communication channels and robust key management. We further introduce a smart contract-based energy trading mechanism that factors trust, distance, and energy type (green or non-green) in the cost calculation. The utility and robustness of RETINA have been validated in a virtualized testbed environment with 500 nodes, demonstrating superior performance in terms of scalability and resilience compared to the existing WoT scheme. Furthermore, RETINA successfully enables a secure and efficient energy trading scheme, promoting the use of renewable energy sources and promptly recovering from market manipulation attacks. Future enhancements will include applying it to a realistic smart grid deployment and integrating additional functionalities. This groundbreaking solution can potentially revolutionize the smart grid ecosystem, addressing its current limitations and propelling the industry toward a future of advanced and secure energy exchange.

A Robust Learning Framework for Smart Grids in Defense Against False-Data Injection Attacks

With the rapid development of the application of smart grids in different sectors, security management has become a major concern due to cyber attack risks. Correctly and accurately estimating the real status of a smart grid under false-data injection attacks (FDIAs) is currently an emerging concern. In response to that concern, this work proposes a distributed robust learning framework for the inference of the working status under data integrity attacks. The proposed paradigm incorporates the technology median-of-means that enables identifying the correct state against various kinds of FDIAs that can efficiently prevent misleading information during the decision-making process in control centers. Compared with existing defense methods, our method is entirely data driven without training data, highly accurate, and reliable for wide-spectrum FDIAs. More important, it is capable of defending large-scale power electronic networks due to its distributed learning framework. Extensive experimental results demonstrate that our approach can provide efficient protection for Photovoltaic (PV) systems from FDIAs.

Research on power data analysis and its mining technology in smart grid

Abstract The application of smart grids and other systems generates a large amount of data, which is of great value to the research in electric power. This paper analyzes and mines the electric power data in a smart grid through data mining technology to provide technical and data support for analyzing users’ electricity consumption behavior, grid load prediction, and power dispatch optimization. The user electricity feature selection algorithm is built using the mRMR criterion, and it is combined with the improved fuzzy C-mean algorithm to categorize and analyze the user’s electricity consumption behavior. The LSTM algorithm is used in this paper to forecast power load in the smart grid due to the ability of long and short-term memory networks to handle long-term dependencies. The objectives for optimizing the power system scheduling, such as minimizing pollution emissions, are selected, and the optimal solution is calculated using a genetic algorithm. The feature selection algorithm evaluates the user’s electricity consumption characteristics, combines the clustering algorithm to compare the internal metrics of the two feature selections, and classifies the user’s electricity consumption behavior into six categories according to the electricity consumption characteristics. The accuracy of the LSTM algorithm’s prediction of the grid load reaches 74.19% on the validation set, and the mean square error is 0.881. In the final optimal solution obtained for power dispatch optimization, the pollution emission is only 8.93 kgCO2/H, and customer satisfaction can reach 74.89%.

Power Line Monitoring-Based Consensus Algorithm for Performance Enhancement of Energy Blockchain Applications in Smart Grid 2.0

Power Line Monitoring-Based Consensus Algorithm for Performance Enhancement of Energy Blockchain Applications in Smart Grid 2.0

Distributed Strategy Design for Multicoalition Games With Autonomous High-Order Players and Its Application in Smart Grids

Distributed Strategy Design for Multicoalition Games With Autonomous High-Order Players and Its Application in Smart Grids

Analysis and Modeling of Direct Ammonia Fuel Cells for Solar and Wind Power Leveling in Smart Grid Applications

Analysis and Modeling of Direct Ammonia Fuel Cells for Solar and Wind Power Leveling in Smart Grid Applications

Performance analysis of solid-state batteries in Electric vehicle applications

In recent years solid-state batteries, have emerged leading research focus, due to their distinct advantages over conventional batteries. Solid-state batteries, having solid electrolytes, offer higher energy and power density, enhanced safety features and longer lifespan. This makes them ideal in fulfilling demand for energy storage in electric vehicle, and smart grid applications. This study aims to evaluate various types of solid-state batteries, analysing their properties, advantages, and disadvantages, assessing their viability in EV applications. The objective is to identify and recommend the most effective solid-state battery that aligns with the specific demands and operational conditions of electric vehicles and conduct a comprehensive analysis of anode and cathode elements of one of the solid-state batteries in their fresh and damaged state using scanning electron microscopy (SEM).

A Machine Learning-based Solution for Monitoring of Converters in Smart Grid Application

A Machine Learning-based Solution for Monitoring of Converters in Smart Grid Application

Research progress of flexible energy storage dielectric materials with sandwiched structure

<sec>Polymer dielectric materials show wide applications in smart power grids, new energy vehicles, aerospace, and national defense technologies due to the ultra-high power density, large breakdown strength, flexibility, easy processing, and self-healing characteristics. With the rapid development of integration, miniaturization and lightweight production of electronic devices, it is required to develop such a storage and transportation dielectric system with larger energy storage density, higher charge and discharge efficiency, good thermostability and being environmentally friendly. However, the contradiction between dielectric constant and breakdown strength of dielectric materials is the key factor and bottleneck to obtain a high performance dielectric material. It is accepted that controlling charge distribution and inhibiting charge carrier injection are important to improve the energy storage characteristics of polymer dielectrics. In recent years, the materials with sandwiched or stacking structures have demonstrated outstanding advantages in inhibiting charge injection and promoting polarization, allowing polymer dielectrics to have increased permittivity and breakdown strength at the same time. Therefore, from the perspectives of material composition, structural design, and preparation methods, this study reviews the research progress of polymer dielectric films with sandwiched structure in improving the energy storage performance. The influence of dielectric polarization, charge distribution, charge injection, interfacial barrier and electrical dendrite growth on the energy storage performance and the synergistic enhancement mechanisms in such sandwich-structured dielectric materials are systematically summarized, showing good development and vast application prospects.</sec><sec>In brief, introducing easy polarization, wide-gap and deep-trap nanofillers has greater designability and regulation in the dielectric and breakdown properties. In addition, using the hard layer as the outer layer can reduce charge injection more effectively, resulting in a high breakdown resistance performance that is easy to achieve. The sandwiched structure design also possesses advantages over other methods in maintaining good flexibility and dielectric stability of dielectric materials, thus having become a hot-topic research area in recent years. In the future, it is necessary to combine low conductivity and high thermal conductivity of dielectric polymers to realize high temperature energy storage and efficiency. Researches on recyclable, self-repairing sandwiched insulating films are good for the service life and safety of electronic components and will further expand the application scope of dielectric polymers. Finally, effective evaluation of dielectric with sandwiched structure and energy storage performances through simulation and theoretical modeling is very helpful in revealing the breakdown mechanism and thermal failure mechanism, and also in theoretically guiding the design of polymer dielectric materials.</sec>

Fractional Order Modeling of Lithium-ion Batteries For A Real Smart Grid System

Establishing robust, accurate, and low-complexity battery models is crucial for Smart Grid applications to effectively utilize and manage lithium-ion batteries and estimate battery states. This paper analyzes lithium-ion battery electrochemical impedance spectrograms and integrates impedance elements with fractional-order characteristics into Thevenin circuit models based on fractional calculus theory. These models can be embedded in microprocessors for accurate real-time results. A time domain identification procedure minimizes Integral Absolute Error, yielding simple, high-accuracy fractional order modeling of Energy Storage Systems in Real Smart Grids, with a very acceptable average Nrmse index of fit.

Real-time Access and Processing of Massive Measurement Data in Smart Power Grids

Abstract This paper introduces a comprehensive smart grid big data solution, focusing on the processing and analysis of vast grid data to facilitate critical applications such as data resource management, real-time monitoring of grid conditions, and predictive load forecasting. Specifically, grid monitoring data are routed to distributed message queues, enhancing the indexing speed of real-time data access via the implementation of a B+ tree indexing algorithm. Furthermore, an optimized ant colony algorithm enhances the integration of big data with other advanced technologies, enabling efficient classification of diverse power information from multiple metering data sources. For empirical validation, data from national grid power meters were analyzed. Correlation analysis revealed that the correlation coefficients among smart meters 1, 5, and 15 are predominantly higher than 0.9. These coefficients tend to become more pronounced with time, delineating clearer connections and distinctions among the data from these meters. Additionally, the correlation between temperature and load values ranged between 0.91 and 0.98, significantly influencing daily load forecasts. The year 2023 saw an increase in the detection of online monitoring faults by 236 compared to 2020, underscoring the enhanced capabilities of smart grid condition maintenance. Moreover, monitoring data from various nodes of the national grid, with the exception of node 1#, exhibited deviation values ranging from 0.01 to 0.05, indicating high monitoring precision. In conclusion, the big data-driven approach to smart grid management presented in this study not only predicts load and performs state inspections efficiently but also holds significant practical value, suggesting a robust framework for future smart grid applications.

Bio-Inspired Design of Future Solar Power Systems for Smart Grid Applications

Bio-Inspired Design of Future Solar Power Systems for Smart Grid Applications

Privacy and security issues in smart grids: A survey

Smart grids have emerged as a transformative technology in the energy sector, enabling efficient electricity management, improved reliability, and integration of renewable energy sources. The necessity to promote smart grid (SG) has been recognized with a strong consensus. The SG integrates electrical grids and communication infrastructures and forms an intelligent electricity network working with all connected components to deliver sustainable electricity supplies. Many advanced communication technologies have been identified for SG applications with a potential to significantly enhance the overall efficiency of power grids. However, the widespread deployment of smart grids raises concerns about the privacy and security of the data collected and transmitted by these systems. To address these concerns, this paper proposes a comprehensive framework for ensuring privacy and security in smart grid systems. This framework includes encryption techniques, access control mechanisms, and robust authentication protocols. Additionally, this paper discusses the importance of user awareness and education in mitigating privacy and security risks. The research contributes to the existing literature on smart grid privacy and security by providing insights specific to the information technology security domain. The findings of this manuscript will be valuable for policymakers, energy providers, and researchers working towards the development of secure and privacy-preserving smart grid systems.