Smart Grid Research Articles

SCMABC Algorithm-based routing for secure and efficient data transmission in smart grid AMI networks

SCMABC Algorithm-based routing for secure and efficient data transmission in smart grid AMI networks

Forecasting solar irradiance for the strategic integration of hybrid hydro and solar photovoltaic systems in rural Indian regions

ABSTRACT Conversion of the conventional electrical grid into a smart and sustainable grid involves several considerations. The primary factors, however, are renewable energy penetration, associated storage systems, and energy generation costs. This research endeavors to conduct a thorough survey and analysis of the solar irradiance on various hydropower locations in India, including Run-of-River (RoR), Run-of-River with Pondage (RoRP), Reservoir Storage (S), Multi-Purpose Storage (MP) and Pumped Storage Systems (PSS). The hydroelectric projects in rural Indian regions have been the subject of the proposed case study. As a preliminary study, the probabilistic variables like minimum, maximum, and mean solar irradiance are calculated for 252 High-Scale Hydropower Plant locations (HSHPs) using the past 40 years day ahead solar radiation data to identify the high-irradiance hydropower plant location in each state of India. This study concludes that the maximum mean solar irradiance location in each state as these sites are well suited for hybrid PV-hydro systems. The identified high-irradiance locations 40 years day ahead data sets are analyzed employing 8 machine learning models and 2 deep learning models. This analysis aims to forecast solar irradiance, serving as a crucial foundation for the initial phase of the implementation of hybrid PV-hydro.

Regional Load Forecasting Scheme for Security Outsourcing Computation

Smart grids generate an immense volume of load data. When analyzed using intelligent technologies, these data can significantly improve power load management, optimize energy distribution, and support green energy conservation and emissions reduction goals. However, in the process of data utilization, a pertinent issue arises regarding potential privacy leakage concerning both regional and individual user power load data. This paper addresses the scenario of outsourcing computational tasks for regional power load forecasting in smart grids, proposing a regional-level load forecasting solution based on secure outsourcing computation. Initially, the scheme designs a secure outsourcing training protocol to carry out model training tasks while ensuring data security. This protocol guarantees that sensitive information, including but not limited to individual power consumption data, remains comprehensively safeguarded throughout the entirety of the training process, effectively mitigating any potential risks of privacy infringements. Subsequently, a secure outsourcing online prediction protocol is devised, enabling efficient execution of prediction tasks while safeguarding data privacy. This protocol ensures that predictions can be made without compromising the privacy of individual or regional power load data. Ultimately, experimental analysis demonstrates that the proposed scheme meets the requirements of privacy, accuracy, and timeliness for outsourcing computational tasks of load forecasting in smart grids.

Developmental Trajectories of Electric Vehicle Research in a Circular Economy: Main Path Analysis

This study explored the development history and future trends of academic research on electric vehicles (EVs) in a circular economy. We collected 4127 articles on circular economy and EVs from the Web of Science database, and main path analysis indicated that academic research in the field of EVs in a circular economy has covered the following topics in chronological order: EVs as a power resource; vehicle-to-grid (V2G) technology; renewable energy and energy storage grids; smart grid and charging station optimization; and sustainable development of energy, water, and environmental systems. Through cluster analysis and data mining, we identified the following main research topics in the aforementioned field: recycling and reuse of EV batteries, charging stations and energy management, V2G systems and renewable energy, power frequency control systems, dynamic economic emissions, and energy management. Finally, data mining and statistical analysis revealed the following emerging research topics in this field from 2020 to 2023: microgrids, deep learning, loop supply chain, blockchain, and automatic generation control. Various achievements have been attained in research on EVs in a circular economy; however, challenges related to aspects such as sustainable battery recycling charging infrastructure and renewable energy integration remain.

Harnessing the Power of Renewable Energy: A Study of Sustainable Sources and Technologies

This research investigates the capacity of renewable energy technologies to fulfill global energy requirements while mitigating environmental effects, concentrating on primary sources like sun, wind, hydropower, biomass, and geothermal energy. It evaluates the efficacy of various technologies for energy production, efficiency, economic viability, and reduction of carbon emissions. The findings underscore notable progress in solar and wind energy, demonstrating considerable efficiency enhancements (5% for solar PV, 10% for wind turbines) and cost reductions (67% and 64%, respectively) during the last decade. Hydropower is the most efficient energy source, converting 90% of available energy, albeit it is geographically constrained. The research highlights the environmental advantages, noting that renewable technologies substantially decrease CO₂ emissions—hydropower and wind energy prevent 250,000 and 120,000 metric tons of CO₂ annually, respectively. Nonetheless, obstacles such as the variability of solar and wind energy, elevated expenses associated with biomass and geothermal energy, and regional variations in implementation persist as substantial impediments. The research highlights the need for further developments in energy storage, grid modernization, and legislative assistance to expedite the worldwide shift towards renewable energy. This study synthesizes current statistics and trends, emphasizing the vital role of renewable energy in attaining a sustainable, low-carbon future, while also tackling ongoing economic and infrastructural problems.

Laboratory Tests in Cyber-Physical Mode for an Energy Management System Including Renewable Sources and Industrial Symbiosis

Abstract The aim of this paper regards the laboratory validation of an energy management system (EMS) for an industrial site on the Eigerøy island (Norway). It will be the demonstration district in the ROBINSON project for a consequent concept replication. This activity in cyber-physical mode is an innovative approach to finalize the EMS tool with real measurement data with prime movers available at laboratory level, considering the necessary EMS robustness and flexibility for replication on other industrial islands. This EMS was designed and developed to minimize variable costs, producing on/off and set-point signals that, through a model predictive control (MPC) software, establish the system status. This smart grid includes renewable sources (e.g., solar panels, a wind turbine, and syngas) and traditional prime movers, such as a steam boiler for the industry needs. Moreover, an energy storage device is installed composed of an electrolyzer with a hydrogen pressure vessel. The main results reported in this work regard 26-h tests performed in cyber-physical mode thanks to the real-time interaction of hardware and software. So, a real microturbine and real photovoltaic (PV) panels were managed by the EMS in conjunction with software models for components not physically present in the laboratory. Although the optimization target was cost minimization, significant improvement was also obtained in terms of efficiency increase and CO2 emission decrease.

Detection Method for Three-Phase Electricity Theft Based on Multi-Dimensional Feature Extraction.

The advent of smart grids has facilitated data-driven methods for detecting electricity theft, with a preponderance of research efforts focused on user electricity consumption data. The multi-dimensional power state data captured by Advanced Metering Infrastructure (AMI) encompasses rich information, the exploration of which, in relation to electricity usage behaviors, holds immense potential for enhancing the efficiency of theft detection. In light of this, we propose the Catch22-Conv-Transformer method, a multi-dimensional feature extraction-based approach tailored for the detection of anomalous electricity usage patterns. This methodology leverages both the Catch22 feature set and complementary features to extract sequential features, subsequently employing convolutional networks and the Transformer architecture to discern various types of theft behaviors. Our evaluation, utilizing a three-phase power state and daily electricity usage data provided by the State Grid Corporation of China, demonstrates the efficacy of our approach in accurately identifying theft modalities, including evasion, tampering, and data manipulation.

Trust-Based Detection and Mitigation of Cyber Attacks in Distributed Cooperative Control of Islanded AC Microgrids

In this study, we address the challenge of detecting and mitigating cyber attacks in the distributed cooperative control of islanded AC microgrids, with a particular focus on detecting False Data Injection Attacks (FDIAs), a significant threat to the Smart Grid (SG). The SG integrates traditional power systems with communication networks, creating a complex system with numerous vulnerable links, making it a prime target for cyber attacks. These attacks can lead to the disclosure of private data, control network failures, and even blackouts. Unlike machine learning-based approaches that require extensive datasets and mathematical models dependent on accurate system modeling, our method is free from such dependencies. To enhance the microgrid’s resilience against these threats, we propose a resilient control algorithm by introducing a novel trustworthiness parameter into the traditional cooperative control algorithm. Our method evaluates the trustworthiness of distributed energy resources (DERs) based on their voltage measurements and exchanged information, using Kullback-Leibler (KL) divergence to dynamically adjust control actions. We validated our approach through simulations on both the IEEE-34 bus feeder system with eight DERs and a larger microgrid with twenty-two DERs. The results demonstrated a detection accuracy of around 100%, with millisecond range mitigation time, ensuring rapid system recovery. Additionally, our method improved system stability by up to almost 100% under attack scenarios, showcasing its effectiveness in promptly detecting attacks and maintaining system resilience. These findings highlight the potential of our approach to enhance the security and stability of microgrid systems in the face of cyber threats.

Research on the Construction of Power System Relay Protection Course System in Electrical Engineering Discipline

Power system relay protection is one of the core courses in the discipline of electrical engineering, which is directly related to the students' professional ability in the field of power system operation and protection. With the rapid development of power system, especially the popularization of smart grid technology, the existing curriculum system gradually exposes problems such as obsolete content, insufficient practical teaching, and disconnection with cutting-edge technology. By analyzing the current situation of the existing curriculum system, this paper proposes key elements such as the combination of theory and practice, the introduction of cutting-edge knowledge, the optimal allocation of teaching resources, and the innovation of teaching methods, and discusses the implementation strategy of curriculum system reform. The study aims to build a relay protection curriculum system that better meets the development needs of modern power systems, and to enhance students' practical ability and innovative thinking to meet the challenges of the future power industry.

Enhancing Solar Power Efficiency: Smart Metering and ANN-Based Production Forecasting

This paper presents a comprehensive and comparative study of solar energy forecasting in Morocco, utilizing four machine learning algorithms: Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), recurrent neural networks (RNNs), and artificial neural networks (ANNs). The study is conducted using a smart metering device designed for a photovoltaic system at an industrial site in Benguerir, Morocco. The smart metering device collects energy usage data from a submeter and transmits it to the cloud via an ESP-32 card, enhancing monitoring, efficiency, and energy utilization. Our methodology includes an analysis of solar resources, considering factors such as location, temperature, and irradiance levels, with PVSYST simulation software version 7.2, employed to evaluate system performance under varying conditions. Additionally, a data logger is developed to monitor solar panel energy production, securely storing data in the cloud while accurately measuring key parameters and transmitting them using reliable communication protocols. An intuitive web interface is also created for data visualization and analysis. The research demonstrates a holistic approach to smart metering devices for photovoltaic systems, contributing to sustainable energy utilization, smart grid development, and environmental conservation in Morocco. The performance analysis indicates that ANNs are the most effective predictive model for solar energy forecasting in similar scenarios, demonstrating the lowest RMSE and MAE values, along with the highest R2 value.

Advances in fuzzy control systems for energy management in smart homes

The drive towards energy efficiency and sustainability has spurred technological innovations for managing energy consumption in residences, with fuzzy control systems standing out. These systems, based on fuzzy logic, offer adaptive and intuitive solutions to optimize energy use in smart homes, dealing with uncertainties and imprecisions in environmental conditions and occupant preferences. Fuzzy logic allows for refined control of devices such as lighting, heating, ventilation, and air conditioning (HVAC), adjusting in real-time to variations in conditions and user behaviors. Recent studies have demonstrated the effectiveness of these systems in various applications. Kontogiannis, Bargiotas, and Daskalopulu (2021) developed a fuzzy control system to recommend optimal energy consumption values based on environmental data, using the Mamdani approach and decision tree linearization. Keshtkar and Arzanpour (2017) introduced an autonomous thermostat combining supervised learning with wireless sensors and dynamic electricity pricing, adjusting temperatures and maintaining user comfort. Ain et al. (2018) proposed a Fuzzy Inference System that incorporates humidity and internal temperature variations to optimize energy consumption without sacrificing comfort. Additionally, Khalid et al. (2019) presented an energy management controller for smart grids using fuzzy logic and heuristic optimization techniques, improving load management and reducing costs and consumption. Collotta and Pau (2015) explored the integration of IoT technology with fuzzy-based systems using Bluetooth Low Energy (BLE) to manage energy consumption in home automation networks. These studies highlight that fuzzy control systems are crucial for optimizing energy consumption in smart homes, balancing efficiency and comfort, with ongoing advancements promising continuous improvements in energy management.

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.

Real-Time Monitoring and Control with Wireless Sensor and Actuator Technology

This paper explores the implementation of a smart monitoring system within a wireless sensor network, with a particular emphasis on developing a robust routing framework using the Routing Protocol for Low-power and Lossy Networks (RPL). This protocol, is designed to address the unique challenges of low-power and lossy environments. Our approach involves using a streamlined version of the Representational State Transfer (REST) architecture, implemented through a binary web service. This setup minimizes overhead and maximizes efficiency, which is critical for resource-constrained networks. Additionally, we use a publish/subscribe model, where each node in the network makes its resources—such as environmental sensors—available to other nodes interested in them. This model enhances the flexibility and responsiveness of the network. A significant part of our research involves a detailed performance evaluation of RPL. We conducted a series of experiments to understand how various parameters of the RPL protocol affect its performance in a smart grid scenario. Our analysis looks at key metrics such as routing efficiency, energy consumption, and overall network reliability. Through these experiments, we aim to provide valuable insights into how different configurations of RPL can impact its effectiveness. Our findings are intended to guide the optimization of RPL for specific applications, offering practical recommendations for deploying smart monitoring systems in similar low-power, lossy environments. This research not only sheds light on RPL’s performance but also contributes to the advancement of more efficient and reliable wireless sensor networks for smart grids and other related applications.

A CNN-based approach for anomaly detection in smart grid systems

Power grids are always a major center of attraction for attackers due to their large coverage area, complex technological infrastructure, remote access facilities, connectivity, and many more. The power grids that have bidirectional power flow, information interchange, and advanced communication technologies to improve the quality of power transmission and distribution are called smart grids. DoS attacks and false data injection attacks are the two major attacks on smart grids. This paper considers a false data injection attack considering noise reduction, state estimation, system parameters, and control system generation. Firstly, we measure the noise and mitigate it using the data matrix of the devices (sensors, generators, substations, and breaker systems). Secondly, state estimation uses the CNN to track the injected attack types. Finally, a control system is designed to provide anomaly patterns to the devices as input to prevent attacks in the future. The experimental results show that the proposed method achieves a higher anomaly detection rate with a lower error rate (approximately . 1.43%). The network performance parameters are providing significant results; throughput (12 Mbps), PDR (0.005%), execution time (1.43 s), and energy consumption are reduced by 12%–15% as compared to the existing research findings.

An ML-Based Solution in the Transformation towards a Sustainable Smart City

The rapid development of modern information technology (IT), power supply, communication and traffic information systems and so on is resulting in progress in the area of distributed and energy-efficient (if possible, powered by renewable energy sources) smart grid components securely connected to entire smart city management systems. This enables a wide range of applications such as distributed energy management, system health forecasting and cybersecurity based on huge volumes of data that automate and improve the performance of the smart grid, but also require analysis, inference and prediction using artificial intelligence. Data management strategies, but also the sharing of data by consumers, institutions, organisations and industries, can be supported by edge clouds, thus protecting privacy and improving performance. This article presents and develops the authors’ own concept in this area, which is planned for research in the coming years. The paper aims to develop and initially test a conceptual framework that takes into account the aspects discussed above, emphasising the practical aspects and use cases of the Social Internet of Things (SIoT) and artificial intelligence (AI) in the everyday lives of smart sustainable city (SSC) residents. We present an approach consisting of seven algorithms for the integration of large data sets for machine learning processing to be applied in optimisation in the context of smart cities.

Application of Electrical Automation in Smart City

With the acceleration of urbanization, the concept of smart city came into being, aiming to improve the efficiency of urban management and the quality of life of residents through advanced information technology and integrated innovative technologies. Electrical automation is the foundation and support of smart city construction, and its intelligent and automated application is crucial to realize the functions of smart city. This article explores several key application areas of electrical automation in smart cities, including smart grids, smart buildings, smart transportation, smart security, etc., and analyzes how these technologies contribute to the development of smart cities, as well as the benefits they bring, and looks at future trends.

A nonsmooth Levenberg–Marquardt method based on KKT conditions for real-time pricing in smart grid

The real-time pricing of smart grid, which is an important mean of demand side management, is an ideal method to adjust the power balance between the supply and demand in smart grid system. By transforming the social welfare maximization model into a nonsmooth system of equations based on KKT conditions and investigating the related nonsmooth-function’s generalized Jacobi, a nonsmooth Levenberg–Marquardt method with global convergence is proposed in this paper. Also, the local convergence rate is obtained under the local error bound, which is weaker than the nonsingular condition. Finally, numerical simulations show that the smaller the approximate parameter in smoothing Newton’s method, the closer the electricity price is to the value obtained by the proposed algorithm. And the price of the multi price based on the method is lower than that of the single price algorithm that does not consider user types. Meanwhile, compared with the distributed algorithm with different scales, it is found that the price is lower and the social welfare is higher, which shows that the algorithm is robust and effective.

Optimizing dynamic voltage restorers with Bee Optimization Algorithm for enhanced power quality in modern hydro turbine grids

ABSTRACT This study explores the optimization of dynamic voltage restorers (DVRs) within hydro turbine systems using the Bee Optimization Algorithm (BOA), focusing on enhancing power system stability and quality through advanced control strategies. Emphasizing total harmonic distortion (THD) minimization, the research addresses power quality challenges prevalent in hydroelectric power generation. Detailed simulations demonstrate how BOA effectively reduces THD, optimizing DVR performance in response to grid disturbances typical in renewable energy integrations. Findings validate the BOA's efficacy in improving voltage stability and underscore the potential of bio-inspired algorithms for smart grid applications in hydro settings. This approach not only supports the reliability of hydroelectric power systems but also opens new avenues for employing multi-objective optimization techniques to advance DVR functionality, contributing to the sustainable management of energy infrastructures.

Optimal energy management via day-ahead scheduling considering renewable energy and demand response in smart grids

The energy optimization in smart power grids (SPGs) is crucial for ensuring efficient, sustainable, and cost-effective energy management. However, the uncertainty and stochastic nature of distributed generations (DGs) and loads pose significant challenges to optimization models. In this study, we propose a novel optimization model that addresses these challenges by employing a probabilistic method to model the uncertain behavior of DGs and loads. Our model utilizes the multi-objective wind-driven optimization (MOWDO) technique with fuzzy mechanism to simultaneously address economic, environmental, and comfort concerns in SPGs. Unlike existing models, our approach incorporates a hybrid demand response (HDR), combining price-based and incentive-based DR to mitigate rebound peaks and ensure stable and efficient energy usage. The model also introduces battery energy storage systems (BESS) as environmentally friendly backup sources, reducing reliance on fossil fuels and promoting sustainability. We assess the developed model across various distinct configurations: optimizing operational costs and pollution emissions independently with/without DR, optimizing both operational costs and pollution emissions concurrently with/without DR, and optimizing operational costs, user comfort, and pollution emissions simultaneously with/without DR. The experimental findings reveal that the developed model performs better than the multi-objective bird swarm optimization (MOBSO) algorithm across metrics, including operational cost, user comfort, and pollution emissions.

An enhanced broadband class-J mode power amplifier for 5G smart meter applications.

Background: With the tremendous increase in the usage of smart meters for industrial/ household purposes, their implementation is considered a crucial challenge in the Internet of Things (IoT) world, leading to a demand for emerging 5G technology. In addition, a large amount of data has to be communicated by smart meters efficiently, which needs a significant enhancement in bandwidth. The power amplifier (PA) plays a major role in deciding the efficiency and bandwidth of the entire communication system. Among the various modes of PAs, a newly developed Class-J mode PA has been proven to achieve high efficiency over a wide bandwidth by maintaining linearity. Methods: This paper proposes a Class-J mode PA design methodology using a CGH40010F-GaN device that operates at a 3.5 GHz frequency to meet the requirements of 5G wireless communication technology for the replacement of existing 4G/LTE technology used for advanced metering infrastructure (AMI) in smart grids. This research's main objective is to design the proper matching networks (M.Ns) to achieve Class-J mode operation that satisfies the bandwidth requirements of 5G smart grid applications. With the target impedances obtained using the load-pull simulation, lumped element matching networks are analyzed and designed in 3 ways using the ADS EDA tool. Results: The simulation results reveal that the proposed Class-J PA provides a maximum drain efficiency (D.E) of 82%, power added efficiency (PAE) of 67% with 13 dB small-signal gain at 3.5 GHz, and output power of 40 dBm (41.4 dBm peak) with a power gain of approximately 7 dB over a bandwidth of approximately 400 MHz with a 28 V power supply into a 50 Ω load. Conclusion: The efficiency and bandwidth of the proposed Class-J PA can be enhanced further by fine-tuning the matching network design to make it more suitable for 5G smart meter/grid applications.