Search

The advanced metering infrastructure (AMI) of the smart grid plays a critical role in energy management and billing by enabling the periodic transmission of consumers' power consumption readings. To optimize data collection efficiency, AMI employs a "change and transmit" (CAT) approach. This approach ensures that readings are only transmitted when there is enough change in consumption, thereby reducing data traffic. Despite the benefits of this approach, it faces security challenges where malicious consumers can manipulate their readings to launch cyberattacks for electricity theft, allowing them to illegally reduce their bills. While this challenge has been addressed for supervised learning CAT settings, it remains insufficiently addressed in unsupervised learning settings. Moreover, due to the distortion introduced in the power consumption readings due to using the CAT approach, the accurate prediction of future consumption for energy management is a challenge. In this paper, we propose a two-stage approach to predict future readings and detect electricity theft in the smart grid while optimizing data collection using the CAT approach. For the first stage, we developed a predictor that is trained exclusively on benign CAT power consumption readings, and the output of the predictor is the actual readings. To enhance the prediction accuracy, we propose a cluster-based predictor that groups consumers into clusters with similar consumption patterns, and a dedicated predictor is trained for each cluster. For the second stage, we trained an autoencoder and a one-class support vector machine (SVM) on the benign reconstruction errors of the predictor to classify instances of electricity theft. We conducted comprehensive experiments to assess the effectiveness of our proposed approach. The experimental results indicate that the prediction error is very small and the accuracy of detection of the electricity theft attacks is high. [ABSTRACT FROM AUTHOR]

This study compares bio-inspired optimization algorithms for enhancing an ANN-based Maximum Power Point Tracking (MPPT) forecast system under partial shading conditions in photovoltaic systems. Four algorithms—grey wolf optimizer (GWO), particle swarm optimization (PSO), squirrel search algorithm (SSA), and cuckoo search (CS)—were evaluated, with the dataset augmented by perturbations to simulate shading. The standard ANN performed poorly, with 64 neurons in Layer 1 and 32 in Layer 2 (MSE of 159.9437, MAE of 8.0781). Among the optimized approaches, GWO, with 66 neurons in Layer 1 and 100 in Layer 2, achieved the best prediction accuracy (MSE of 11.9487, MAE of 2.4552) and was computationally efficient (execution time of 1198.99 s). PSO, using 98 neurons in Layer 1 and 100 in Layer 2, minimized MAE (2.1679) but had a slightly longer execution time (1417.80 s). SSA, with the same neuron count as GWO, also performed well (MSE 12.1500, MAE 2.7003) and was the fastest (987.45 s). CS, with 84 neurons in Layer 1 and 74 in Layer 2, was less reliable (MSE 33.7767, MAE 3.8547) and slower (1904.01 s). GWO proved to be the best overall, balancing accuracy and speed. Future real-world applications of this methodology include improving energy efficiency in solar farms under variable weather conditions and optimizing the performance of residential solar panels to reduce energy costs. Further optimization developments could address more complex and larger-scale datasets in real-time, such as integrating renewable energy sources into smart grid systems for better energy distribution. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources, such as rooftop solar panels, into smart grids poses significant challenges for managing customer-side battery storage. In response, this paper introduces a novel reinforcement learning (RL) approach aimed at optimizing the coordination of these batteries. Our approach utilizes a single-agent, multi-environment RL system designed to balance power saving, customer satisfaction, and fairness in power distribution. The RL agent dynamically allocates charging power while accounting for individual battery levels and grid constraints, employing an actor–critic algorithm. The actor determines the optimal charging power based on real-time conditions, while the critic iteratively refines the policy to enhance overall performance. The key advantages of our approach include: (1) Adaptive Power Allocation: The RL agent effectively reduces overall power consumption by optimizing grid power allocation, leading to more efficient energy use. (2) Enhanced Customer Satisfaction: By increasing the total available power from the grid, our approach significantly reduces instances of battery levels falling below the critical state of charge (SoC), thereby improving customer satisfaction. (3) Fair Power Distribution: Fairness improvements are notable, with the highest fair reward rising by 173.7% across different scenarios, demonstrating the effectiveness of our method in minimizing discrepancies in power distribution. (4) Improved Total Reward: The total reward also shows a significant increase, up by 94.1%, highlighting the efficiency of our RL-based approach. Experimental results using a real-world dataset confirm that our RL approach markedly improves fairness, power efficiency, and customer satisfaction, underscoring its potential for optimizing smart grid operations and energy management systems. [ABSTRACT FROM AUTHOR]

The increasing importance of hydropower generation has led to the development of new smart technologies and the need for reliable and efficient equipment in this field. As long as hydropower plants are more complex to build up than other power plants, the operation regimes and maintenance activities become essential for the hydropower companies to optimize their performance, such that including the data-driven approaches in the decision-making process represents a challenge. In this paper, a comprehensive and multi-task framework integrated into a Knowledge Discovery module based on Data Mining to support the decisions of the operators from the control rooms and facilitate the transition from the classical to smart Supervisory Control and Data Acquisition (SCADA) system in hydropower plants has been designed, developed, and tested. It integrates tasks related to detecting the outliers through advanced statistical procedures, identifying the operating regimes through the patterns associated with typical operating profiles, and developing strategies for loading the generation units that consider the number of operating hours and minimize the water amount used to satisfy the power required by the system. The proposed framework has been tested using the SCADA system's database of a hydropower plant belonging to the Romanian HydroPower Company. The framework can offer the operators from the control room comparative information for a time horizon longer than one year. The tests demonstrated the utility of a Knowledge Discovery module to ensure the transition toward smart SCADA systems that will help the decision-makers improve the management of the hydropower plants. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A smart grid is designed to enable the massive deployment and efficient use of distributed energy resources, including distributed photovoltaics (DPV). Due to the large number, wide distribution, and insufficient monitoring information of DPV stations, the pressure to maintain them has increased rapidly. Furthermore, based on reports in the relevant literature, there is still a lack of efficient large-scale maintenance strategies for DPV stations at present, leading to the high maintenance costs and overall low efficiency of DPV stations. Therefore, this paper proposes a maintenance period decision model and an areal maintenance strategy. The implementation steps of the method are as follows: firstly, based on the reliability model and dust accumulation model of the DPV components, the maintenance period decision model is established for different numbers of DPV stations and different driving distances; secondly, the optimal maintenance period is determined by using the Monte Carlo method to calculate the average economic benefits of daily maintenance during different periods; then, an areal maintenance strategy is proposed to classify all the DPV stations into different areas optimally, where each area is maintained to reach the overall economic optimum for the DPV stations; finally, the validity and rationality of this strategy are verified with the case study of the DPV poverty alleviation project in Badong County, Hubei Province. The results indicate that compared with an independent maintenance strategy, the proposed strategy can decrease the maintenance cost by 10.38% per year, which will help promote the construction of the smart grid and the development of sustainable cities. The results prove that the method proposed in this paper can effectively reduce maintenance costs and improve maintenance efficiency. [ABSTRACT FROM AUTHOR]

The growing adoption of behind-the-meter (BTM) photovoltaic (PV) systems, electric vehicle (EV) home chargers, and heat pumps (HPs) is causing increased grid congestion issues, particularly in power distribution grids. Leveraging BTM prosumer flexibility offers a cost-effective and readily available solution to address these issues without resorting to expensive and time-consuming infrastructure upgrades. This work evaluated the effectiveness of this solution by introducing a novel modeling framework that combines a rolling horizon (RH) optimal power flow (OPF) algorithm with a customized piecewise linear cost function. This framework allows for the individual control of flexible BTM assets through various control measures, while modeling the power flow (PF) and accounting for grid constraints. We demonstrated the practical utility of the proposed framework in an exemplary residential region in Schutterwald, Germany. To this end, we constructed a PF-ready grid model for the region, geographically allocated a future BTM asset mix, and generated tailored load and generation profiles for each household. We found that BTM storage systems optimized for self-consumption can fully resolve feed-in violations at HV/MV stations but only mitigate 35% of the future load violations. Implementing additional control measures is key for addressing the remaining load violations. While curative measures, e.g., temporarily limiting EV charging or HP usage, have minimal impacts, proactive measures that control both the charging and discharging of BTM storage systems can effectively address the remaining load violations, even for grids that are already operating at or near full capacity. [ABSTRACT FROM AUTHOR]

Grid dynamics and control mechanisms have improved as smart grids have used more inverter-based renewable energy resources (IBRs). Modern converter technologies try to improve converters' capacities to compensate for grid assistance, but their inertia still makes them heavily dependent on synchronous generators (SGs). Grid-following (GFL) converters ensure grid reliability. As RES penetration increases, the GFL converter efficiency falls, limiting integration and causing stability difficulties in low-inertia systems. A full review of grid converter technologies, grid codes, and controller mechanisms is needed to determine the current and future needs. A more advanced converter is needed for integration with more renewable energy sources (RESs) and to support weak grids without SGs and with low inertia. Grid-forming (GFM) inverters could change the electrical business by addressing these difficulties. GFM technology is used in hybrid, solar photovoltaic (PV), battery energy storage systems (BESSs), and wind energy systems to improve these energy systems and grid stability. GFM inverters based on BESSs are becoming important internationally. Research on GFM controllers is new, but the early results suggest they could boost the power grid's efficiency. GFM inverters, sophisticated smart inverters, help maintain a reliable grid, energy storage, and renewable power generation. Although papers in the literature have compared GFM and GFL, none of them have examined them in terms of their performance in a low-SCR system. This paper shows how GFM outperforms GFL in low-inertia and weak grid systems in the form of a review. In addition, a suitable comparison of the results considering the performance of GFM and GFL in a system with varying SCRs has been depicted in the form of simulation using PSCAD/EMTDC for the first time. [ABSTRACT FROM AUTHOR]

Power theft and other abnormal power consumption behaviors seriously affect the safety, reliability, and stability of the power grid system. The traditional abnormal power consumption detection methods have complex models and low accuracy. In this paper, an unsupervised abnormal power consumption detection method based on multi-cluster feature selection and the Gaussian mixture model is proposed. First of all, twelve features are extracted from the load sequence to reflect the overall form, fluctuation, and change trend of the user's electricity consumption. Then, multi-cluster feature selection algorithm is employed to select a subset of important features. Finally, based on the selected features, the Gaussian mixture model is formulated to cluster the normal power users and abnormal power users into different groups, so as to realize abnormal power consumption detection. The proposed method is evaluated through experiments based on a power load dataset from Anhui Province, China. The results show that the proposed method works well for abnormal power consumption detection, with significantly superior performance comapred to the traditional approaches in terms of the popular binary evaluation indicators like recall rate, precision rate, and F-score. [ABSTRACT FROM AUTHOR]

Flexible DC power grids are widely recognized as an important component of building smart grids. Compared with traditional AC power grids, flexible DC power grids have strong technical advantages in islanding power supplies, distributed power supplies, regional power supplies, and AC system interconnection. In multi-terminal flexible DC power grids containing renewable energy sources such as solar and wind power, due to the instability and intermittency of renewable energy, it is usually necessary to add energy storage units to pre-regulate the power of the multi-terminal flexible DC power grid in islanded operation. Aiming at the important problem of large current impact and serious consequences when the flexible DC distribution network fails, a combined location method combining an improved impedance method (series current-limiting reactors at both ends of the line to obtain a more accurate current differential value) and a particle swarm optimization algorithm is proposed. Initially, by establishing the enhanced impedance model, the differential variables under the conditions of inter-electrode short-circuit and single-pole grounding fault can be obtained. Then tailor-made fitness functions are designed for these two models to optimize parameter identification. Subsequently, the iterative parameters of the particle swarm optimization algorithm are fine-tuned, giving it dynamic sociality and self-learning ability in the iterative process, which significantly improves the convergence speed and successfully avoids local optimization. Finally, various fault types in a six-terminal DC distribution network are simulated and analyzed by MATLAB, and the results show that this method has good accuracy and robustness. This research provides strong theoretical and methodological support for improving the safety and reliability of DC distribution systems. [ABSTRACT FROM AUTHOR]

Considering the widespread presence of switching devices on the power grid (including renewable energy system inverters), network distortion is more prominent. To maximize network efficiency, our goal is to minimize these distortions. Measuring the voltage and current total harmonic distortion (THD) using power meters and other specific equipment, and assessing power factor and peak currents, represents a crucial step in creating an efficient and stable smart grid. In this paper, we propose a power meter capable for measuring both standard electrical parameters and power quality parameters such as the voltage and current total harmonic distortion factors. The resulting device is compact and DIN-rail-mountable, occupying only three modules in an electrical cabinet. It integrates both wired and wireless communication interfaces and multiple communication protocols, such as Modbus RTU/TCP and MQTT. A microSD card can be used to store the device configuration parameters and to record the measured values in case of network fault events, the device's continuous operation being ensured by the integrated backup battery in this situations. The device was calibrated and tested against three industrial power meters: Siemens SENTRON PAC4200, Janitza UMG-96RM, and Phoenix Contact EEM-MA400, obtaining an overall average measurement error of only 1.22%. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The integration of the Internet of Things (IoT) with renewable energy technologies is revolutionizing modern power systems by enhancing efficiency, reliability, and sustainability. This paper examines the role of the IoT in optimizing the integration and management of renewable energy sources, such as solar and wind power, into the electrical grid. The IoT enables real-time monitoring, data analysis, and automation, facilitating advanced load management, demand response, and energy storage solutions. Key advancements in IoT technologies, including smart grids and energy management systems, are discussed, highlighting their impact on improving grid stability and promoting the use of renewable energy. The paper also finds some challenges such as data security, privacy, and the need for standardized communication protocols. Furthermore, it finds how the IoT optimizes electric vehicle performance through advanced battery management, real-time energy consumption monitoring, and improved interaction with the electrical grid. Future research directions emphasize the potential of the IoT to further enhance renewable energy integration through artificial intelligence and machine learning, driving the transition towards a more sustainable and resilient energy future. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources (RES) into smart grids has been considered crucial for advancing towards a sustainable and resilient energy infrastructure. Their integration is vital for achieving energy sustainability among all clean energy sources, including wind, solar, and hydropower. This review paper provides a thoughtful analysis of the current status of the smart grid, focusing on integrating various RES, such as wind and solar, into the smart grid. This review highlights the significant role of RES in reducing greenhouse gas emissions and reducing traditional fossil fuel reliability, thereby contributing to environmental sustainability and empowering energy security. Moreover, key advancements in smart grid technologies, such as Advanced Metering Infrastructure (AMI), Distributed Control Systems (DCS), and Supervisory Control and Data Acquisition (SCADA) systems, are explored to clarify the related topics to the smart grid. The usage of various technologies enhances grid reliability, efficiency, and resilience are introduced. This paper also investigates the application of Machine Learning (ML) techniques in energy management optimization within smart grids with the usage of various optimization techniques. The findings emphasize the transformative impact of integrating RES and advanced smart grid technologies alongside the need for continued innovation and supportive policy frameworks to achieve a sustainable energy future. [ABSTRACT FROM AUTHOR]

Substantial breakthroughs in renewable energy have been made in order to reduce energy-induced climate change. Yet our reliance on these sources is still insufficient. The UK's objective of attaining net-zero emissions by 2050 is highly dependent on shifting to an electrical system that exclusively relies on zero-carbon generation. This entails integrating renewable energy sources, along with other low-carbon sources such as nuclear power, into the energy mix. However, the primary barrier to incorporating additional renewable energy sources into the grid is their intermittent and volatile nature. Therefore, there is a pressing need to stabilise the generation of renewables and manage this volatility by enhancing the balancing mechanism between microgrids and the national grid. This paper examines previous research on microgrids and smart grids, specifically from a supply chain perspective. It has been observed that the majority of the current literature focuses on documenting selfish microgrids that strive to optimise performance at the microgrid level. However, there is an alternative approach that draws inspiration from the field of supply chain management. Consequently, it is possible to enhance a microgrid's performance within the broader system that it belongs to by reconsidering the timing and location of storage utilisation. [ABSTRACT FROM AUTHOR]

As the demand for power supply stability and reliability in the active distribution network (ADN) increases, the primary and secondary integrated intelligent switch (PSIIS) has become the primary choice for smart grid transformation in many weak infrastructure areas. However, the number of PSIISs that can be configured is often limited. It is necessary to comprehensively utilize the measurement data and sectional capabilities of PSIISs through the optimal configuration method to find the optimal configuration scheme. Therefore, fault observability-related indexes (FORIs) are proposed based on the functional characteristics of PSIISs. These indexes include fault type observability, fault location observability, fault current distribution characteristics observability, transition resistance observability, and weak infrastructure area observability. An optimization configuration model of PSIISs considering the comprehensive fault observability (CFO) is constructed. The adaptive genetic algorithm (AGA) is selected as the model-solving method. Subsequently, an optimal configuration method of PSIISs considering CFO is proposed. Finally, an example analysis is conducted in MATLAB to verify the effectiveness and feasibility of the proposed method. This optimal configuration method aims to maximize the use of a limited number of PSIISs by considering CFO, and the AGA proves to be an effective tool in solving the optimal configuration scheme. [ABSTRACT FROM AUTHOR]

A smart grid protection system based on phasor information at circuit breakers is proposed in this paper. Phasor data for fault diagnosis is obtained from phasor measurement units or intelligent electronic devices installed near circuit breakers, without the need for additional measurement equipment. When a fault occurs, the protection system first locates the core protection circuit breaker closest to the fault point. It then identifies the faulty component by analyzing the current phasor differences between this breaker and adjacent ones. This scheme offers high computational efficiency, enabling rapid identification of faulty equipment and its precise location. The proposed protection system structure can be divided into two types: centralized and decentralized, and the most suitable scheme should be selected based on the actual situation. Finally, the feasibility of the proposed scheme was verified through the IEEE 39-bus system and a simulated actual area power grid model, demonstrating its applicability to practical smart grid scenarios. The simulation network validates the implementation method of the proposed protection strategy. [ABSTRACT FROM AUTHOR]

With the gradual increase in the proportion of new energy sources in the power grid, there is an urgent need for more flexible resources to participate in short-term regulation. The impact of communication network channel quality will continue to magnify, and factors such as communication latency may directly affect the efficiency and effectiveness of resource regulation. In this context of a large number of flexible demand-side resources accessing the grid, this article proposes a bidirectional channel delay measurement method based on MQTT (Message Queuing Telemetry Transport). It can effectively evaluate the real-time performance of communication links, considering that resources mainly access the grid through the public network. Subsequently, focusing on two typical types of resources on the demand side, namely, split air conditioners and central air conditioners, this article proposes an assessment method for correcting the response capabilities of air conditioning resources considering communication latency. Experimental simulations are conducted, and the results demonstrate that under given communication conditions, this method can more accurately estimate the response capability of air conditioners. This provides a basis for formulating more reasonable scheduling strategies, avoiding excessive or insufficient resource regulation caused by communication issues, and aiding the power grid in achieving precise scheduling. [ABSTRACT FROM AUTHOR]

This review comprehensively examines the burgeoning field of intelligent techniques to enhance power systems' stability, control, and protection. As global energy demands increase and renewable energy sources become more integrated, maintaining the stability and reliability of both conventional power systems and smart grids is crucial. Traditional methods are increasingly insufficient for handling today's power grids' complex, dynamic nature. This paper discusses the adoption of advanced intelligence methods, including artificial intelligence (AI), deep learning (DL), machine learning (ML), metaheuristic optimization algorithms, and other AI techniques such as fuzzy logic, reinforcement learning, and model predictive control to address these challenges. It underscores the critical importance of power system stability and the new challenges of integrating diverse energy sources. The paper reviews various intelligent methods used in power system analysis, emphasizing their roles in predictive maintenance, fault detection, real-time control, and monitoring. It details extensive research on the capabilities of AI and ML algorithms to enhance the precision and efficiency of protection systems, showing their effectiveness in accurately identifying and resolving faults. Additionally, it explores the potential of fuzzy logic in decision-making under uncertainty, reinforcement learning for dynamic stability control, and the integration of IoT and big data analytics for real-time system monitoring and optimization. Case studies from the literature are presented, offering valuable insights into practical applications. The review concludes by identifying current limitations and suggesting areas for future research, highlighting the need for more robust, flexible, and scalable intelligent systems in the power sector. This paper is a valuable resource for researchers, engineers, and policymakers, providing a detailed understanding of the current and future potential of intelligent techniques in power system stability, control, and protection. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive bibliometric review and visualization of smart and sustainable energy consumption, delving into the challenges and opportunities of developing renewable and non-renewable energy sources. The study examines research trends and emerging themes about integrating smart solutions and sustainable energy resource consumption. The analytical methods used involve thoroughly analyzing empirical data, case studies, and review papers to map the research landscape. The results highlight dominant research topics, influential authors, and publication timelines in this field. The review identifies the key challenges in harnessing renewable and non-renewable energy sources, including the need for reliable energy sources, energy storage systems, and smart grid technologies. The paper concludes with insights into the most effective practices for promoting smart and energy-efficient methods while emphasizing the complexity of sustainable energy solutions. [ABSTRACT FROM AUTHOR]

For the advancement of smart grids, solar power generation predictions have become an important research topic. In the case of using traditional modeling methods, excessive computational costs may be incurred and it is difficult for these methods to learn the multi-variable dependencies of the data. Therefore, in this paper, a deep learning model was used to combine convolutional neural networks and long short-term memory recurrent network predictions. This method enables hourly power generation one day into the future. Convolutional neural networks are used to extract the features of multiple time series, while long short-term memory neural networks predict multivariate outcomes simultaneously. In order to obtain more accurate prediction results, we performed feature selection on meteorological features and combined the selected weather features to train the prediction model. We further distinguished sunny- and rainy-day models according to the predicted daily rainfall conditions. In the experiment, it was shown that the method of combining meteorological features further reduced the error. Finally, taking into account the differences in climate conditions between the northern and southern regions of Taiwan, the experimental results of case studies involving multiple regions were evaluated to verify the proposed method. The results showed that training combined with selected meteorological features can be widely used in regions with different climates in Taiwan. [ABSTRACT FROM AUTHOR]

The global energy sector is currently undergoing a significant transformation to address sustainability, energy efficiency, and grid resilience. Smart grids, leveraging advanced technologies like the power internet of things (PIoT), play a crucial role in this transformation. This research focuses on enhancing the efficiency, reliability, and sustainability of electricity distribution through IoT technologies. It envisions a system where interconnected devices, sensors, and data analytics optimize energy consumption, monitor grid conditions, and manage demand response scenarios. Central to this effort is the integration of PIoT into the smart grid infrastructure, particularly in implementing dynamic pricing strategies for demand response. Leveraging power line communication (PLC) techniques, this innovative approach facilitates real-time communication between grid components and consumers. The results demonstrate improved grid stability through dynamic load management, effectively responding to demand fluctuations, and minimizing disruptions. The deployment of dynamic pricing methods using PLC-driven schemes empowers customers by offering access to real-time energy use data. This access incentivizes energy-efficient behavior. leading to a 30% increase in the adoption of energy-saving techniques among consumers. A utility company pilot study claimed a 12% drop in peak demand after adopting time-of-use charges, with an accuracy rate of 98.87% in total. [ABSTRACT FROM AUTHOR]

Renewable energy sources play a critical role in all governments' and organizations' energy management and sustainability plans. The solar cell represents one such renewable energy resource, generating power in a population-free circumference. Integrating these renewable sources with the smart grids leads to the generation of green smart grids. Smart grids are critical for modernizing electricity distribution by using new communication technologies that improve power system efficiency, reliability, and sustainability. Smart grids assist in balancing supply and demand by allowing for real-time monitoring and administration, as well as accommodating renewable energy sources and reducing outages. However, their execution presents considerable problems. High upfront expenditures and the need for substantial and reliable infrastructure changes present challenges. Despite these challenges, shifting to green smart grids is critical for a resilient and adaptable energy future that can fulfill changing consumer demands and environmental aims. To this end, this work considers developing a reliable Internet of Things (IoT)-based green smart grid. The proposed green grid integrates traditional grids with solar energy and provides a control unit between the generation and consumption parts of the grid. The work deploys intelligent IoT units to control energy demands and manage energy consumption effectively. The proposed framework deploys the paradigm of distributed edge computing in four levels to provide efficient data offloading and power management. The developed green grid outperformed traditional grids in terms of its reliability and energy efficiency. The proposed green grid reduces energy consumption over the distribution area by an average of 24.3% compared to traditional grids. [ABSTRACT FROM AUTHOR]

Australia, like many other countries around the world, is undergoing a transition toward net zero emissions. It requires changes and development in many sectors, which not only bring benefits but also challenges. The rapid growth in renewable energy sources (RESs) is necessary to decarbonise electricity generation but negatively affects grid stability. Residential buildings also contribute to this issue through specific load profiles and the high penetration of rooftop photovoltaic (PV) installations. Maintaining grid balance will be crucial for further emissions reductions. One of the potential solutions can be the replacement of conventional heating and cooling systems in houses with solutions capable of storing energy and shifting the electrical load. As presented in this paper, heat pumps and hydronic systems can significantly improve the electrical load of a typical South Australian household when they are controlled by algorithms reacting to the current grid conditions and household-generated electricity compared to conventional solutions. TRNSYS 18 simulations of air source and ground source heat pump systems with smart control based on measured electricity consumption and domestic hot water usage data showed the possibility of total energy consumption reduction, shifting the load from peak periods towards periods of excessive RES generation and increasing self-consumption of rooftop PV electricity. These improvements reduce the amount of emissions generated by such a household and allow for further development of other sectors. [ABSTRACT FROM AUTHOR]

In the field of electrical engineering, there is an increasing concern among managers and operators about the secure and cost-efficient operation of smart power systems in response to disturbances caused by physical cyber attacks and natural disasters. This paper introduces an innovative framework for the hybrid, coordinated control of Unified Power Flow Controllers (UPFCs) and Power System Stabilizers (PSSs) within a power system. The primary objective of this framework is to enhance the system's security metrics, including stability and resilience, while also considering the operational costs associated with defending against cyber-physical attacks. The main novelty of this paper lies in the introduction of a real-time online framework that optimally coordinates a power system stabilizer, power oscillation damper, and unified power flow controller to enhance the power system's resilience against transient disturbances caused by cyber-physical attacks. The proposed approach considers technical performance indicators of power systems, such as voltage fluctuations and losses, in addition to economic objectives, when determining the optimal dynamic coordination of UPFCs and PSSs—aspects that have been neglected in previous modern research. To address the optimization problem, a novel multi-objective search algorithm inspired by Harris hawks, known as the Multi-Objective Harris Hawks (MOHH) algorithm, was developed. This algorithm is crucial in identifying the optimal controller coefficient settings. The proposed methodology was tested using standard IEEE9-bus and IEEE39-bus test systems. Simulation results demonstrate the effectiveness and efficiency of this approach in achieving optimal system recovery, both technically and economically, in the face of cyber-physical attacks. [ABSTRACT FROM AUTHOR]

Recent statistics indicate that as of 2023, the global renewable energy capacity has reached new heights, with Saudi Arabia significantly contributing to this growth through its strategic initiatives. The kingdom is particularly focusing on harnessing solar power, given its abundant sunlight, and is also exploring wind energy, leveraging its vast desert landscapes. These efforts are part of Saudi Arabia's broader strategy to become a global leader in renewable energy. This paper provides a detailed exploration of Saudi Arabia's ambitious journey from a fossil-fuel-dominated energy sector to a more sustainable, renewable-energy-driven future. Anchored in the broader context of global energy trends, the study emphasizes the critical shift toward renewable resources, with a particular focus on Saudi Arabia's unique position in this global movement. Key to this transition is Saudi Arabia's Vision 2030, a strategic framework that guides the country's renewable energy policies and initiatives. A comprehensive review of these policies, including other governmental and international collaborations that support renewable energy development, is offered. The current state of renewable energy in Saudi Arabia is assessed, including an overview of existing projects and production statistics. The paper also explores emerging technologies such as energy storage systems and smart grid solutions, emphasizing their role in Saudi Arabia's energy transition. An impact assessment is conducted to understand the environmental, economic, and social effects of this energy shift. The paper also includes a comparative analysis of other countries' renewable energy transitions, extracting lessons and best practices applicable to the Saudi context. Strategic recommendations and reflections on Saudi Arabia's future role in the global energy landscape are provided for stakeholders in energy policy, environmental planning, and sustainable development. [ABSTRACT FROM AUTHOR]

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

Renewable energy resources are playing a crucial role in minimizing fossil fuel emissions. Integrating machine learning techniques with tidal power forecasting could greatly enhance the accuracy and reliability of predictions, which is crucial for efficient energy production and management. A hybrid approach combining different methods often yields better results than relying on individual techniques. The accuracy of tidal current power is very important, especially for smart grid applications. This work proposes hybrid adaptive neuro-fuzzy inference system (ANFIS) with the Kalman filter (KF) and a neuro-wavelet (WNN) for tidal current speed, direction, and power forecasting. The turbine used in this study is driven by a direct drive permanent magnet synchronous generator (DDPMSG). The predictions of individual and hybrid models including the ANFIS, the Kalman filter, and the WNN for tidal current speed and the power it generates are compared with another dataset as a way of validation which is the tidal currents direction. Also, other published work results in the literature are compared to the proposed work. Different hybrid models are proposed for smart grid integration. The results of this work indicate that the hybrid model of the WNN and the ANFIS for tidal current power or speed forecasting has the highest performance compared to all other models. [ABSTRACT FROM AUTHOR]

Power electronics plays a key role in the management and conversion of electrical energy in a variety of applications, including the use of renewable energy sources such as solar, wind and hydrogen energy, as well as in electric vehicles, industrial technologies, homes and smart grids. These technologies are essential for the successful implementation of the green transition, as they help reduce carbon emissions and promote the production and consumption of cleaner and more sustainable energy. The present work presents a new generation of power electronic devices and systems, which includes the following main aspects: advances in semiconductor technologies, such as the use of silicon carbide (SiC) and gallium nitride (GaN); nanomaterials for the realization of magnetic components; using a modular principle to construct power electronic devices; applying artificial intelligence techniques to device lifecycle design; and the environmental aspects of design. The new materials allow the devices to operate at higher voltages, temperatures and frequencies, making them ideal for high-power applications and high-frequency operation. In addition, the development of integrated and modular power electronic systems that combine energy management, diagnostics and communication capabilities contributes to the more intelligent and efficient management of energy resources. This includes integration with the Internet of Things (IoT) and artificial intelligence (AI) for automated task solving and work optimization. [ABSTRACT FROM AUTHOR]

Plug-in hybrid electric vehicles (PHEVs) are becoming increasingly widespread due to their environmental benefits. However, PHEV penetration can overload distribution systems and increase operational costs. It is a major challenge to find an economically optimal solution under the condition of flattening load demand for systems. To this end, we formulate this problem as a two-layer optimization problem, and propose a hierarchical algorithm to solve it. For the upper layer, we flatten the load demand curve by using the water-filling principle. For the lower layer, we minimize the total cost for all consumers through a consensus-like iterative method in a distributed manner. Technical constraints caused by consumer demand and power limitations are both taken into account. In addition, a moving horizon approach is used to handle the random arrival of PHEVs and the inaccuracy of the forecast base demand. This paper focuses on distributed solutions under a time-varying switching topology so that all PHEV chargers conduct local computation and merely communicate with their neighbors, which is substantially different from the existing works. The advantages of our algorithm include a reduction in computational burden and high adaptability, which clearly has its own significance for the future smart grid. Finally, we demonstrate the advantages of the proposed algorithm in both theory and simulation. [ABSTRACT FROM AUTHOR]

Smart power grids suffer from electricity theft cyber-attacks, where malicious consumers compromise their smart meters (SMs) to downscale the reported electricity consumption readings. This problem costs electric utility companies worldwide considerable financial burdens and threatens power grid stability. Therefore, several machine learning (ML)-based solutions have been proposed to detect electricity theft; however, they have limitations. First, most existing works employ supervised learning that requires the availability of labeled datasets of benign and malicious electricity usage samples. Unfortunately, this approach is not practical due to the scarcity of real malicious electricity usage samples. Moreover, training a supervised detector on specific cyberattack scenarios results in a robust detector against those attacks, but it might fail to detect new attack scenarios. Second, although a few works investigated anomaly detectors for electricity theft, none of the existing works addressed consumers' privacy. To address these limitations, in this paper, we propose a comprehensive federated learning (FL)-based deep anomaly detection framework tailored for practical, reliable, and privacy-preserving energy theft detection. In our proposed framework, consumers train local deep autoencoder-based detectors on their private electricity usage data and only share their trained detectors' parameters with an EUC aggregation server to iteratively build a global anomaly detector. Our extensive experimental results not only demonstrate the superior performance of our anomaly detector compared to the supervised detectors but also the capability of our proposed FL-based anomaly detector to accurately detect zero-day attacks of electricity theft while preserving consumers' privacy. [ABSTRACT FROM AUTHOR]

Phasor measurement units (PMUs) are deployed at power grid nodes around the transmission grid, determining precise power system monitoring conditions. In real life, it is not realistic to place a PMU at every power grid node; thus, the lowest PMU number is optimally selected for the full observation of the entire network. In this study, the PMU placement model is reconsidered, taking into account single- and multi-capacity placement models rather than the well-studied PMU placement model with an unrestricted number of channels. A restricted number of channels per monitoring device is used, instead of supposing that a PMU is able to observe all incident buses through the transmission connectivity lines. The optimization models are declared closely to the power dominating set and minimum edge cover problem in graph theory. These discrete optimization problems are directly related with the minimum set covering problem. Initially, the allocation model is declared as a constrained mixed-integer linear program implemented by mathematical and stochastic algorithms. Then, the 0 / 1 integer linear problem is reformulated into a non-convex constraint program to find optimality. The mathematical models are solved either in binary form or in the continuous domain using specialized optimization libraries, and are all implemented in YALMIP software in conjunction with MATLAB. Mixed-integer linear solvers, nonlinear programming solvers, and heuristic algorithms are utilized in the aforementioned software packages to locate the global solution for each instance solved in this application, which considers the transformation of the existing power grids to smart grids. [ABSTRACT FROM AUTHOR]

With the construction and development of the smart grid, the power business puts higher requirements on the communication capability of the network. In order to improve the energy efficiency of the space–air–ground-integrated power three-dimensional fusion communication network, we establish an optimization problem for joint air platform (AP) flight path selection, ground power facility (GPF) association, and power control. In solving the problem, we decompose the problem into two subproblems, one is the AP flight path selection subproblem and the other is the GPF association and power control subproblem. Firstly, based on the GPF distribution and throughput weights, we model the AP flight path selection subproblem as a Markov Decision Process (MDP) and propose a multi-agent iterative optimization algorithm based on the comprehensive judgment of GPF positions and workload. Secondly, we model the GPF association and power control subproblem as a multi-agent, time-varying K-armed bandit model and propose an algorithm based on multi-agent Temporal Difference (TD) learning. Then, by alternately iterating between the two subproblems, we propose a reinforcement learning (RL)-based joint optimization algorithm. Finally, the simulation results indicate that compared to the three baseline algorithms (random path, average transmit power, and random device association), the proposed algorithm improves an overall energy efficiency of the system of 16.23%, 86.29%, and 5.11% under various conditions (including different noise power levels, GPF bandwidth, and GPF quantities), respectively. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

With the development of smart grids and new power systems, the combination of non-intrusive load identification technology and smart home technology can provide users with the operating conditions of home appliances and equipment, thus reducing home energy loss and improving users' ability to demand a response. This paper proposes a non-intrusive load decomposition model with a parallel multiscale attention mechanism (PMAM). The model can extract both local and global feature information and fuse it through a parallel multiscale network. This improves the attention mechanism's ability to capture feature information over long time periods. To validate the model's decomposition ability, we combined the PMAM model with four benchmark models: the Long Short-Term Memory (LSTM) recurrent neural network model, the Time Pooling-based Load Disaggregation Model (TPNILM), the Extreme Learning Machine (ELM), and the Load Disaggregation Model without Parallel Multi-scalar Attention Mechanisms (UNPMAM). The model was trained on the publicly available UK-DALE dataset and tested. The models' test results were quantitatively evaluated using a confusion matrix. This involved calculating the F1 score of the load decomposition. A higher F1 score indicates better model decomposition performance. The results indicate that the PMAM model proposed in this paper maintains an F1 score above 0.9 for the decomposition of three types of electrical equipment under the same household user, which is 3% higher than that of the other benchmark models on average. In the cross-household test, the PMAM also demonstrated a better decomposition ability, with the F1 score maintained above 0.85, and the mean absolute error (MAE) decreased by 5.3% on average compared with that of the UNPMAM. [ABSTRACT FROM AUTHOR]

Microgrid optimization scheduling, as a crucial part of smart grid optimization, plays a significant role in reducing energy consumption and environmental pollution. The development goals of microgrids not only aim to meet the basic demands of electricity supply but also to enhance economic benefits and environmental protection. In this regard, a multi-objective optimization scheduling model for microgrids in grid-connected mode is proposed, which comprehensively considers the operational costs and environmental protection costs of microgrid systems. This model also incorporates improvements to the traditional particle swarm optimization (PSO) algorithm by considering inertia factors and particle adaptive mutation, and it utilizes the improved algorithm to solve the optimization model. Simulation results demonstrate that this model can effectively reduce electricity costs for users and environmental pollution, promoting the optimized operation of microgrids and verifying the superior performance of the improved PSO algorithm. After algorithmic improvements, the optimal total cost achieved was CNY 836.23, representing a decrease from the pre-improvement optimal value of CNY 850. [ABSTRACT FROM AUTHOR]

This correction notice provides updates and corrections to a research paper on deep machine learning model-based cyber-attacks detection in smart power systems. The paper proposes a deep learning-based attack detection model that addresses system disturbances caused by natural events and cyber-attacks. It discusses the use of principal component analysis (PCA) for feature selection and the potential of deep learning-based and decision tree classifiers for detecting cyber-attacks in intelligent energy grids. The notice also includes updates to references, clarifications on the experimental data set, and feature selection based on PCA. The study evaluates the effectiveness of the proposed model using various assessment indexes and compares it to conventional methods, concluding that the proposed model is effective in detecting cyber-attacks and demonstrates good performance in detecting destructive attacks. [Extracted from the article]

This paper introduces a groundbreaking approach to demand response management, aiming to empower consumers through innovative strategies. The key contribution is the concept of "acquiring flexibility rights", wherein consumers engage with power aggregators to curtail energy usage during peak-load periods, receiving incentives in return. A flexibility right coefficient is introduced, allowing consumers to tailor their participation in demand response programs, ensuring their well-being. Additionally, a lighting intensity control system is developed to enhance residential lighting network efficiency. The study demonstrates that high-energy consumers, adopting a satisfaction factor of 10, can achieve over 61% in electricity cost savings by combining the lighting control system and active participation in demand response programs. This not only reduces expenses but also generates income through the sale of flexibility rights. Conversely, low-energy consumers can fully offset their expenses and accumulate over USD 33 in earnings through the installation of solar panels. This paper formulates an optimization problem considering flexibility rights, lighting control, and time-of-use tariff rates. An algorithm is proposed for a distributed solution, and a sensitivity analysis is conducted for evaluation. The proposed method showcases significant benefits, including cost savings and income generation for consumers, while contributing to grid stability and reduced blackout occurrences. Real data from a residential district in Tehran validates the method's effectiveness. This study concludes that this approach holds promise for demand response management in smart grids, emphasizing the importance of consumer empowerment and sustainable energy practices. [ABSTRACT FROM AUTHOR]

Microgrids are local energy production and distribution networks that can operate independently when disconnected from the main power grid thanks to the integration of power generation systems, energy storage units and intelligent control systems. However, despite their advantages, the optimal energy management of real microgrids remains a subject that requires further investigation. Specifically, an effective management of microgrids requires managing a large number of electrical variables related to the power generated by the microgrid's power supplies, the power consumed by the loads and the aspects of power quality. This study analyzes how we can monitor different variables, such as the active power, reactive power, power factor, total harmonic distortion and frequency in the loads of a microgrid, using high-precision power meters. Our empirical study, conducted using a functional microgrid comprising a hybrid wind–solar power system and several household appliances, demonstrates the feasibility of using low-cost and high-performance power meters with IoT functionality to collect valuable power quality and energy consumption data that can be used to control the microgrid operation. [ABSTRACT FROM AUTHOR]

This review paper synthesizes the recent advancements in voltage regulation techniques for active distribution networks (ADNs), particularly in contexts with high renewable energy source (RES) penetration, using photovoltaics (PVs) as a highlighted example. It covers a comprehensive analysis of various innovative strategies and optimization algorithms aimed at mitigating voltage fluctuations, optimizing network performance, and integrating smart technologies like smart inverters and energy storage systems (ESSs). The review highlights key developments in decentralized control algorithms, multi-objective optimization techniques, and the integration of advanced technologies such as soft open points (SOPs) to enhance grid stability and efficiency. The paper categorizes these strategies into two main types: analytical methods and computational methods. In conclusion, this review underscores the critical need for advanced analytical and computational methods in the voltage regulation of ADNs with high renewable energy penetration levels, highlighting the potential for significant improvements in grid stability and efficiency. [ABSTRACT FROM AUTHOR]

Metering anomalies not only mean huge economic losses but also indicate the faults of equipment and power lines, especially within the substation. As a result, metering anomaly diagnosis is becoming one of the most important missions in smart grids. However, due to the insufficient and imbalanced anomaly cases, identifying the anomalies in smart meter data accurately and efficiently remains challenging. Existing methods usually employ few-shot learning models in computer vision directly, which requires the rich experience of human experts and sufficient abnormal cases for training. It blocks model generalizing to various application scenarios. To address these shortcomings, we propose a novel framework for metering anomaly diagnosis based on few-shot learning, named FSMAD. Firstly, we design a fault data injection model to emulate anomalies, so that no abnormal samples are required in the training phase. Secondly, we provide a learnable variable transformation to reveal inherent relationships among various smart meter data and help FSMAD extract more efficient features. Finally, the deeper metric network is equipped to support FSMAD in obtaining powerful comparison capability. Extensive experiments on a real-world dataset demonstrate the advantages of our FSMAD over state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Electricity theft presents a significant financial burden to utility companies globally, amounting to trillions of dollars annually. This pressing issue underscores the need for transformative measures within the electrical grid. Accordingly, our study explores the integration of block chain technology into smart grids to combat electricity theft, improve grid efficiency, and facilitate renewable energy integration. Block chain's core principles of decentralization, transparency, and immutability align seamlessly with the objectives of modernizing power systems and securing transactions within the electricity grid. However, as smart grids advance, they also become more vulnerable to attacks, particularly from smart meters, compared to traditional mechanical meters. Our research aims to introduce an advanced approach to identifying energy theft while prioritizing user privacy, a critical aspect often neglected in existing methodologies that mandate the disclosure of sensitive user data. To achieve this goal, we introduce three distributed algorithms: lower–upper decomposition (LUD), lower–upper decomposition with partial pivoting (LUDP), and optimized LUD composition (OLUD), tailored specifically for peer-to-peer (P2P) computing in smart grids. These algorithms are meticulously crafted to solve linear systems of equations and calculate users' "honesty coefficients," providing a robust mechanism for detecting fraudulent activities. Through extensive simulations, we showcase the efficiency and accuracy of our algorithms in identifying deceitful users while safeguarding data confidentiality. This innovative approach not only bolsters the security of smart grids against energy theft, but also addresses privacy and security concerns inherent in conventional energy-theft detection methods. [ABSTRACT FROM AUTHOR]

As a core component of the smart grid, capacitive equipment plays a critical role in modern power systems. When defects occur, they pose a significant threat to the safety of both other equipment and personnel. Hence, it is of great significance to predict whether defects occur in capacitive equipment in advance. To achieve this goal, we propose a novel method that integrates the weight of evidence (WOE) feature encoding with machine learning (ML). Five models, including support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), multi-layer perceptron (MLP), and linear classification, are employed with WOE features for defect prediction. Furthermore, based on the prediction of equipment with defects, an additional prediction is conducted to determine the potential defect level of the equipment. Experimental results demonstrate that the performance of each algorithm significantly improves with WOE encoding features. Particularly, the RF model with WOE encoding features exhibits optimal performance. In conclusion, the proposed method offers a promising solution for predicting the occurrence of defects and the corresponding defect levels of capacitive equipment. It enables relevant personnel to focus on and inspect equipment predicted to be at risk of defects, thereby preventing major malfunctions. [ABSTRACT FROM AUTHOR]

To achieve sustainability and fulfill sustainable development goals, the digitalization of the power sector is vital. This study aims to examine how blockchain can be integrated into and enrich smart grids. In total, 10 research questions are explored. Scopus and Web of Science (WoS) were used to identify documents related to the topic. The study involves the analysis of 1041 scientific documents over the period 2015–2022. The related studies are analyzed from different dimensions including descriptive statistics, identification of the most common keywords and most widely used outlets, examination of the annual scientific production, the analysis of the most impactful and productive authors, countries, and affiliations. The advancement of the research focus and the most popular topics are also examined. Additionally, the results are analyzed, the main findings are discussed, open issues and challenges are presented, and suggestions for new research directions are provided. Based on the results, it was evident that blockchain plays a vital role in securing smart grids and realizing power sector digitalization, as well as in achieving sustainability and successfully meeting sustainable development goals. [ABSTRACT FROM AUTHOR]

Agricultural environments are usually characterized by height differences and tree shading, which pose challenges for communication in smart agriculture. This study focuses on optimizing the packet loss rate and power consumption of LoRa's practical communication quality. The research includes the investigation of the PHY anti-frame loss mechanism, encompassing PHY frame loss detection and the response mechanism between gateways and nodes. By implementing a closed loop for transmission and reception, the study enhances the communication network's resistance to interference and security. Theoretical performance calculations for the SX1278 radio frequency chip were conducted under different parameters to determine the optimal energy efficiency, reducing unnecessary energy waste. An experimental assessment of the packet loss rate was conducted to validate the practical efficacy of the research findings. The results show that the LoRa communication with the anti-frame loss mechanism and the optimal energy ratio parameter exhibits an adequate performance. In the presence of strong and weak interferences, the reception rates are maximally improved by 37.8% and 53.4%, with effective distances of 250 m and 600 m, corresponding to enhancements of 100 m and 400 m, respectively. This research effectively reduces LoRa energy consumption, mitigates packet loss, and extends communication distances, providing insights for wireless transmission in agricultural contexts. [ABSTRACT FROM AUTHOR]

Water is an essential service for the sustainable development and economic competitiveness of any country. The global water demand has increased substantially due to economic development, climate change, and rising population. The Internet of Things (IoT) and Information and Communication Technologies (ICT) can help conserve available water resources. Smart cities apply IoT to boost the performance and efficiency of urban facilities. Smart cities are towns created to use IoT and ICT (innovative technologies) such as smart water applications. Several studies on smart water technology have been conducted, but there is a need to review current research that leverages the IoT as a communication technology to design effective smart water applications. This review paper is aimed at presenting evidence on the current design of smart water applications. The study also covers publication statistics to increase collaboration between stakeholders. Findings show that various technologies such as microcontrollers, embedded programming languages, sensors, communication modules, and protocols are used by researchers to accomplish their aim of designing IoT-based smart water solutions. None of the publications employed the 5G mobile networks as a communication module for their smart water application development. Findings further show that the integration of 3D printing and solar energy into IoT-based smart water applications is revolutionary and can increase the sustainability of the systems. Future directions required to ensure that developed smart water applications are widely adopted to help conserve and manage water resources are suggested. [ABSTRACT FROM AUTHOR]

An essential building block of today's smart grids is the integration of various renewable power generators into the system. Renewable distributed generators (DGs) provide environment friendly energy at a low cost. In this work, a distributed economic dispatch (ED) system is considered for renewable DGs to address the scalability challenge and reduce single points of failure compared to a centralised ED system. A consensus control mechanism is devised to achieve the incremental cost (IC) consensus among the DGs and utilise a self-triggered control (STC) mechanism to reduce the communication of the distributed DG system. A lossy communication channel between local DGs is considered in this work, which can arise from power lines or congestion on noisy wireless communication channels. A regression-based estimation approach is proposed to account for lost or corrupted data. The proposed method is compared with a centralised ED system and periodic communication. The proposed STC approach achieves the same level of performance as periodic communication while using half the communication resources. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The growing development of communication technologies has given rise to the Internet of Things, which has led to the emergence of new cities, smart grids, and smart buildings, and the development of energy generation using renewable sources, as well as the emergence of new electrical loads such as the electric car. These advances give rise to the need for new media devices with remote communication, and require a greater control and monitoring of the state of the electrical grid in order to verify its correct state, as well as the detection of faults or alterations that are occurring in it due to these new generation systems or new loads. These remote, unsupervised measurement devices require galvanic isolation to protect the measurement and communication system, so that even if there is a break in the isolation, the integrity of the measurement and communication system is maintained. In addition, as it is a device prepared for multipoint measurement, the cost of the probe must be contained. This article details the design, implementation, and validation of a low-cost remote isolated differential voltage probe. This probe is intended for monitoring at network supply points, as well as for the verification of the European standard EN 50160 as a means of detecting disturbances in network behaviour. Its characteristics as a differential and isolated probe provide it with the possibility of floating voltage averaging, guaranteeing the integrity of the electronics of the low-voltage probe, i.e., the digitalisation and communication system. The measurements collected are sent via an MQTT protocol, which makes the remote probe a device compatible with the Internet of Energy. For the validation of the probe, a full functional test is performed, including FFT spectral analysis to verify the compliance of the mains voltage with the aforementioned European standard EN 50160. [ABSTRACT FROM AUTHOR]

Renewable energy sources, encompassing wind, solar, hydro, and geothermal options, are assuming an increasingly crucial role in the global energy landscape. They present a sustainable substitute for fossil fuels, effectively reducing greenhouse gas emissions and significantly contributing to the ongoing efforts against climate change. The widespread adoption of renewable energy technologies has undergone rapid expansion on a global scale, propelled by governmental policies, technological advancements, and decreasing costs. Despite their numerous advantages, renewable energy sources encounter challenges such as intermittent energy supply, storage solutions, and integration into existing power grids. Nevertheless, with sustained investment and innovation, renewable energy sources have the potential to become the predominant energy source of the future. This article conducts a bibliometric analysis of research on renewable energy sources in Poland and Germany. The analysis is grounded in publications catalogued in the Web of Science database, spanning the years from 1990 to 2023. The investigation delves into research topics related to renewable energy sources and scrutinizes the most frequently cited publications authored by individuals from these two countries. This bibliometric analysis stands out through its unique value proposition compared to other similar studies by placing a distinctive emphasis on critical research gaps, such as energy storage, smart grid technologies, and renewable energy in transportation. Additionally, the study's focus on the specific trajectories of Poland and Germany in renewable energy adoption, coupled with the identification of key institutions with the highest centrality index, provides unparalleled insights into the evolving landscape of sustainable energy research. The findings from this study can serve as a valuable source of information for policymakers, researchers, and other stakeholders interested in promoting the advancement and utilization of renewable energy sources. [ABSTRACT FROM AUTHOR]