Smart Electric Grid Research Articles

Optimal control algorithms for Gabon’s Smart Grid: Enhancing efficiency and sustainability

The adoption of smart grid technologies offers Gabon several opportunities to enhance energy efficiency and sustainability. This study investigates the use of optimum control algorithms to increase grid stability and enhance the utilization of renewable energy sources. Mathematical models and simulations are used to assess genetic algorithms in light of Gabon's unique energy situation. Systems known as electrical smart grids supply electricity to users directly from power plants in an effort to reduce costs, cut down on blackouts, and improve energy efficiency. Smart grids, or SGs, are well-known pieces of equipment because of their amazing capabilities, which include bi-directional communication, stability, power failure detection, and interconnectivity with appliances for monitoring. Many modern data and security management systems, including modeling, monitoring, optimization, and/or artificial intelligence, lead to SGs. Energy was once cheap, easily managed, and dependent only on fundamental elements. Demand has significantly increased as a result of the current circumstances, which has also increased Gabon's electricity expenses, the likelihood of a contingency, and the complexity of the electrical network. In Gabon, smart grids have shown to be the most practical and clever way to lessen these problems in recent years. An electrical network with information technology integrated is called a smart grid. According to this study, using genetic algorithms in a smart grid could lower Gabon's overall electricity prices. In order to meet demand, the proposed approach makes use of off-grid battery banks and renewable energy sources. The goal of GA optimization is the short-term time averaged power cost; factors that affect this include grid energy to satisfy load, battery discharge, and other factors. MATLAB software is used to solve the optimization problem over a 24-hour period of renewable input, real-time energy pricing, and load. The results are presented.

Three Duopoly Game-Theoretic Models for the Smart Grid Demand Response Management Problem

Demand response management (DRM) significantly influences the prospective advancement of electricity smart grids. This paper introduces three distinct game-theoretic duopoly models for the smart grid demand response management problem. It delineates several rational assumptions regarding the model variables, functions, and parameters. The first model adopts a Cournot duopoly form, offering a unique closed-form equilibrium solution. The second model adopts a Stackelberg duopoly structure, also providing a unique closed-form equilibrium solution. Following a comparison of the economic viability of the two model equilibria and an assessment of their sensitivity to parametric changes, the paper proposes a third model with a Cartel structure and discusses its advantages over the earlier models. Finally, the paper examines how demand forecasting affects the equilibrium quantities and pricing solutions of each model.

A data-driven model for the operation and management of prosumer markets in electric smart grids

The digital transformation of electric power systems requires forecasts and planning for optimal management, as well as real-time data streaming for the ongoing optimization of the system during operation. Recent research efforts have developed models for power system capacity planning, real-time monitoring and control, fault analysis, and energy efficiency assessment. However, those models are usually not integrated and do not combine operational data with management information and real-time decision-making. This paper conceives a data-driven model that integrates optimization and machine learning techniques for optimal operation and management of prosumer markets in electric smart grids. While classical optimization is used during day-ahead mode for operation planning, Gaussian Processes are used to predict demand forecasts for day-ahead and pre-dispatch modes while assimilating real-time measurements. The proposed approach is applied in a case study comprising a community manager coordinating a smart grid with prosumers operating thermal and renewable generators. Results highlight that the data-driven model helps achieve near-optimal operation of the smart grid in normal conditions while guaranteeing its reliability under disruptive events.

Application and Optimization of Multi-Factor Authentication and Biometric Technology in Power Grid Energy Network Access Control

With the sudden development in electronic and network technology, the power grid has emerged in several evolved nations and other areas with faster development. Energy storage can enhance the stability, resiliency and efficiency of the electric smart grid. The power grid transfers energy via bidirectional control of energy and information flow. In the power grid, a smart meter is an important component that is deployed on the user side to determine the consumption of energy regularly. However, the data about the usage of electricity leads to the leakage of the private information of the user, which threatens the privacy of the user. The attackers can change the in-transit message induce wrong information and sometimes cause disastrous action. Hence, it is important to assure data security and privacy of users by conducting authentication in power grid communication. An optimization technique named Puzzle Optimization Algorithm (POA) is designed for the efficient privacy-preserving aggregation approach with multi-factor authentication and a biometric scheme. Also, optimal key-based Elliptic Curve Cryptography (ECC) is used to encrypt the sensitive data that are securely stored. Authentication offers high security and texture analysis. In the registration phase, the privacy information of the person with their biometrics is the important feature point of the individual unique identification. Additionally, the designed approach is compared with other existing optimizations to analyse the functionality of the implemented technique. The simulation outcome defined that the suggested technique enhances user privacy and information security than other standard approaches.

Exploring the Feasibility of Quantum-Based Secure Communications for Nuclear Applications

Recent advancements in reactor designs could offer new revolutionary capabilities, including remote monitoring, increased flexibility, and reduced operation and maintenance costs. Embracing new digital technologies would allow for operational concepts such as semiautonomous or near-autonomous control, and two-way communications for real-time integration with the upcoming smart electric grid. However, such continuous data transmission from and toward a reactor site could potentially introduce new challenges and vulnerabilities, necessitating the prioritization of cybersecurity. Conventional information technology–based encryption schemes, which rely mostly on computational complexity, have been shown to be vulnerable to cyberattacks. With the advent of quantum computing, practically any asymmetric encryption could be potentially compromised. For example, it has been shown that a RSA-2048 bit key could be broken in 8 h. To address this challenge, we explore the feasibility of quantum key distribution (QKD) to secure communications. QKD is a physical layer security scheme relying on the laws of quantum mechanics instead of mathematical complexity. QKD promises not only unconditional security but also detection of potential intrusion, as it allows the communication parties to become aware of eavesdropping. To test the proposed hypothesis, a novel simulation tool (NuQKD) was developed to allow for real-time simulation of the BB84 QKD protocol between two remote terminals. A reference scenario is proposed, generic enough to cover various internal and external communication links to a reactor site. Using NuQKD, the internal and external data links were modeled, and receiver operating characteristic curves were calculated for various QKD configurations. A performance analysis was conducted, demonstrating that QKD can provide adequate secret key rates with low false alarms and has the potential of addressing the nuclear industry’s high standards of confidentiality for distance lengths up to 75 km of fiberoptics. Using a conservative estimation, QKD can provide up to 21.5 kbps of secret key rate for a distance of 1 km and 14.4 kbps at 10 km. The target secret key rates for the corresponding links were estimated at 16 kbps and 80 bps, respectively, based on the analysis of real data from a PUR-1 fully digital reactor. Consequently, QKD is shown to be compatible with existing and future point-to-point reactor communication architectures. These results motivate further study of quantum communications for nuclear reactors.

Anomaly detection based on LSTM and autoencoders using federated learning in smart electric grid

In smart electric grid systems, various sensors and Internet of Things (IoT) devices are used to collect electrical data at substations. In a traditional system, a multitude of energy-related data from substations needs to be migrated to central storage, such as Cloud or edge devices, for knowledge extraction that might impose severe data misuse, data manipulation, or privacy leakage. This motivates to propose anomaly detection system to detect threats and Federated Learning to resolve the issues of data silos and privacy of data. In this article, we present a framework to identify anomalies in industrial data that are gathered from the remote terminal devices deployed at the substations in the smart electric grid system. The anomaly detection system is based on Long Short-Term Memory (LSTM) and autoencoders that employs Mean Standard Deviation (MSD) and Median Absolute Deviation (MAD) approaches for detecting anomalies. We deploy Federated Learning (FL) to preserve the privacy of the data generated by the substations. FL enables energy providers to train shared AI models cooperatively without disclosing the data to the server. In order to further enhance the security and privacy properties of the proposed framework, we implemented homomorphic encryption based on the Paillier algorithm for preserving data privacy. The proposed security model performs better with MSD approach using HE-128 bit key providing 97% F1-score and 98% accuracy for K=5 with low computation overhead as compared with HE-256 bit key.

Smart grid electricity theft prediction using cascaded R-CNN and hybrid metaheuristic optimization

Smart grid electricity theft prediction using cascaded R-CNN and hybrid metaheuristic optimization

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems.

The evolving technologies regarding Unmanned Aerial Vehicles (UAVs) have led to their extended applicability in diverse domains, including surveillance, commerce, military, and smart electric grid monitoring. Modern UAV avionics enable precise aircraft operations through autonomous navigation, obstacle identification, and collision prevention. The structures of avionics are generally complex, and thorough hierarchies and intricate connections exist in between. For a comprehensive understanding of a UAV design, this paper aims to assess and critically review the purpose-classified electronics hardware inside UAVs, each with the corresponding performance metrics thoroughly analyzed. This review includes an exploration of different algorithms used for data processing, flight control, surveillance, navigation, protection, and communication. Consequently, this paper enriches the knowledge base of UAVs, offering an informative background on various UAV design processes, particularly those related to electric smart grid applications. As a future work recommendation, an actual relevant project is openly discussed.

Enhancing smart grid electricity prediction with the fusion of intelligent modeling and XAI integration

This study examines the vital role of accurate load forecasting in the energy planning of smart cities. It introduces a hybrid approach that uses machine learning (ML) to forecast electricity usage in homes, improving accuracy through the extraction of correlated features. The accuracy of predictions is assessed using loss functions and the root mean square error (RMSE). In response to increasing interest in explainable artificial intelligence (XAI), this paper proposes a framework for predicting energy consumption in smart homes. This user-friendly approach helps users understand their energy consumption patterns by employing shapley additive explanations (SHAP) techniques to provide clear explanations. The research uses gradient boosting and long short-term memory neural networks to forecast energy usage. In the context of sustainable urban development, it emphasizes the importance of conserving energy in homes. The paper explores AI and ML methods for predicting residential energy use, aiming to make socially meaningful impacts. It highlights the need to understand the factors affecting predictions to improve the accountability, reliability, and justification of decisions in energy optimization. Explainable AI techniques are used to gain insights into the prediction models and identify factors influencing household energy consumption. This research aids in decision-making processes related to electricity forecasting, advancing discussions on intelligent decision-making in power management, especially in smart grids and sustainable urban development.

Pseudocapacitive effects of polyoxometalate implanted on graphene oxide matrix with polypyrrole for symmetric Supercapacitor applications

Modern technological requirements emphasize designing and manufacturing electrochemical energy storage devices with high energy and power densities and longer cycle life. Supercapacitors with hybrid electrode materials have gained considerable attention as one of these systems due to their potential usage in futuristic applications such as electric vehicles and smart electric grids, among others. In this work, we synthesize potassium 9-tungsto-2-molybdo-1-vanadosilicate K5[α-SiMo2VW9O40]⋅10H2O and graphene oxide (GO) complex treating the latter as the supporting matrix for the former. We prepare the SiMo2VW9-polypyrrole (PPy) complex and then combine that with the GO matrix. The resulting nanohybrids GO-SiMo2VW9 and GO-PPy/SiMo2VW9 are found to have enhanced electrochemical properties when used in symmetric cells. Combining GO and pseudocapacitive materials can augment SC performance owing to their excellent redox properties. GO-SiMo2VW9 and GO-PPy/SiMo2VW9 showed 55.8 % and 85.5 % capacitive behavior at a scan rate of 10 mV/s, suggesting their use as high-performance pseudocapacitive materials as hybrid electrodes. GO-PPy/SiMo2VW9 electrode material shows a specific capacitance of 351.6 F/g with energy and power densities of 48.83 Wh/kg and 999.93 W/kg, respectively, at 0.5 A/g current density. Both the electrode materials yield capacitance retention of 60 % (GO-SiMo2VW9) and 80 % (GO-PPy/SiMo2VW9) after 5000 cycles at an 8A/g current density with almost 100 % coulombic efficiency, implying the stability of the electrode material.

An Optimum Load Forecasting Strategy (OLFS) for Smart Grids Based on Artificial Intelligence

Recently, the application of Artificial Intelligence (AI) in many areas of life has allowed raising the efficiency of systems and converting them into smart ones, especially in the field of energy. Integrating AI with power systems allows electrical grids to be smart enough to predict the future load, which is known as Intelligent Load Forecasting (ILF). Hence, suitable decisions for power system planning and operation procedures can be taken accordingly. Moreover, ILF can play a vital role in electrical demand response, which guarantees a reliable transitioning of power systems. This paper introduces an Optimum Load Forecasting Strategy (OLFS) for predicting future load in smart electrical grids based on AI techniques. The proposed OLFS consists of two sequential phases, which are: Data Preprocessing Phase (DPP) and Load Forecasting Phase (LFP). In the former phase, an input electrical load dataset is prepared before the actual forecasting takes place through two essential tasks, namely feature selection and outlier rejection. Feature selection is carried out using Advanced Leopard Seal Optimization (ALSO) as a new nature-inspired optimization technique, while outlier rejection is accomplished through the Interquartile Range (IQR) as a measure of statistical dispersion. On the other hand, actual load forecasting takes place in LFP using a new predictor called the Weighted K-Nearest Neighbor (WKNN) algorithm. The proposed OLFS has been tested through extensive experiments. Results have shown that OLFS outperforms recent load forecasting techniques as it introduces the maximum prediction accuracy with the minimum root mean square error.

Validation of Machine Learning-Aided and Power Line Communication-Based Cable Monitoring Using Measurement Data.

The implementation of power line communications (PLC) in smart electricity grids provides us with exciting opportunities for real-time cable monitoring. In particular, effective fault classification and estimation methods employing machine learning (ML) models have been proposed in the recent past. Often, the research works presenting PLC for ML-aided cable diagnostics are based on the study of synthetically generated channel data. In this work, we validate ML-aided diagnostics by integrating measured channels. Specifically, we consider the concatenation of clustering as a data pre-processing procedure and principal component analysis (PCA)-based dimension reduction for cable anomaly detection. Clustering and PCA are trained with measurement data when the PLC network is working under healthy conditions. A possible cable anomaly is then identified from the analysis of the PCA reconstruction error for a test sample. For the numerical evaluation of our scheme, we apply an experimental setup in which we introduce degradations to power cables. Our results show that the proposed anomaly detector is able to identify a cable degradation with high detection accuracy and low false alarm rate.

Chance-Constrained Optimization of Storage and PFC Capacity for Railway Electrical Smart Grids Considering Uncertain Traction Load

Chance-Constrained Optimization of Storage and PFC Capacity for Railway Electrical Smart Grids Considering Uncertain Traction Load

Maximizing Efficiency in Energy Trading Operations through IoT-Integrated Digital Twins.

The Internet of Things (IoT) has brought about significant transformations in multiple sectors, including healthcare and navigation systems, by offering essential functionalities crucial for their operations. Nevertheless, there is ongoing debate surrounding the unexplored possibilities of the IoT within the energy industry. The requirement to better the performance of distributed energy systems necessitates transitioning from traditional mission-critical electric smart grid systems to digital twin-based IoT frameworks. Energy storage systems (ESSs) used within nano-grids have the potential to enhance energy utilization, fortify resilience, and promote sustainable practices by effectively storing surplus energy. The present study introduces a conceptual framework consisting of two fundamental modules: (1) Power optimization of energy storage systems (ESSs) in peer-to-peer (P2P) energy trading. (2) Task orchestration in IoT-enabled environments using digital twin technology. The optimization of energy storage systems (ESSs) aims to effectively manage surplus ESS energy by employing particle swarm optimization (PSO) techniques. This approach is designed to fulfill the energy needs of the ESS itself as well as meet the specific requirements of participating nano-grids. The primary objective of the IoT task orchestration system, which is based on the concept of digital twins, is to enhance the process of peer-to-peer nano-grid energy trading. This is achieved by integrating virtual control mechanisms through orchestration technology combining task generation, device virtualization, task mapping, task scheduling, and task allocation and deployment. The nano-grid energy trading system's architecture utilizes IoT sensors and Raspberry Pi-based edge technology to enable virtual operation. The evaluation of the proposed study is carried out through the examination of a simulated dataset derived from nano-grid dwellings. This research analyzes the efficacy of optimization approaches in mitigating energy trading costs and optimizing power utilization in energy storage systems (ESSs). The coordination of IoT devices is crucial in improving the system's overall efficiency.

Towards stable electrode–electrolyte interphases: Regulating solvation structures in electrolytes for rechargeable batteries

AbstractRechargeable batteries are highly in demand to power various electronic devices and future smart electric grid energy storage. The electrode–electrolyte interphases play a crucial role in influencing the electrochemical performance of batteries, with the solvation chemistries of the electrolyte being particularly significant in regulating these interfacial reactions. However, the reaction mechanisms of electrolyte solvation and their specific functions in batteries are not yet fully understood. In this review, we embark on an exploration of the fundamental principles governing solvation and present a comprehensive overview of how solvation structures impact interfacial reactions at the electrode–electrolyte interface. We underscore the significance of interactions among cations, anions, and solvents in shaping electrolyte solvation structures. The primary strategies for controlling solvation structures are also discussed, including the optimization of salt concentrations, solvent interactions, and the introduction of functional cosolvents. Furthermore, we elucidate the oxidation/reduction reaction mechanisms of electrolyte components in different solvation structures and the new understanding of electrolyte additives in modulating interfacial chemistries in batteries. Additionally, we emphasize the importance of incorporating new characterization techniques and theoretical simulations to attain a deeper understanding of the intricate processes taking place within batteries. This review provides an in‐depth understanding in solvations and interphasial properties and new ideas for designing advanced functional electrolytes for rechargeable batteries.

Optimal energy scheduling of the smart stand-alone electrical distribution grid considering power-to-gas storage technology and reserve strategy

Optimal energy scheduling of the smart stand-alone electrical distribution grid considering power-to-gas storage technology and reserve strategy

IoT Orchestration-Based Optimal Energy Cost Decision Mechanism with ESS Power Optimization for Peer-to-Peer Energy Trading in Nanogrid

The Internet of things has revolutionized various domains, such as healthcare and navigation systems, by introducing mission-critical capabilities. However, the untapped potential of IoT in the energy sector is a topic of contention. Shifting from traditional mission-critical electric smart grid systems to IoT-based orchestrated frameworks has become crucial to improve performance by leveraging IoT task orchestration technology. Energy trading cost and ESS power optimization have long been concerns in the scientific community. To address these issues, our proposed architecture consists of two primary modules: (1) a nanogrid energy trading cost and ESS power optimization strategy that utilizes particle swarm optimization (PSO), with two objective functions, and (2) an IoT-enabled task orchestration system designed for improved peer-to-peer nanogrid energy trading, incorporating virtual control through orchestration technology. We employ IoT sensors and Raspberry Pi-based Edge technology to virtually operate the entire nanogrid energy trading architecture, encompassing the aforementioned modules. IoT task orchestration automates the interaction between components for service execution, involving five main steps: task generation, device virtualization, task mapping, task scheduling, and task allocation and deployment. Evaluating the proposed model using a real dataset from nanogrid houses demonstrates the significant role of optimization in minimizing energy trading cost and optimizing ESS power utilization. Furthermore, the IoT orchestration results highlight the potential for virtual operation in significantly enhancing system performance.

Impact of Power-Line Communication Radiation on Human Health

The advent of smart electrical grids (Smart-grids) has created the need for a reliable communication system within traditional electrical grids. Many technologies are available on the market for communication in smart-grids environment. Communication using PLC (Power-Line Communication or power-line carrier) technology is by far the best choice for operators of these smart-grids, because it uses the electrical cables already in place as a communication link. Unfortunately, since the electrical grid (frequency = 50/60 Hz) was not designed for the transport of high-frequency signals, in PLC mode (100 kHz - 30 MHz) the electrical cables behave like antennas and emit electromagnetic radiation in the nearby environment. This work is a contribution in the evaluation of electromagnetic radiation impact of PLC technology on human health. According to ICNIRP (International Commission for Non-Ionizing Radiation Protection), this type of assessment in done base on SAR (Specific Absorption Rate) and ∆T (tissue temperature raising during exposure to radiation). PLC cables radiation SAR and ∆T are confront with ICNIRP-limits (Guidelines). In order to cover many exposure scenario, interaction between the radiation of an Outdoor-PLC cable and a human head have been analyze using analytical approach (Maxwell equations) and numerical approach (simulation with Feko software). These analyses have demonstrated that exposure to Outdoor-PLC cable electromagnetic radiation is not harmful to human being. For a long-term exposure of human head to PLC cable electromagnetic radiation, The SAR is less than 1nW/Kg and ∆T under 10-5 °C (Far from ICNIRP limits). Analyses have also produce a simplified expression of PLC cable electromagnetic radiation SAR base on Biot-&-Savart law.

Security Management for an Advanced Metering Infrastructure (AMI) System of Smart Electrical Grids

Advanced Metering Infrastructure (AMI) plays a crucial role in enabling the efficient functioning of Smart Electrical Grids, but its successful implementation hinges on robust cybersecurity measures. To uphold data confidentiality and integrity, the deployment of an effective key management scheme (KMS) for multiple Smart Meters (SMs) and devices is imperative. The AMI exhibits unique characteristics, including storage and computation constraints in SMs, hybrid message transmission techniques, and varying participation levels in Demand Response (DR) projects, necessitating a tailored approach to security compared to other systems. In this research, we propose a KMS that is designed to address the specific security concerns of the AMI. The scheme comprises three key management procedures catering to the unicast, broadcast, and multicast modes of hybrid transmission. Given the resource limitations of SMs, we adopted simple cryptographic techniques for key creation and refreshing policies, ensuring efficiency without compromising on security. Furthermore, considering the variability of participants in DR projects, we established key refreshing policies that adapted to changing involvement. The effectiveness and security of the proposed KMS were rigorously evaluated, demonstrating its practical applicability and ability to safeguard the AMI ecosystem. The results of the evaluation indicate that our approach provides a viable and robust solution to the security challenges faced by AMI systems. By employing the proposed KMS, stakeholders can confidently deploy and manage AMI, ensuring the protection of sensitive data and maintaining the integrity of the Smart Electrical Grid.

Electricity Pricing and Its Role in Modern Smart Energy System Design: A Review

Energy is the foundation for human survival and socio-economic development, and electricity is a key form of energy. Electricity prices are a key factor affecting the interests of various stakeholders in the electricity market, playing a significant role in the sustainable development of energy and the environment. As the number of distributed energy resources (DERs) increases, today’s power systems no longer rely on a vertical market model and fixed electricity pricing scheme but instead depend on power dispatch and dynamic pricing to match supply and demand. This can help prevent significant fluctuations in supply–load imbalance and maintain system stability. Modern power grids have evolved by integrating information, communication, and intelligent control technologies with traditional power systems, giving rise to the concept of smart electric grids. Choosing an appropriate pricing scheme to manage large-scale DERs and controllable loads in today’s power grid become very important. However, the existing literature lacks a comprehensive review of electricity pricing in power systems and its transformative impact on shaping the energy landscape. To fill this void, this paper provides a survey on the developments, methods, and frameworks related to electricity pricing and energy trading. The review mainly considers the development of pricing in a centralized power grid, peer-to-peer (P2P) and microgrid-to-microgrid (M2M) energy trading and sharing, and various pricing methods. The review will cover the pricing schemes in modern power systems, particularly with respect to renewable energy sources (RESs) and batteries, as well as controllable load applications, and the impact of pricing schemes based on demand-side ancillary services (DSAS) for grid frequency support. Lastly, this review article describes the current frameworks and limitations of electricity pricing in the current energy market, as well as future research directions. This review should offer a great overview and deep insights into today’s electricity market and how pricing methods will drive and facilitate the future establishment of smart energy systems.