Smart Grid Applications Research Articles

Effects of Amplitude and Frequency of the Modulation Field on the Sensitivity for Low-Frequency Magnetic Field in Magnetoelectric Sensors

Magnetoelectric sensors based on piezoelectric/ferromagnetic composites have been investigated extensively due to their resonance-enhanced magnetoelectric coupling effect and high sensitivity for magnetic field, especially at resonant frequency. However, the sensitivity drops rapidly when the frequency drifts away from resonance, making it unsuitable for low frequency applications. Frequency modulation has been proposed as an effective method to up-convert the frequency of the desired signal into the mechanical resonance. In this work, we study the optimized amplitude and frequency of the modulation field, which improve the sensitivity at low frequency by two orders of magnitude without increasing the noise level. Magnetic field of 200 pT is detected at 10 Hz with a near-flat frequency response in the range of 1 - 100 Hz, showing promising potential for low frequency applications in smart grid and renewable energy.

Application of smart grid and non-dominated sorting genetic algorithm in adaptive energy-saving control of building lighting

Introduction: In the wave of urbanization, the increase of public lighting equipment in buildings has brought about more prominent problems of energy saving and consumption reduction.Methods: In order to solve the above problems, this paper designs a set of intelligent lighting solutions for digital buildings by combining the smart grid and non-dominant sorting genetic algorithms. Firstly, an intelligent lighting monitoring solution is constructed through ZigBee ad hoc network and sensor technology to monitor the relevant environment and lighting control of the laboratory building. Secondly, this paper uses the DIALux software network to build a public lighting light distributiona public lighting light distribution model in the building, and deeply studies the dimming control strategy of the system under the principle of making full use of sunlight and natural light.Results: The purpose ofself-adaptive intelligent control of desktop illuminance, finally using this scheme to achieve the optimal balance of desktop lighting.Discussion: The simulation experiment counts the power data of the intelligent lighting system under different weather conditions. The experimental results verify that the intelligent lighting control scheme can effectively reduce the output luminous flux of the lamps, thereby reducing power consumption.

Artificial Intelligence Applications in Electric Distribution Systems: Post-Pandemic Progress and Prospect

Advances in machine learning and artificial intelligence (AI) techniques bring new opportunities to numerous intractable tasks for operation and control in modern electric distribution systems. Nevertheless, AI applications for such grids as cyber-physical systems encounter multifaceted challenges, e.g., high requirements for the quality and quantity of training data, data efficiency, physical inconsistency, interpretability, and privacy concerns. This paper provides a systematic overview of the state-of-the-art AI methodologies in the post-pandemic era, represented by transfer learning, deep attention mechanism, graph learning, and their combination with reinforcement learning and physics-guided neural networks. Dedicated research efforts on harnessing such recent advances, including power flow, state estimation, voltage control, topology identification, and line parameter calibration, are categorized and investigated in detail. Revolving around the characteristics of distribution system operation and integration of distributed energy resources, this paper also illuminates prospects and challenges typified by the privacy, explainability, and interpretability of such AI applications in smart grids. Finally, this paper attempts to shed light on the deeper and broader prospects in the realm of smart distribution grids by interoperating them with smart building and transportation electrification

Digital Twin Concept, Method and Technical Framework for Smart Meters

Smart meters connect smart grid electricity suppliers and users. Smart meters have become a research hotspot as smart grid applications like demand response, power theft prevention, power quality monitoring, peak valley time of use prices, and peer-to-peer (P2P) energy trading have grown. But, as the carriers of these functions, smart meters have technical problems such as limited computing resources, difficulty in upgrading, and high costs, which to some extent restrict the further development of smart grid applications. To address these issues, this study offers a container-based digital twin (CDT) approach for smart meters, which not only increases the user-facing computing resources of smart meters but also simplifies and lowers the overall cost and technical complexity of meter changes. In order to further validate the effectiveness of this method in real-time applications on the smart grid user side, this article tested and analyzed the communication performance of the digital twin system in three areas: remote application services, peer-to-peer transactions, and real-time user request services. The experimental results show that the CDT method proposed in this paper meets the basic requirements of smart grid user-side applications for real-time communication. The container is deployed in the cloud, and the average time required to complete 100 P2P communications using our smart meter structure is less than 2.4 seconds, while the average time required for existing smart meter structures to complete the same number of P2P communications is 208 seconds. Finally, applications, the future development direction of the digital twin method, and technology architecture are projected.

Smart Meter Development Using Digital Twin Technology for Green Energy Distribution Optimization

This study proposes a digital twin (DT) approach and technical framework for smart meters to solve potential implementation and development problems and adapt to the new energy revolution trend and increase smart grid network security. DT models were deployed in the cloud and edge using a smart meter DT demonstration system. This paper evaluates the DT system's communication performance in real-time smart grid application through three dimensions: remote application service for smart grid user side, P2P transaction on the user side, and user real-time request service. This study's container-based decision tree strategy for smart meters meets the smart grid's real-time communication requirements for user-side applications.

Data-driven prediction models of photovoltaic energy for smart grid applications

Due to the low total cost of production, photovoltaic energy is a key component of installed renewable energy worldwide. However, photovoltaic energy is volatile in nature as it depends on weather conditions. This makes the integration, control, and operation of this type of energy difficult for network operators. Therefore, renewable energy forecasting is emerging as a key solution to effectively manage renewable energy in the power system. In this paper, we present a time series based energy forecasting system using a solar home database. We worked on Australian data recorded from 2010 to 2013. Three large databases have been combined to obtain better results. The objective is to test and compare the three prediction algorithms, namely: LSTM, FFNN and GRU. We also compared the performances via RMSE, MAE and R2_score. The results showed that neural networks accurately and reliably predict the energy consumed with R2_score up to 0.92 and MSE not exceeding 0.23, with a slight difference between the three models tested in this work.

Securing AMI-IoT networks against multiple RPL attacks using ensemble learning IDS and light-chain based prediction detection and mitigation mechanisms

ABSTRACT Advanced Metering Infrastructure (AMI) is one of the Internet of Things (IoT) enabled smart applications of smart grids. The Routing Protocol for Low Power and Lossy network (RPL) has been accepted to facilitate effective routing services for the AMI. However, numerous RPL attacks appear in AMI due to resource scarcity and dubious wireless medium, which significantly impedes the successful deployment of AMI-RPL. To enable secure and reliable AMI-RPL, this work proposes a novel Intrusion Detection System (IDS) named AMI Attack-aware Intelligent Machine learning IDS (AIMS). The primary objectives of AIMS are to predict, detect, and mitigate different types of RPL security attacks in the AMI environment. To predict the RPL attacks using the Stacked Ensemble (SE) machine learning model, a novel AMI-RPL Attack Dataset (ARAD) is generated by the Cooja simulator with the suitable pre-processing and the Spider Monkey Optimization (SMO) based feature selection. The advanced prediction of attack nodes improves the performance and significantly diminishes the future damages of AMI. The attack detection is based on immutable blocks of a light-chain model, and the cryptocurrency-based mitigation model effectively isolates the attackers. AIMS mechanism amplifies RPL security with high reliability and maximizes the AMI network lifetime by delivering superior results.

Collaborative LEO Satellites for Secure and Green Internet of Remote Things

The Internet of Remote Things (IoRT) supported by Low Earth Orbit (LEO) satellites is becoming indispensable for remote sensing, and it will play an important role in the forthcoming sixth generation (6G) communication network. In exploring its applications in remote mining and smart grid, etc., it is found that the implementation of IoRT faces challenges, including limited energy supplies, high mobility, and security vulnerabilities. To address these challenges, we propose employing collaborative LEO satellites to enable the implementation of secure and green IoRT. By combining the uplink signals received at collaborative LEO satellites, the Signal-to-Noise Ratio (SNR) can be significantly improved, so that relieving the transmit power requirement of the energy-limited terminal. Aiming at constructing a collaborative LEO satellite-based IoRT network, this paper introduces the system design principles regarding to frequency planning, waveform selection, collaboration strategies, and terminal design. In order to obtain optimal collaboration performance, we propose a signal coherent combining scheme to compensate Doppler shift, propagation delay, and phase differences. Furthermore, we propose a modified SUMPLE algorithm to estimate and compensate phase differences among satellites, which is applicable to Direct Sequence Spread Spectrum (DSSS) signal scheme. Simulation results demonstrate that our proposed algorithm outperforms the traditional SUMPLE algorithm in combining gain.

Design and analysis of trans Z-source inverter for electric vehicle applications using neural network-clustering

The presented paper analyzes the detailed design of a trans Z-source inverter (ZSI) with an input from solar photovoltaic (SPV) system. Increase in SPV uses requires highly efficient SPV enabled inverters under varying weather parameters are in high demand in modern smart grid applications. The SPV-trans ZSI has high conversion efficiency because of the single-stage voltage boost conversion capability. In contradiction, the conventional voltage source inverter (VSI) requires an additional step-up transformer to boost the output voltage of inverter. This reduces the efficiency by increasing the volume of set up and also increase the cost of the system. In the proposed SPV system it provides a better output against VSI. The increase in inverter output voltage is because of shoot through time period present in ZSI. It also reduces the voltage stress and harmonics content as compared to VSI. The proposed model has been validated through MATLAB simulation.

IOT Based Smart Energy Meter

Abstract: The labour involved in gathering utility metre readings for energy. The main goal of this project is to use the Internet of Things (IoT) to measure electricity consumption in home appliances and produce a bill automatically. IoT is an efficient and cost-effective way to transfer information about energy consumers wirelessly. A distributed architecture must be used to create the energy grid so that it can dynamically include various energy sources. IoT may be used for a variety of smart grid applications, including energy demand side management, distributed energy plant metering, smart metres for energy generation and consumption, and many areas of energy production.

Power Electronic Technology in Smart Grid Prospect

bstract: Electric energy is an extremely complex energy source that has become increasingly important in industry, agriculture, the economy, and daily life. The annual increase in electricity consumption symbolizes more stable economic growth in China, but there are still some problems. Power outages, low power quality, and complex power grid structures are issues that the power grid is facing. The development of power systems, the emergence of power electronics technology, and continuous research on power electronics technology can provide a feasible solution for alleviating and overcoming power problems. This article first summarizes the basic principles of power electronics technology in the construction of intelligent fields and then analyses the current examples of smart grid applications, including high-voltage direct current transmission, high-frequency technology, and intelligent switch technology. Finally, the possibility and potential for the development of power electronics technology were pointed out.

Global Simulation Model Design of Input-Serial, Output-Parallel Solid-State Transformer for Smart Grid Applications

This paper provides an overview of an early attempt at developing a simulation model on a solid-state transformer (SST) based on input-serial and output-parallel (ISOP) topology. The proposed SST is designed as a base for a smart grid (SG). The paper provides a theoretical review of the power converters under consideration, as well as their control techniques. Further, the paper presents a simulation model of the proposed concept with a PLECS circuit simulator. The proposed simulation model examines bidirectional energy flow control between the medium-voltage AC grid and DC smart grid, while evaluating power flow efficiency and qualitative indicators of the AC grid. After the completion of design verification and electrical properties analysis by the PLECS simulation models, the synthesis offers recommendations on the optimal layout of the proposed SST topology for smart grid application.

English

The electricity grid's upcoming update will be called Smart Grid. It covers all power production, management, transmission, distribution, and use aspects. It uses a variety of renewable resources to produce energy. The user and control unit can communicate in multiple directions. For smart grid applications, various wired and wireless technologies are available. We prefer wireless technology to wired technology because it has many advantages. Key wireless technology features are critical for rapid deployment, mobility, and inexpensive installation costs. This work uses solar cells, wind turbines, and water turbines to produce energy. Energy is produced by the generation unit and sent to the control unit. After that, the control unit gives to the user by their instructions. The critical attribute of the smart grid is that it improves the economy, agility, efficiency, reliability, and security.

Investigating the impact of cyber security risks and reliability scenarios under the influence of IoT on the Smart Grid environment

The modern infrastructure of the smart grid minimises the power losses and maintain the electric power flow closer to nominal values. It enables the bidirectional flow of energy between consumer end and generation end along with a communication flow. Due to increasing number of smart grid equipment, it is important to investigate security protection and appropriate recovery measures for the smart grid application. Cyber security measures are important to consider for improving the reliability and intelligence features of the smart grid. This paper investigated the security measurements and different standards to counter the future security threats to the smart grid power systems. False data injection and risk analysis is carried out along with an internet of thing (IoT) based security solution to increase the reliability of data flow and communication on the smart grid. The investigations are performed by creating a power system model on a MATALB/Simulink environment.

Hybrid precoding design for secure smart-grid enabled MIMO wireless communications in Industry 5.0

Secure and reliable wireless communication systems are essential for the successful integration of smart grid-enabled wireless communications and Industry 5.0 applications. This work proposes a hybrid precoding design for secure smart-grid multiple-input multiple-output (MIMO) wireless communications in Industry 5.0 to maximize the average secrecy capacity of the system while minimizing user outages crucial for maintaining the security and privacy of sensitive data in critical infrastructure. To achieve this, we develop a mathematical model that considers the impact of various system parameters, such as eavesdropping and interference caused by the active eavesdropper and noise, on the secrecy capacity. To find an efficient solution to the optimization problem, we propose an algorithm that decouples the original optimization problem into a series of subproblems and uses iterative techniques to find the optimal values, thereby improving computational efficiency. The hybrid precoding scheme is an effective technique for optimizing the design parameters of MIMO-based secure wireless communication systems. Our proposed approach provides a practical solution for achieving this optimization. Our numerical results demonstrate that our proposed scheme outperforms traditional benchmark schemes, maximizing the average secrecy capacity while minimizing user outages. Our work highlights the importance of secure wireless communication systems in Industry 5.0 and smart grid applications. The proposed approach provides an efficient method for designing secure wireless communication systems that can effectively address the unique challenges posed by these critical infrastructure systems.

Multi-model approach applied to meteorological data for solar radiation forecasting using data-driven approaches

The accuracy of the solar forecasting model depends upon various factors such as attributes of the data, time horizon, time interval, length of the data, etc. This means attributes of data selection are an important feature of any solar forecasting model. A lot of research has been done on solar forecasting using deep learning and machine learning approaches, providing better accuracy for a particular type of model and data. However, how to accurately apply the multi-model techniques in the data-driven approaches of the solar radiation prediction model is still a challenging issue due to the complexity of the temporal resolution, length, and attribute selection of the data. This paper proposes a multi-model technique using data-driven approaches to get an improvised solar forecasting model with respect to the attributes of data. In this research, a multi-model method based on data-driven approaches is developed for short-term solar radiation forecasting. Three years of weather data with different temporal resolutions taken from reliable online sources were used for training and testing in this work. The weather data considered various input parameters such as temperature, humidity, pressure, wind speed, and wind direction; solar radiation data is considered the model’s target variable. This work is a two-stage process. In the first stage, multi-model methods are analyzed based on solar forecasts at different temporal resolutions, and best-performing models are selected. The optimization technique is applied to the best model in the second stage to obtain an improvised solar forecasting model. It is observed that the multi-model forecasts provide better accuracy in terms of R2, nRMSE, and MAE than the conventional forecasting model. In this work, the performance parameters of nRMSE = 0.34, MAE = 4.71, and R2 = 0.93 are obtained before optimization, and nRMSE = 0.25, MAE = 1.39 and R2 = 0.96 are obtained after optimization in the testing stage for an hourly dataset. For daily datasets, the performance parameters nRMSE = 0.24, MAE = 3.19, and R2 = 0.93 are obtained before optimization, and nRMSE = 0.16, MAE = 2.61, and R2 = 0.95 are obtained after optimization in the testing stage. As a result, the proposed algorithm focused on an efficient model to predict solar radiation. Accurate solar radiation prediction models are highly in demand in various smart grid applications. Therefore, the proposed work may help in suggesting a suitable model for solar energy forecasting.

Marginalized particle filtering for online parameter estimation of PEMFC applied to hydrogen UAVs

Proton exchange membrane fuel cells (PEMFCs) are an emerging candidate for clean power generation, with applications in smart grids, vehicles, and unmanned aerial vehicles. Accurate modeling of the power characteristics of PEMFC is required for system management purposes, where an electro-chemical model with unknown parameters is often adopted. Estimation of the unknown parameters is challenging as operating condition shifts and system degradation will lead to variation in PEMFC characteristics, particularly in the presence of system nonlinearities and noise. In this paper, a novel online parameter estimation approach for PEMFC is proposed based on the marginalized particle filtering approach. By introducing a filter derivation based on Bayesian inference and estimating the linear and nonlinear parameters separately, the marginalized approach has reduced computation cost compared with conventional particle filters. Estimation accuracy of the proposed approach is validated by experimental and simulation results, where superior accuracy compared with extended Kalman filter is obtained. Furthermore, a self-designed hydrogen quadrotor was flight-tested and energy management studies were conducted to assess the performance of the proposed estimator in hydrogen unmanned aerial vehicle applications using real flight data.

Grid Nodes Selection Strategies for Power Quality Monitoring

In the past few years, technical progress has determined traditional electric power system conversion to Smart Grids, and consequently empowered a worldwide renaissance in the forgotten PQ field. Since the installation of PQ monitors is currently associated with high investments and operational costs, it is inappropriate to install a monitor on each grid busbar. Hence, grid operators must establish the best cost–benefit scenario for monitors installation and achieve maximal observability with a limited number of analyzers. Firstly, this paper presents a review of the state-of-the-art of displacement strategies, and discusses them regarding the tendencies of node selection criteria, the test schemes used, and the grid size. Secondly, the relevant fundamental issues which must be solved in the future in order to eliminate restrictions in PQ monitors’ allocation planning are presented and discussed. These issues concern the treatment, interpretation, and assessment of PQ events, measurement chain technical features, PQ’s role in the grid planning stage, communication technologies, and other remote monitoring aspects, and integration with other Smart Grid applications. The provided insights are based on experience which has been obtained during the PQ measurement campaign in the Lithuanian DSO grid. Finally, a PQ system development strategy—both short-term and long-term perspectives—in the Lithuanian distribution grid is presented, including monitors allocation criteria.

Formalizing the Semantics of DDS QoS Policies for Improved Communications in Distributed Smart Grid Applications

Quality communication is a major challenges in large-scale and distributed smart grid applications. Several protocols and middleware have been proposed to address communication quality issues in those applications. DDS is a standard data-centric middleware for publish/subscribe communication. It has been proposed for smart grid to address both connectivity and communication quality issues. DDS provides multiple quality of service (QoS) policies to address reliability, latency, and data availability. One of the main challenges in adopting the standard in smart grids is the complexity of adopting and tailoring its QoS policies. This is because those policies are described informally introducing ambiguities, which hinders the precise implementation of DDS. To address this, we formalize the descriptions of DDS QoS policies using the object constraint language (OCL). We also clearly defined the design structural relations among DDS entities and QoS policies. In the process, we analyzed the dependencies among QoS policies and we built clear and concise structural relations. We then proposed feature modeling and a management layer to facilitate QoS tuning and to reduce development and configuration complexity. We implemented the proposed approach in a simulated power consumption domain. The results show that the approach improves the development process. They also show that the approach significantly improves the performance of DDS-enabled applications.

Power Distribution System Outage Management Using Improved Resilience Metrics for Smart Grid Applications

Smart grid systems play a significant role in improving the resilience of distribution systems (DSs). In this paper, two strategies are proposed for implementation of a smart grid application: (a) a network reconfiguration and (b) a network reconfiguration with mobile emergency generator (MEGs) deployment. An improved set of resilience metrics to quantify and enhance the resiliency of distribution systems (DSs) is developed for the proposed optimization. The metrics aim to determine a suitable strategy and the optimal number and capacity of MEGs to restore the disconnected loads through the development of several microgrids. These metrics are then aggregated with the proposed strategy to develop an automated solution provider. The objective is to maximize system resilience considering the priority loads. The proposed resilience metrics are tested on the IEEE 33-Bus radial DSs. The case studies conducted proved the performance of the proposed power outage management strategy and resilience metrics in maximizing system resiliency for smart grids.