Grid Applications Research Articles

Showcasing Data Management Challenges for Future IoT Applications with NebulaStream

Data management systems will face several new challenges in supporting IoT applications during the coming years. These challenges arise from managing large numbers of heterogeneous IoT devices and require combining elastic cloud and fog resources in unified fog-cloud environments. In this demonstration, we introduce a smart city simulation called IoTropolis and use it to create interactive eHealth and Smart Grid application scenarios. We use these scenarios to showcase three key challenges of unified fog-cloud environments. Furthermore, we demonstrate how our recently proposed data management system for the IoT NebulaStream addresses these challenges. Visitors to our demonstration can configure and interact with the scenarios to manage electricity usage in IoTropolis or to distribute patients across different hospitals. Thereby, visitors can actively engage with the challenges showcased by IoTropolis and utilize NebulaStream to address them. As a result, our demonstration enables visitors to experience data management for future IoT applications.

带多局部约束的改进push-sum框架分布式优化及其在智能电网中的应用

带多局部约束的改进push-sum框架分布式优化及其在智能电网中的应用

Significantly enhanced flashover voltage of epoxy in vacuum by graded surface roughness modification

AbstractThe flashover strength of epoxy (EP) insulations in the High voltage direct current applications of future energy grids can be improved by tailoring their surface condition. This work aims to improve the DC surface flashover characteristics of EP after being treated with sandpaper of different gradings. Samples with virgin EP and homogenously modified EP considering varying surface roughness (Ra = 0.54, 3.16, 5.24, and 8.35 μm) are prepared. Different experimental characterisations, such as water contact angle, surface intrinsic conductivity, surface voltages, flashover strength, and trap distributions are conducted and evaluated to analyse the difference between virgin and treated EP. Moreover, based on the obtained experimental results of homogenously treated EP and theoretical analysis, the concept of surface functionally graded materials (SFGMs) is put forward. The flashover voltages of homogenously treated EP are augmented significantly compared to virgin EP regardless of the voltage polarity and enhanced by enhancing the surface roughness. The sample TModel‐C with SFGM design shows a 45.02% and 43.75% improvement in the negative and positive flashover voltages than that of the virgin EP. In the end, COMSOL simulations are conducted to justify the experimental findings and to analyse the difference between virgin and modified samples in terms of electric field distribution.

COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications

Power prediction is now a crucial part of contemporary energy management systems, which is important for the organization and administration of renewable resources. Solar and wind powers are highly dependent upon environmental factors, such as wind speed, temperature, and humidity, making the forecasting problem extremely difficult. The suggested composite model incorporates Long Short-Term Memory (LSTM) and Swarm Intelligence (SI) optimization algorithms to produce a framework that can precisely estimate offshore wind output in the short term, addressing the discrepancies and limits of conventional estimation methods. The Coati optimization algorithm enhances the hyper parameters CNN-LSTM. Optimum hyper parameters improvise learning rate and performance. The day-ahead and hour-ahead short-term predictions RMSE can be decreased by 0.5% and 5.8%, respectively. Compared to GWO-CNN-LSTM, LSTM, CNN, and PSO-CNN-LSTM models, the proposed technique achieves an nMAE of 4.6%, RE 27% and nRMSE of 6.2%. COA-CNN-LSTM outperforms existing techniques in terms of the Granger causality test and Nash-Sutcliffe metric analysis for time series forecasting performance, scores are 0.0992 and 0.98, respectively. Experimental results show precise and definitive wind power-making predictions for the management of renewable energy conversion networks. The presented model contributes positively to the body of knowledge and development of clean energy.

Applications of IoT and digital twin in electrical power systems: A comprehensive survey

AbstractThis paper reviews the applications of Internet of Things (IoT) and digital twin technology in electrical power systems. It begins by discussing the generalized IoT value chain, followed by the terminology of smart grid, with clarifying the role of IoT‐systems and the digital twin structure within the Smart Grid. A comparison between different short‐range and long‐range transports is presented. The paper then discusses the use of IoT and digital twin technology for effective energy management with applications in smart homes, smart buildings, smart grids, smart industries (Industry 4.0), smart transportation and smart cities. Additionally, the paper explores the use of IoT and digital twin technology for condition monitoring and diagnosis (CMD) in electrical power systems. Three different cases are presented for CMD, that is, CMD of power transformers, CMD of electrical grids and CMD of substations. IoT and digital twin applications are also highlighted in power electronic systems. Finally, the paper discusses the challenges and opportunities of applying IoT and digital twin technology to electrical power systems and provides recommendations for future research.

A new approach to seasonal energy consumption forecasting using temporal convolutional networks

There has been a significant increase in the attention paid to resource management in smart grids, and several energy forecasting models have been published in the literature. It is well known that energy forecasting plays a crucial role in several applications in smart grids, including demand-side management, optimum dispatch, and load shedding. A significant challenge in smart grid models is managing forecasts efficiently while ensuring the slightest feasible prediction error. A type of artificial neural networks such as recurrent neural networks, are frequently used to forecast time series data. However, due to certain limitations like vanishing gradients and lack of memory retention of recurrent neural networks, sequential data should be modeled using convolutional networks. The reason is that they have strong capabilities to solve complex problems better than recurrent neural networks. In this research, a temporal convolutional network is proposed to handle seasonal short-term energy forecasting. The proposed temporal convolutional network computes outputs in parallel, reducing the computation time compared to the recurrent neural networks. Further performance comparison with the traditional long short-term memory in terms of MAD and sMAPE has proved that the proposed model has outperformed the recurrent neural network.

Review and Evaluation of Reinforcement Learning Frameworks on Smart Grid Applications

With the rise in electricity, gas and oil prices and the persistently high levels of carbon emissions, there is an increasing demand for effective energy management in energy systems, including electrical grids. Recent literature exhibits large potential for optimizing the behavior of such systems towards energy performance, reducing peak loads and exploiting environmentally friendly ways for energy production. However, the primary challenge relies on the optimization of such systems, which introduces significant complexities since they present quite dynamic behavior. Such cyberphysical frameworks usually integrate multiple interconnected components such as power plants, transmission lines, distribution networks and various types of energy-storage systems, while the behavior of these components is affected by various external factors such as user individual requirements, weather conditions, energy demand and market prices. Consequently, traditional optimal control approaches—such as Rule-Based Control (RBC)—prove inadequate to deal with the diverse dynamics which define the behavior of such complicated frameworks. Moreover, even sophisticated techniques—such as Model Predictive Control (MPC)—showcase model-related limitations that hinder the applicability of an optimal control scheme. To this end, AI model-free techniques such as Reinforcement Learning (RL) offer a fruitful potential for embedding efficient optimal control in cases of energy systems. Recent studies present promising results in various fields of engineering, indicating that RL frameworks may prove the key element for delivering efficient optimal control in smart buildings, electric vehicle charging and smart grid applications. The current paper provides a comprehensive review of RL implementations in energy systems frameworks—such as Renewable Energy Sources (RESs), Building Energy-Management Systems (BEMSs) and Electric Vehicle Charging Stations (EVCSs)—illustrating the benefits and the opportunities of such approaches. The work examines more than 80 highly cited papers focusing on recent RL research applications—between 2015 and 2023—and analyzes the model-free RL potential as regards the energy systems’ control optimization in the future.

Fault diagnosis and protection strategy based on spatio-temporal multi-agent reinforcement learning for active distribution system using phasor measurement units

Active distribution system (ADS) requires intelligent sensors to provide real-time data. Due to the harmonic distortion and sparse reward function, the multi-agent reinforcement learning strategy has the fuzzy characteristic and slow convergence. This work proposes a model-free spatio-temporal multi-agent reinforcement learning (STMARL) strategy for the spatio-temporal fault diagnosis and protection. The augmented-state extended Kalman filter tracks spatial–temporal sequences measured by phasor measurement unit (PMU) and feed into the diagnosis model. The supervised multi-residual generation learning (SMGL) model is constructed to diagnose the single-phase-to-ground fault. Based on spatio-temporal sequences, the SMGL diagnosis model integrates the ADS protection as a Markov decision process and the protection operation is quantified as the STMARL reward. In the hybrid multi-agent framework, the STMARL protection strategy converges faster based on the higher-level agent suggestion without the global reward. The STMARL protection strategy is validated in the IEEE 34-bus distribution test system with 10 PMUs. Comparing with the SOGI, WNN, Sarsa and DDPG algorithms, in the common fault conditions, the STMARL protection strategy shows better performance in the high dynamic environment with the response time 1.274 s and the diagnosis accuracy rate 97.125%. The STMARL diagnosis and protection strategy guides ADS in a stable operation coordinate with all PMUs, which lays foundation for the synchronous measurement application in the smart grid.

Deep learning methods for intra-day cloudiness prediction using geostationary satellite images in a solar forecasting framework

Accurate solar resource forecasting remains a challenge. Electricity grid applications require both days-ahead and intra-day prediction. Satellite-based methods are known to be the best option for hourly intra-day solar forecasts up to some hours ahead. An adapted Deep Learning (DL) method has been recently reported to outperform the traditional Cloud Motion Vectors (CMV) strategy. This article analyzes the utilization of a well-documented computer vision DL architecture, the U-Net in various forms, for the satellite Earth albedo forecast problem (cloudiness), a straightforward proxy for solar irradiance forecast. It is shown that the U-Net performs better than advanced and optimized CMV techniques and previous art IrradianceNet, setting it at the state-of-the-art. The tests are done over the Pampa Húmeda region of southeast South America, an area in which challenging cloud conditions are frequent. The data for this study are GOES-16 visible channel images. These images present a finer spatial (≃1 km/pixel) and temporal (10 min) resolution than previously explored data sources for solar forecasting. Moreover, the image size used here is ×4 bigger (1024 × 1024 pixels) and the predictions reach further into the future (5 h) than in previous works. The analysis includes several ablation studies, involving different architectures, optimization objectives, inputs, and network sizes. The U-Net is optimized for direct and differential image prediction, being the latter a better-performing option. More notably, the U-Net models are shown to be able to predict cloud extinction, something that has been a barrier for CMV methods.

A Review of Modeling and Applications of Energy Storage Systems in Power Grids

As the penetration of variable renewable generation increases in power systems, issues, such as grid stiffness, larger frequency deviations, and grid stability, are becoming more relevant, particularly in view of 100% renewable energy networks, which is the future of smart grids. In this context, energy storage systems (ESSs) are proving to be indispensable for facilitating the integration of renewable energy sources (RESs), are being widely deployed in both microgrids and bulk power systems, and thus will be the hallmark of the clean electrical grids of the future. Hence, this article reviews several energy storage technologies that are rapidly evolving to address the RES integration challenge, particularly compressed air energy storage (CAES), flywheels, batteries, and thermal ESSs, and their modeling and applications in power grids. An overview of these ESSs is provided, focusing on new models and applications in microgrids and distribution and transmission grids for grid operation, markets, stability, and control.

Asymmetric Trapezoidal Wave Modulation of Modular Multilevel Resonant DC/DC Converter for Current Stress Optimization

A modular multilevel resonant dc/dc converter (MMRDC) possesses high power and high voltage rating, which is adopted for high step-ratio interconnection in dc grid applications. However, the current stress and reactive power of MMRDC are high with the conventional quasi-square-wave modulation scheme when in a wide voltage range and light load conditions. Hence, this article proposes an optimized asymmetric trapezoidal wave (ATW) modulation scheme that reduces current stress over wide load and voltage ranges by separately modulating the duty cycles of string output voltage rising and falling processes. The time-domain mathematical model of MMRDC with the ATW modulation scheme is established. Then, based on the soft switching characteristic and reactive current analysis, the optimized operating point of MMRDC can be derived while considering low current stress and zero voltage switching constraint of low voltage side full-bridge switches. On this basis, the numerical solutions of optimized control variables are obtained, and a control block diagram is presented for the practical implementation of the optimized ATW modulation scheme. Finally, a 4kW MMRDC prototype is constructed, and the experimental results verify the correctness and effectiveness of the analysis and the optimized ATW modulation scheme.

Thermal Energy Storage Technology in solar Energy Utilization: A Review

Thermal energy storage (TES) is a promising technique that conserves accumulated thermal energy from heat and cold mediums, making it available for future use. This method allows energy to be stored under various conditions, presenting an attractive solution for harnessing solar radiation efficiently and in large quantities. TES is becoming increasingly important as renewable electricity integration grows and the demand for low-carbon energy rises. Concentrating solar power plants benefit from TES, enabling them to store excess solar energy during peak times and utilize it during periods of lower solar radiation, ensuring a continuous power supply. Additionally, standalone TES systems for grid applications are gaining popularity, especially with the declining costs of renewable energy. These systems facilitate energy integration and help meet the increasing energy demands sustainably. Phase change materials (PCMs) play a vital role in thermal energy storage systems, contributing to effective energy conservation. Their high thermal storage density and moderate temperature volatility make them ideal for storing and releasing significant amounts of thermal energy. As a result, PCMs have gained popularity in this field. This study examines various aspects of thermal energy storage systems, with a particular focus on research articles related to storage materials and methods. It explores sensible heat storage, which involves altering material temperatures to store energy, latent heat storage that capitalizes on phase change properties like those of PCMs, chemical storage utilizing chemical reactions for energy storage, and cascaded thermal storage systems that combine different methods for optimized energy storage. By exploring these areas, this research aims to advance the understanding of thermal energy storage and contribute to the ongoing efforts in achieving sustainable and low-carbon energy solutions for the future.

Efficient Cluster Tree Topology Operation and Routing for IEEE 802.15.4-Based Smart Grid Networks

Wireless sensor networks (WSNs) have been utilized as communication infrastructure for smart grid applications. The primary requirement of WSNs for smart grid applications is to transmit delay-critical data from smart grid assets ether at the maximum rate or by reducing collision rates. Additionally, WSNs should utilize the limited resources of the network to provide the required long-term QoS. The achievement of these objectives requires a remarkable design of WSN protocols to satisfy the requirements of smart grid applications. In this study, a multi-channel cluster tree protocol is proposed to prevent collisions and increase network performance. In the proposed scheme, the cluster head serves to broadcast a beacon frame containing information on the allocated channels and time slots. This enables the new node to determine its channel and timeslot. A performance analysis reveals that the proposed scheme can achieve a low end-to-end delay and low collision rates compared with the well-known IEEE 802.15.4 MAC protocols widely used in the literature to provide QoS to smart-grid applications.

Distributed fault-tolerant control for heterogeneous multiagent systems and application in wireless power transfer grid

This paper investigates the incipient fault-tolerant problem of heterogeneous multiagent systems and applies the proposed method to the wireless power transfer grid. Firstly, a novel extended state observer is designed for every follower system, which not only relative output information of its agent but also contains output information of neighboring agents. Then, a state estimation feedback control scheme is proposed to compensate for the time delay and incipient faults effectively. It is worth noting that the proposed observer-based control strategy distributes the weight of the output information error. Therefore, the observer has higher adjustability and more substantial applicability. Further, we make a consistent analysis of the system under the proposed control scheme with the Lyapunov–Krasovskii theory that the sufficient conditions can be formed for the stability of the system. Finally, the validity of the proposed control scheme is demonstrated through numerical and application examples.

Data security of machine learning applied in low-carbon smart grid: A formal model for the physics-constrained robustness

Towards the low-carbon goal, a smart grid features remote connection, data sharing, and cyber–physical integration to increase the flexibility of energy supplies, to reduce electricity wastage, and to enhance safety under intermittent renewables. In this context, machine learning (ML) is increasingly employed in smart grids to solve tasks that require deep data analytics. However, it is well recognized that ML models are vulnerable to adversarial examples that might be contained in open shared data, which creates opportunities for potential attackers to launch cyberattacks. In particular, system operating states acting as critical inputs to ML-based smart grid applications (MLsgAPPs) can be manipulated by malicious actors to mislead the system operator into making wrong decisions that in turn may cause major blackouts and other damaging cascading events. It is thus imperative to advance vulnerability assessment for MLsgAPPs.In this paper, we propose a physics-constrained robustness evaluation framework for MLsgAPPs, which is a first attempt of using tree ensemble (TE) models. Unlike adversarial attacks against prior studied image-classification tasks, adversarial examples against TE-based smart grid applications (TEsgAPPs) must not only cheat human intuition but also follow laws of physics and bypass error-checking mechanisms for power systems. Thus, formulation of the traditional robustness evaluation problem must be reconsidered to account for domain-specific misclassification, physical limit, power balance, and error-bypass constraints, in order to obtain tight lower bounds of the magnitudes of tolerated adversarial perturbations. We adopt a formal modeling approach to construct a discrete tree structure and to specify the model evasion conditions. We propose an efficient robustness evaluation method by transforming variables considering ℓ1 and ℓ∞ norms, followed by a generalization that expands the variable space to admit other types of norms. Using TE-based power system security assessment as an example, comprehensive simulations are conducted for evaluating the physics-constrained robustness under different TE models and model parameters.

A systematic review on performance evaluation metric selection method for IoT-based applications

Internet of Things (IoT) has evolved many day-to-day objects into smart objects, which humans can control when they are connected via Internet. Its practical implementation came into existence a few years back. Since the advent of IoT, it has tackled many challenges such as management, tracking, observation, etc. It has contributed to various fields such as Healthcare 4.0, smart grid, smart agriculture, Intelligent Transportation Systems (ITS), smart cities, smart homes, smart cars, and many more. It has applications through almost all domains, but the question arises: the performance evaluating parameters about specific IoT applications to consider to detect flaws in it. As per literature, there exist no such article helps beginners in selecting performance evaluation parameters for IoT-enabled applications. Motivated from the aforementioned facts, this paper presents a survey on performance evaluation metrics’ systematic study for various IoT applications. We then graphically present the comparison of multiple parameters of domains security, energy efficiency, data storage, and network performance. From the analysis, we can infer that, in the smart city, latency is the most usable parameter, i.e., 77%. In contrast, in Healthcare 4.0, equal importance is given to latency and network bandwidth, which is 50%. Also, in Industry 4.0, scalability is the most opted performance parameter by the researchers, which is 100%. The smart grid application uses latency and delay are the most valuable parameters, i.e., 60%.

DDoS: Distributed denial of service attack in communication standard vulnerabilities in smart grid applications and cyber security with recent developments

Smart grid system is evident with control technologies and digital communications systems. The cyber–physical system is a critical infrastructure connected with complex drives and devices. The modern smart grid’s prominent communication standard adopts high scalability, supports numerous communication devices’ input/outputs, and has multi-vendor interoperability. Cyber-attack leads to an unstable power grid system and enormous economic loss. Throughout different cyber-attack, distributed denial of service attack is mostly destructive to smart grid infrastructure. This paper investigates smart grid cyber security systems and the communication vulnerabilities of other communication protocols. A comprehensive study on distributed denial of service attack techniques and detection approaches is present. Finally, examine a new hybrid machine learning-based distributed denial of service attack detection technique for a sustainable smart grid system.

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