Electrical Research Articles

Reliability and line loading enhancement of distribution systems using optimal integration of renewable energy and compressed air energy storages simultaneously under uncertainty

Recently, with the significant growth of load demand, further interest in renewable energy sources (RESs) incorporated into electric distribution systems (DSs) has become important. However, the output powers' stochastic fluctuations of the photovoltaic (PV) and wind turbine (WT) generation sources, as well as the load demand, result in obvious operational challenges related to the reliability and line loading of these systems. Therefore, this present study is intended to enhance the load-oriented reliability indices (LORIs), the customer-oriented reliability indices (CORIs), and the line loading index (LLI) of the DSs by assigning the optimal integration of RESs and compressed air energy storages (CAESs) through the determination of both the optimal placement and capacities of RESs and CAESs simultaneously, as well as the optimal charging and discharging powers of the CAESs and their initial state of charge based on the red-tailed hawk (RTH) optimization technique. The Monte Carlo Simulation (MCS) and the Scenario-based Reduction (SBR) uncertainty methods are implemented to address the uncertainties of RESs and loading. The influences of the linear failure rate of DS's feeders and the installation of protective devices are also demonstrated. The efficiency of the intended methodology is demonstrated on the IEEE 69-bus and IEEE 33-bus DSs associated with voltage-dependent, time-varying mixed loads. The outcomes obtained from the suggested RTH are compared to those of other reported optimization techniques to validate its effectiveness. The results reveal that the optimal integration of RESs along with CAESs in the IEEE 69-bus test DS can improve the energy not supplied (ENS), system average interruption duration index (SAIDI), system average interruption frequency index (SAIFI), average service unavailability index (ASUI), and LLI by 1.09 %, 1.86 %, 1.73 %, 1.89 %, and 64.53 %, respectively.

Security with Wireless Sensor Networks in Smart Grids: A Review

Smart Grids are an area where next-generation technologies, applications, architectures, and approaches are utilized. These grids involve equipping and managing electrical systems with information and communication technologies. Equipping and managing electrical systems with information and communication technologies, developing data-driven solutions, and integrating them with Internet of Things (IoT) applications are among the significant applications of Smart Grids. As dynamic systems, Smart Grids embody symmetrical principles in their utilization of next-generation technologies and approaches. The symmetrical integration of Wireless Sensor Networks (WSNs) and energy harvesting techniques not only enhances the resilience and reliability of Smart Grids but also ensures a balanced and harmonized energy management system. WSNs carry the potential to enhance various aspects of Smart Grids by offering energy efficiency, reliability, and cost-effective solutions. These networks find applications in various domains including power generation, distribution, monitoring, control management, measurement, demand response, pricing, fault detection, and power automation. Smart Grids hold a position among critical infrastructures, and without ensuring their cybersecurity, they can result in national security vulnerabilities, disruption of public order, loss of life, or significant economic damage. Therefore, developing security approaches against cyberattacks in Smart Grids is of paramount importance. This study examines the literature on “Cybersecurity with WSN in Smart Grids,” presenting a systematic review of applications, challenges, and standards. Our goal is to demonstrate how we can enhance cybersecurity in Smart Grids with research collected from various sources. In line with this goal, recommendations for future research in this field are provided, taking into account symmetrical principles.

Routing and charging scheduling for the electric carsharing system with mobile charging vehicles

Electric carsharing systems are expected to be an optional alternative to private vehicles for decreasing the urban traffic congestion and emissions. However, the temporal and spatial imbalance of the charging demand of shared electric vehicles adds to the managerial complexity of electric carsharing systems. This paper integrates mobile charging vehicles into the electric carsharing system to address this imbalance. Mobile charging vehicles can dwell at stations to provide elastic charging capacity, and thereby decrease both the waiting time of shared electric vehicles at busy stations and the investments in fixed charging piles at suburban stations. In this paper, a mixed integer linear programming formulation is proposed based on a time-space network, in which the routes of shared electric vehicles, charging schedules of shared electric vehicles, and routes of mobile charging vehicles are optimized simultaneously. Then, an algorithm based on Lagrangian relaxation is proposed. Specifically, the proposed formulation is decomposed into three independent subproblems. We propose three exact algorithms for these subproblems, and a tailored multistep repair algorithm is designed to generate feasible solutions. A case study in Hefei, China demonstrates the performance of the proposed algorithm and the effects of the number of SEVs, the number of MCVs, the number of fixed charging piles, trip component, battery capacity, and revenue on the operation of the electric carsharing system.

Frequency domain solution method for electromagnetic influence analysis on torsional vibrations

In electric motor-driven machines, mechanical torsional dynamics are nonlinearly coupled with the electrical system through the electromagnetic torque and the counter-electromotive force. In this paper, an approach based on small-signal linearization is proposed for modeling the steady-state torsional dynamics of the coupled system. Using the linearized model, resonance interference diagrams, torsional response, and stability can be evaluated rapidly across numerous operating points, while accurately accounting for the electromagnetic effects. The model is validated with conventional time-stepping simulations of two induction machines. An example analysis of a 3 MW motor-driven compressor train displays the modification of torsional properties due to the electromagnetic coupling effect and explains the mechanism of torsional destabilization. Finally, previously published measurements of a 37 kW motor test bench are considered to validate the instability of the elastic torsional mode predicted by the model.

Direct Torque Control of a Dual Star Induction Generator Based on a Modified Space Vector PWM Under Fault Conditions

Recent studies have shown that electrical systems in wind power conversion are subject to a 67% failure rate, and conventional three-phase generators are considered to be the most sensitive to these faults. Induction generator current harmonics are a major source of torque ripples causing acoustic noise and vibrations. These ripples can progressively increase under faulty operating conditions. In six-phase machines, there is appearance of high harmonic currents in the air gap as space harmonics. Power electronic converters are also subject to faults, the most common being Short-Circuit (SC) and Open-Circuit (OC) faults. SC faults cause large peaks in the line currents, which trigger the protection devices, resulting in a complete shutdown of the production system. OC faults, on the other hand, cause imbalances in the phases, but the system remains in operation.Direct Torque Control (DTC) based on Space Vector Modulation (SVM) at constant switching frequency is an effective solution to tackle induction generator current harmonics. This paper proposes a DTC combined with a Proportional Integral Fuzzy Logic Controller (PIFLC) optimized by Particle Swarm Optimization (PSO) in order to overcome the problems of torque ripples. Furthermore, using Vector Space Decomposition (VSD) and the Modified Space Vector Pulse Width Modulation (MSVPWM) strategy, a significant reduction of harmonics in the air-gap can be achieved.Simulations were carried out under MATLAB/Simulink, and the results obtained demonstrate the superiority of the proposed DTC-PIFLC-PSO controller in terms of robustness against wind speed variations and under faulty switch conditions in the power converter of the Dual Star Induction Generator (DSIG) generator. In addition, a comparative study with the classical PI controller is presented to show the effectiveness of the DTC-PIFLC-PSO controller against faults with a significant reduction in the Total Harmonic Distortion (THD) during both faulty and non-faulty conditions.

Fault diagnosis based on incomplete sensor variables with a hierarchical semi-supervised Gaussian mixture classifier

We propose a hierarchical semi-supervised variational semi-Bayesian Gaussian mixture classifier based on the partially incomplete and unlabeled samples for the fault diagnosis of mechanical and electrical systems. These systems are typically complex in structure and are monitored by multiple sensors simultaneously. Some sensor variables in the collected data may be incomplete due to sensor malfunctions or transmission errors. Additionally, labeling the data can be a time-consuming and labor-intensive task, resulting in many unlabeled samples. To address these challenges, the missing sensor variables and the unavailable labels are treated as hidden values and handled within the framework of the Expectation Maximization algorithm. We employ a semi-Bayesian technique with variational inference to estimate the model parameters. Specifically, we introduce prior distribution to the mean and covariance matrix to address the possible singularity of the empirical covariance matrix. The weighting coefficients are left without putting prior distribution so that their values can decay to zero, effectively removing redundant components of the mixture model. The factorized distribution is utilized to approximate the posterior distribution of the model parameters, as well as the missing labels and other latent variables. Numerical and real case studies are carried out to verify the effectiveness of the proposed technique.

Environmental performance of a multi-energy liquid air energy storage (LAES) system in cogeneration asset – A life cycle assessment-based comparison with lithium ion (Li-ion) battery

Increase in energy demand is shaping both developed and developing countries globally. As a result, the endeavour to reduce carbon emissions also encompasses electrical energy storage systems to ensure environmentally friendly power production and distribution. Currently, the scientific community is actively exploring and developing new storage technologies for this purpose. The focus of this work is to compare the eco-friendliness of a relatively novel technology such as liquid air energy storage (LAES) with an established storage solution such as Li-Ion battery (Li-ion). The comparison is carried out through Life Cycle Assessment (LCA) methodology which aims to assess the environmental impacts from each life stage, according to different impact categories. In particular, the study refers to the unitary storage and the delivery of electricity as well as cooling energy, considering all the inefficiencies and limits of each technology. The results show that in the full electric case study Li-ion battery environmentally outperform LAES due to (1) the higher round trip efficiency and (2) the significantly high environmental impact of the diathermic oil utilized by LAES, accounting for 92 % of the manufacture and disposal phase. Conversely, the cogeneration case study highlights that the “flexibility” and “dualism” of LAES, capable to efficiently deliver both electricity and cooling, offset the impact related to the higher electricity consumption during the use phase. Notably in energy mix frameworks with high share of primary energy source from fossil fuels, cogenerative LAES demonstrates superior environmental performance compared to Li-ion battery (i.e. 1302 kgCO2eq/MWhe vs 1140 kgCO2eq/MWhe for Singapore energy mix), attributable to its reduced electricity consumption.

Effect of salinity-clay variation on the transient magnetic field in the Quaternary aquifer, theoretically and practically

This study focuses on understanding the physical foundations of the transient electromagnetic method, such as the transition from a simple basic principle to a more complex principle, starting from the Biot-Savart law until the current concept. It calculates the steady and transient magnetic fields around any electrical wire system. Accordingly, this law will be used to determine the magnetic field around the square loop configuration, which matches the magnetic field arising from the circular loop configuration. The square loop equation was derived and extended to include changes in the half-space, which allows us to understand how the electromagnetic waves travel in the subsurface and the farthest point, that can record the TEM response from the TEM loop, according to the loop size and loop current. Accordingly, the deduced equations were applied to predict the transient magnetic field curves of the Quaternary aquifer in three different water zones along the Nile delta, depending on the values of resistivity and chargeability caused by the variations in salinity and clay contents. The calculated transient magnetic curves were then compared with other measured field curves to confirm the validity of the derived equations. Therefore, these curves are expected to be used as guide curves for the transient magnetic field response in this aquifer. Also, these curves are important in estimating the hydro-geophysical characteristics of the main groundwater aquifer in the selected area and in other areas with the same geologic and hydrogeologic settings. Also, a common model is presented for any delta environment in the world in measured and calculated data.

Optimal energy management in smart energy systems: A deep reinforcement learning approach and a digital twin case-study

This research work introduces a novel approach to energy management in Smart Energy Systems (SES) using Deep Reinforcement Learning (DRL) to optimize the management of flexible energy systems in SES, including heating, cooling and electricity storage systems along with District Heating and Cooling Systems (DHCS). The proposed approach is applied on Meridia Smart Energy (MSE), a french demonstration project for SES. The proposed DRL framework, based on actor-critic architecture, is first applied on a Modelica digital twin that we developed for the MSE SES, and is benchmarked against a rule-based approach. The DRL agent learnt an effective strategy for managing thermal and electrical storage systems, resulting in optimized energy costs within the SES. Notably, the acquired strategy achieved annual cost reduction of at least 5% compared to the rule-based benchmark strategy. Moreover, the near-real time decision-making capabilities of the trained DRL agent provides a significant advantage over traditional optimization methods that require time-consuming re-computation at each decision point. By training the DRL agent on a digital twin of the real-world MSE project, rather than hypothetical simulation models, this study lays the foundation for a pioneering application of DRL in the real-world MSE SES, showcasing its potential for practical implementation.

Analysis of hub parameters in fuzzy hypergraphs extending to intuitionistic fuzzy threshold hypergraphs: Applications in designing transport networks in amusement parks using hub hyperpaths

A hypergraph is a generalization of a graph where an edge can connect any number of vertices. In this paper, many different aspects of fuzzy hypergraphs and their applications are examined. The concepts of the hub hyperpath, hub set, and hub number of fuzzy hypergraphs are defined and analyzed. Specifically, the hub number in complete fuzzy hypergraphs has been analyzed and provided an example as an application part in transportation systems. Furthermore, these concepts have been expanded to Intuitionistic Fuzzy Hypergraphs (IFHGs), defining the hub hyperpath, hub set, and hub number. Additionally, the problem of designing an efficient electric transport system for moving between game stations in a large amusement park has been investigated using hub analysis within the framework of an Intuitionistic Fuzzy Threshold Hypergraph (IFTHG) environment.

Empowering energy conservation: A prototype of household energy consumption monitoring based on Internet of Things

Abstract One of the obstacles to control household energy consumption is unavailability electricity instruments/tools to monitor electricity energy consumed by appliances causing a lack of awareness of household energy saving. Energy meter installed on buildings only show electricity consumption in total. This study aims to design and create a prototype system that can monitor and control the electrical appliances usage utilizing the Internet of Things (IoT)-based electronic devices where electricity usage data is stored in Firebase real-time database. The methods carried out are arranged in several stages, starting from data collection, design, configuration, and implementation. The tools used in this research will utilize the ESP8266 as the microcontroller, the PZEM-004T as current sensor and the Blynk as the web server. The tool will send current voltage (Volt (V)) and capacity (Watt (W)) data and then converted to energy and also the costs incurred from electricity usage based on the basic electricity tariff of National Electricity Company (PLN) and the results can be seen directly through IoT (i.e. smartphone, PC, laptop). The accuracy of power measurement by the system is 97.4% compared to measurement used by watt-checker. The results of this study show that an electrical power monitoring system makes people easier to monitor electrical power consumption by appliances used. The pilot measurement shows that a unit system can monitor electricity usage for four units of electronic equipment i.e. water pump, Television, fridge, electrical iron, rice cooker, etc. precisely and succeeded to control and increase energy saving awareness to reduce electricity consumption at the end.

Comparative field assessment of grounding enhancement material for electrical earthing system

Comparative field assessment of grounding enhancement material for electrical earthing system

Mutual Knowledge-Distillation-Based Federated Learning for Short-Term Forecasting in Electric IoT Systems

Mutual Knowledge-Distillation-Based Federated Learning for Short-Term Forecasting in Electric IoT Systems

Optimal battery electric bus system planning considering heterogeneous vehicles, opportunity charging, and battery degradation

Battery electric buses (BEBs) have been seen as a viable alternative for sustainable mobility. The charger placement and fleet configuration play a crucial role in the operational efficiency of BEBs. In this study, a joint optimization model is developed to strategically plan the charger placement and fleet configuration for a BEB system. Distinct from existing research, we simultaneously incorporate opportunity charging, heterogeneous vehicles, and battery degradation to integratively minimize the costs of charger installation, BEB purchase, energy consumption, and battery degradation. Based on a model reformulation, a heuristic-exact hybrid solution method is customized to solve the model. A real-world case study from Oslo, Norway is provided to demonstrate the optimization model and solution method. The results indicate that the proposed model can reduce the total costs by 21.27% in comparison to the conventional model. Sensitivity analyses are further conducted to provide insightful findings and managerial implications for BEB systems.

Reliability analysis of load-sharing system with the common-cause failure based on GO-FLOW method

The load-sharing system (LSS) with the common-cause failure (CCF) is widely used in industrial engineering applications. If a component in this system fails, the total load is shared by the other components, leading to an increased failure rate of the surviving components. The traditional GO-FLOW method is difficult to calculate the reliability of this system accurately. To address this issue, a new reliability analysis approach is proposed in this paper. In this approach, a new GO-FLOW operator is established to simulate the LSS with CCF. Firstly, the state transfer relationship between components in the LSS is identified. Secondly, the α-factor is used to establish the relationship between the independent failure rate λI and the CCF rate λC. Finally, the Markov method is employed to calculate the transient-state and steady-state reliability of the system, and the calculation process for the parallel system and k-out-of-n(F) system are given, respectively. The feasibility of the proposed method is illustrated through a numerical example of a distributed electric propulsion system. This approach extends the applicability of the GO-FLOW method.

An overview on building-integrated photovoltaics: technological solutions, modeling, and control

The advancement of renewable and sustainable energy generation technologies has been driven by environment-related issues, energy independence, and high costs of fossil fuels. Building-integrated photovoltaic systems have been demonstrated to be a viable technology for the generation of renewable power, with the potential to assist buildings in meeting their energy demands. This work reviews the current status of novel PV technologies, including bifacial solar cells and semi-transparent solar cells. This review discusses the various constructions of PV technologies, recent advances in these products, the influence of key design factors on electrical and thermal performance, and their potential in the design of energy-efficient smart buildings. The attention is focused on bifacial and semi-transparent PV systems, given the high level of interest of the scientific community in their current and potential applications.Focus is also devoted to the analysis of the electrical, optical, and thermal modeling procedures developed for sizing, designing, and integrating photovoltaics into larger building simulations. The development of these models has a positive impact on the implementation of next-generation smart buildings. The latest innovative developments and key issues in the application of bifacial PV solutions in buildings are also summarized and analyzed. Special attention is paid to rear side electrical performance, which can be evaluated by means of illuminance/optical backside modeling. Finally, energy management and control of PV-equipped buildings via both model-based and data-driven approaches are discussed, as well as the integration of electric storage systems in a multi-building context.

Two-stage low-carbon economic dispatch of an integrated energy system considering flexible decoupling of electricity and heat on sides of source and load

Under the goal of "double carbon", in order to further enhance the level of new energy consumption and solve the problem of restricting the flexibility of the system by "ordering power by heat" of combined heat and power (CHP) units, a low-carbon economic planning strategy with flexible decoupling of electricity and heat is proposed, by introducing a new type of electric-thermal coupling equipment on both sides of the source and load. Firstly, Consideration of the low-carbon and environmentally friendly characteristics of green ammonia production and ammonia-doped combustion technologies, a wind power(WT) – power to ammonia(P2A) - CHP units - thermal power (TH) units joint operation strategy is proposed on the source side. This strategy realizes the conversion of abandoned wind to green ammonia to ammonia coal hybrid generation, the decoupled operation of CHP units and promotes the consumption of wind power and the low-carbon operation of the system. Secondly, A dynamic incentive demand response model is developed to meet the demand of high proportion distributed PV in situ consumption on the load side. The dynamic incentive price guides the distributed power-to-heat load to change the response capacity, tracks the abandonment of wind and light, realizes the flexible conversion of power and heat load, and cooperates with the source side to promote the coupling operation of electric pyrolysis. On this basis, consider the flexible decoupling capability of electric-heat coupling equipment on both sides of the source and load to establish a two-phase low-carbon scheduling model for the day-before and day-after phases. In the day-ahead phase, the source-side electric-thermal-ammonia joint operation strategy is considered, and the electric and thermal energy supply plans are adjusted centrally; In the intra-day phase, the flexible adjustment range of power-to-heat devices and the heat load inertia on the load side are taken into account, and the electricity and heat planning strategies are adjusted in a distributed-centralised manner in conjunction with the source side. Finally, through the simulation of different cases, the results show that compared with the traditional electric heating system, the total cost of the system considering the scheduling strategy proposed in this paper decreases by ￥826,900, the carbon emission decreases by 1.2t, and basically realises the consumption of wind power and distributed photovoltaic power output. The proposed scheme reduces carbon emissions, promotes the consumption of wind power and distributed photovoltaic output, and is able to reach the goal of low-carbon economic dispatch.

A planning framework for intra-city electric bus road transportation systems

Gasoline-based vehicle transportation plays a crucial role in environmental pollution, which eventually leads to serious health issues in urban areas. The scope of large-scale deployment of personalized electric vehicles in the near future can lean toward such issues but can overburden local distribution grids (LDGs), and the entities are producing green energy. In this context, an electric bus road transportation system (EBRTS) can be seen as an effective and promising way, although it is capital-intensive. EBRTs can be successfully deployed by developing a well-tailored planning model that encompasses amicable solutions while considering the conflicting interests of electric utilities and investors/transporters.This paper proposes a planning model for intra-city EBRTS, especially for metropolitan cities, to extract tangible benefits in favour of electric utilities and private players. The proposed framework envisages a bus fleet charging model and financial model within the spatial, technical, and financial constraints yet preserves the interests of electric utilities and investors by providing tentative solutions with all information necessary from the investor’s perspective. The methodology is free from optimization as the realistic constraints inherently employ search space reduction. The simulation results on a real public transportation system reveal the proposed methodology’s simplicity, rationality, applicability, and flexibility.

MAXIMIZING SELF-CONSUMPTION FROM PHOTOVOLTAICS

The present study refers to the maximization of self-consumption through the automation of a residential consumer that is or not fed from the low-voltage electrical network and from its own photovoltaic system. The level of self-consumption from photovoltaic sources in the residential sector is strictly dependent on the power installed in the own sources, as well as the load profile of the respective consumer. Two major directions are relevant regarding the possibility of increasing and optimizing self-consumption: the management of electrical energy storage systems (especially through the use of battery systems), respectively the implementation of load management systems, through the partial or total redistribution of their operation over time, but also through prioritization of critical consumers.

Automated monitoring of insulation by ultraviolet imaging employing deep learning

The corona effect on the surface of electrical system equipment and components generally indicates undesirable phenomena that can lead to physical degradation of materials or even equipment failure. One of the most promising techniques for monitoring corona discharges is the use of specialized cameras for the detection of ultraviolet radiation. This paper introduces an innovative algorithm for classifying the criticality of insulation based on attributes extracted from videos recorded using an ultraviolet detection camera. The attributes extracted from each facula origin include maximum persistence, area, and the minimum distance between the facula origin and the insulation. To obtain this distance, a technique combining a deep convolutional neural network model with an adaptive segmentation thresholding method is proposed. To validate the proposed methodology, inspections were conducted at a 500 kV substation. A total of 96 videos were recorded, within which 99 facula origins were identified. The object detection model applied demonstrated an accuracy of 85.5 % in detecting insulation in images, based on a validation set comprising 1,985 images and 8,730 instances. The results of the classification showed that 72.7 % of the facula origins recorded originated from regions far from the insulation (mainly cables and corona rings). These results demonstrate that the distance between the insulation and the facula origin is an essential attribute for video analysis, providing context for recorded discharges and allowing differentiation between cases where ultraviolet radiation originates from insulation and those where discharge location is less critical.