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Today's societies need a sustainable supply of energy more than ever, whereas unseen events challenge the electric power supply. The coordinated operation of a power‐gas‐transportation system can decrease the effects of such faults in the distribution level. This paper proposes a model predictive control approach for dynamic load restoration in an integrated energy system. The coordination of electric and gas networks besides the mobile energy storage (MES) units provides a novel resilient alternative to serve active and reactive loads. First, the location and time of drastic events are identified as the initialization phase in this paper using a vulnerability analysis by a master–slave problem to find the weak points of the electric grid. Then, a rolling horizon model predictive control‐based approach is proposed to make corrective decisions to serve loads. Meanwhile, a new linearization approach for the Weymouth equation has been proposed based on the binary expansion method. The proposed linear resiliency‐oriented scheduling problem is tested on IEEE 33‐bus and IEEE 69‐bus integrated with a seven‐node gas distribution grid and a six‐station transportation railway. The analysis revealed that the coordination between electric and gas resources minimizes the not‐supplied load; MES units act as certain emergency tools to serve the critical loads. [ABSTRACT FROM AUTHOR]

This paper is aimed at performing a comprehensive review of the approaches employed in nanosatellite platforms, specifically 1 U and 3 U, for electrical power systems (EPS). Moreover, it seeks to develop a robust maximum power point tracking (MPPT) controller using the sliding mode control (SMC) method. The proposed control strategy is intended to monitor the solar panel's maximum power point (MPP) and adapt to changes in temperatures and solar irradiance in low Earth orbit (LEO). The EPS model, designed with the proposed sizing method, is built in MATLAB-Simulink and integrates a solar panel, battery storage, and power converters controlled by diverse MPPT methods. Then, simulation results demonstrate the effectiveness of the proposed SMC approach compared to other conventional control methods for the designed EPS under varying LEO conditions. To achieve a robust validation of the most appropriate MPPT control method under steady-state conditions, this paper presents an experimental investigation into the proposed EPS hardware design. The proposed SMC method achieved an increase in power generation from 10% to 12% for buck and boost power converters, respectively, compared to traditional control methods. [ABSTRACT FROM AUTHOR]

This paper presents the feasibility analysis and study of integrated renewable energy (IRE) using solar photovoltaic (PV) and wind turbine (WT) system in a hypothetical study of rural health clinic in Borno State, Nigeria. Electrical power consumption and metrology data (such as solar radiation and wind speed) were used for designing and analyzing the integrated renewable energy system. The health clinic facility energy consumption is 19 kWh/day with a 3.4 kW peak demand load. The metrological data was collected from National Aeronautics and Space Administration (NASA) website and used to analyze the performance of electrical generation system using HOMER program. The simulation and optimization results show that the optimal integrated renewable energy system configuration consists of 5 kW PV array, BWC Excel‐R 7.5 kW DC wind turbine, 24 unit Surrette 6CS25P battery cycle charging, and a 19 kW AC/DC converter and that the PV power can generate electricity at 9,138 kWh/year while the wind turbine system can generate electricity at 7,490 kWh/year, giving the total electrical generation of the system as 16,628 kWh/year. This would be suitable for deployment of 100% clean energy for uninterruptable power performance in the health clinic. The economics analysis result found that the integrated renewable system has total NPC of 137,139 US Dollar. The results of this research show that, with a low energy health facility, it is possible to meet the entire annual energy demand of a health clinic solely through a stand‐alone integrated renewable PV/wind energy supply. [ABSTRACT FROM AUTHOR]

With the rapid development of China's economy, power demand has been closely linked with economic development in order to analyze and predict the future power situation in China. Based on the historical data of China's electricity consumption, this paper analyzes the data characteristics of China's electricity consumption by using Eviews software. The long-term trend of power consumption sequence is eliminated by fitting the regression curve, and then the residual sequence is analyzed and identified according to the relevant theory of time series. Using the data of China's power demand from 2004 to 2019, the ARMA time series model is used to analyze China's future power situation finally according to the test statistics of the model. Finally, the exponential regression ARMA model is selected to predict China's electricity consumption. The index regression analysis method extracts the long-term characteristic information of the sequence, which is conducive to the stability processing and empirical analysis of long-term prediction. The result predicts China's power demand in 2020 more accurately, which can provide a reference for the future productivity layout of China's power. [ABSTRACT FROM AUTHOR]

Nowadays, power electronic technology is widely affecting people's daily work and life. However, there are still many problems in the current power supply research. When the fault information of power transformer is not complete or there is some ambiguity or even the information is lost, it will largely lead to the conclusion and correct conclusion of fault diagnosis. In this case, the fuzzy theory is applied to the fault diagnosis of shunt capacitor, and the fuzzy fault diagnosis system of shunt capacitor is studied. At the same time, a map-based fault diagnosis system is proposed. In this paper, the cloud computing technology is introduced into the deep learning and compared with SVM and DBN algorithm. The research results of this paper show that the accuracy of fuzzy diagnosis results is 94%, 84%, 90%, 80%, 83%, and 70%, respectively, which shows that the model diagnosis reliability is relatively high. Among the three algorithms, MR-DBN overall detection rate is higher and the time-consuming is lower than the other two methods. The diagnostic accuracy and misjudgment rate of DBN are as follows: 96.33% and 3.90%. The diagnosis accuracy and misjudgment rate of SVM are as follows: 96.40% and 3.83%. The diagnostic accuracy and misjudgment rate of MR-DBN are, respectively, 99.52% and 0.57%. Compared with the other two methods, MR-DBN has the highest diagnostic accuracy and the lowest error rate, which to a large extent indicates that MR-DBN algorithm has higher diagnostic accuracy and has greater advantages and reliability in power supply diagnosis and identification. It not only improves the accuracy of power capacitor fault diagnosis and identification but also provides a new method for the application of power capacitor fault research and development. [ABSTRACT FROM AUTHOR]

At present, multirotor drones are restricted by the control accuracy and cannot position accurately according to the accuracy of point cloud data. Also, track planning in three-dimensional space is much more complicated than that in two-dimensional space, which means that existing track planning methods cannot achieve fast planning. Meanwhile, most existing researches were implemented in quasi-three-dimensional space with the shortest route length as the objective function and omitted environmental impacts. To overcome these, this paper uses the grid method to segment point cloud data of the flying space via ArcGIS software according to the drone's controlling accuracy. It also extracts the grid coordinate information and maps it to a three-dimensional matrix to build the model accurately. This paper sets the minimal energy consumption as the objective function and builds a track planning model based on the drone's performance and natural wind constraints. The improved ant colony optimization and A ∗ (ACO- A ∗ ) algorithm are utilized to design this algorithm for a faster solution. That is, we use the improved ant colony optimization to quickly find a near-optimal track covering all viewpoints with the minimal energy consumption. The improved A ∗ algorithm will be used for local planning for adjacent tracks passing through obstacles. In the designed simulation environment, the simulation results show that, to ensure that the same components are shot, the improved algorithm in this paper can save 62.88% energy compared to that of the Shooting Manual of Drone Inspection Images for Overhead Transmission Lines. Also, it can save 9.33% energy compared to a track with the shortest route length. Besides, the ACO- A ∗ algorithm saves 96.6% time than the A ∗ algorithm. [ABSTRACT FROM AUTHOR]

Summary: Fault diagnosis is vital for detecting the type of fault and rapid restoration of the electric power supply. The existing machine learning‐based classification method for detecting fault shows better performance. But these methods require manual pre‐processing steps and feature engineering, which makes them unreliable and time‐consuming for the complex and multi‐bus system. In this paper, a fully automated approach based on a deep learning framework has been proposed for the multi‐classification of faults in a grid network. The deep learning framework provides a better incentive to predict the type of fault directly using raw data as input. For this intended task of fault diagnosis, a fully convolutional network is designed and implemented to provide better performance with less trainable parameters. The proposed scheme is tested on the large number of simulated data generated from an IEEE 30 bus system. The performance for fault classification is validated through 10‐fold cross‐validation and numerous performance metrics like accuracy, recall, precision, specificity, and F1 score are evaluated. The performance of the different models with parameter variation is compared to obtain better performance with optimal trainable parameters. It has also been compared with some existing methods and shows promising result for fault classification in the large or multi‐bus system. [ABSTRACT FROM AUTHOR]

This paper explores the feasibility of using the strut-braced wing (SBW) configuration to improve the flight endurance of the new energy unmanned aerial vehicle (UAV). A conceptual scheme of a new energy UAV with SBW configuration was designed, and the influence of the SBW on the aerodynamic and structural performance was analyzed. The results show that the SBW can improve the structural performance of the wing and increase the solar laying area of the wing, but the aerodynamic performance did not improve significantly. From the perspective of UAV energy consumption in level flight, the subtraction between electric output power and solar input power is selected as the evaluation index of the increasing effect of the flight endurance. A multidisciplinary design optimization (MDO) model was established considering the coupling of aerodynamics, structure, energy, and weight. Surrogate model technology and multiobjective genetic algorithm are used to optimize the SBW configuration. Compared with the conventional configuration, the optimal design result of the SBW configuration can reduce the level-flight output power and increase the solar input power, thus effectively increasing the flight endurance of the UAV. [ABSTRACT FROM AUTHOR]

The global demand of electrical power has increased enormously due to various reasons such as fast-changing and challenging technologies, climatic change, economic growth, and lifestyle of mankind. Due to global warming issues and the depletion of conventional sources of power generation, the utilization of renewable energy sources has drastically increased. So, many challenges exist in integrating the microgrid with AC grid and load. The alternative arm converter (AAC) is among the most innovative converter topologies used in high voltage direct current (HVDC) applications. This research work presents a new control strategy to generate pulses to trigger the switches in the AAC in a proper sequence to obtain a smooth output waveform. The AAC output is controlled by implementing various controllers such as the proportional-integral-derivative (PID) controller, fractional order PID (FOPID) controller, and FOPID controller tuned by metaheuristic algorithm-bacterial foraging optimization technique (BFOT). Also, a comparative analysis is performed based on the spectral analysis of the output voltage obtained. In comparison to other controllers, the FOPID controller optimized by the bacterial foraging optimization technique (BFOT) produced the least total harmonic distortion (THD) of the AAC output voltage. In addition, this paper also discusses about the performance and analysis on the design of an energy management system (EMS) to optimally utilize the energy sources such as PV system, wind system, and battery based on their availability which feed the AAC. The energy management system controls the entire integrated system in association with an integrated CUK-SEPIC converter to fulfil the load demand at the point of common coupling, either from the microgrid or the AC grid. A nine-level AAC model is designed integrating the microgrid, grid, and industrial loads with an energy management system using MATLAB/Simulink. The performance parameters of the entire model are analysed at every stage to provide stabilized output to meet the load demand. [ABSTRACT FROM AUTHOR]

In this paper, an intelligent integrated approach is proposed to control the reactive power and restore the voltage balance in a three-phase power system using particle swarm optimization (PSO), Gaussian process regression (GPR), and support vector machine (SVM). The PSO algorithm is used in offline mode to determine the optimal set of firing angles for the thyristor-controlled-reactor (TCR) compensator according to the smallest fitness value required for voltage balancing. The optimum firing angles are then used to train the GPR and SVM regression models. The GPR and SVM models are finally used as a real-time controller to retrieve the voltage balance in online mode. A simulation model and experimental setup of the electrical power system are built. The modeled system consists of a 500 km long transmission line. The line is divided into three-pi sections to guarantee a real system response. Several simulation and practical case studies have been conducted to test and validate the capability of the proposed integrated approach in solving the voltage unbalance problem. The results have revealed the supreme ability of the proposed integrated approach to restore the voltage balance quickly (within 20 ms) and for a wide range of voltage unbalance factors (VUFs) (3.90–8.42%). [ABSTRACT FROM AUTHOR]

Summary The power grid constructed with electric power router (EPR) will have the ability of flexibly controlling the voltage and power flow on each line of the grid, which will be helpful to maximize the integration of the renewable distributed generation to the grid. Most of the current methods for transmitting energy depend on storage unit or large scale calculation, which are relatively difficult. To avoid these disadvantages, this paper proposed a two‐step routing strategy based on the improved shortest path method for DC power grid with EPRs. The improved shortest path method combines Shortest Path Faster Algorithm (SPFA) with depth first search (DFS) algorithm, which could handle the negative weights and the negative cycle issue in the graph of the power flow paths. The increase of the transmission line power loss and the EPR's power loss are taken as the weight for the path search method to evaluate the cost of the path. Applying the proposed method to each power demand and supply finally helps to obtain the current distribution of the whole grid. By analysis of typical scenarios, compared with the power flow distribution of traditional DC power grid, the one produced by the proposed strategy could improve the power transmission capability as well as reduce the maximum voltage drop in the network. Besides, this strategy introduces less power loss, than many other ways do, in power routing. The effectiveness of the rioting strategy is related to the configuration of the network. [ABSTRACT FROM AUTHOR]

With the rapid development of the city, shared bicycle travel has also entered a stage of rapid development. In order to ensure the happiness of citizens traveling, in view of the current supply and demand resources and bicycle scheduling problems, a model of supply and demand resource forecasting and multisite and multivehicle scheduling are established. Supply and demand resource prediction uses characteristic variables based on the graph structure itself as input variables of the prediction model XGBoost (extreme gradient boosting). Combined with the actual situation, the improved ant colony algorithm is used to design a reasonable scheduling for the multisite and multivehicle routing problem (VRP). Using Xiamen City shared bicycle data for simulation training, the results show that the shared bicycle scheduling method proposed in this paper has strong robustness and rationality. [ABSTRACT FROM AUTHOR]

Summary: With the commercialization of the electric vehicles, the large‐scale lithium‐ion cells as the power of electric vehicles are to be retired. The second‐use of retired cells is of great significance to improve the battery economy. A fast classification and regroup evaluation method of the retired lithium‐ion cells are proposed in this paper to improve the classification efficiency of retired lithium‐ion cells and adapt to the regroup under different conditions. The lithium‐ion cells after being balanced in parallel are charged in series with a constant current. A support vector regression (SVR) model with the parameters optimized by the particle swarm optimization (PSO) algorithm is built for the fast capacity estimation and the error will not exceed 0.3%. Different cells regrouped means different performance. In order to improve the consistency of retired cells and satisfy different using conditions, a Weighted‐K‐means algorithm is proposed in this paper to regroup the cells with the known capacity and internal resistance. The classification method is evaluated by the voltage consistency of cells using different working conditions, which indicates capacity occupied a large proportion can meet the requirement of energy condition meanwhile keep a good consistency. But the resistance will dominate in algorithm under conditions which have requirement for instantaneous power. [ABSTRACT FROM AUTHOR]

Knowledge graph is a kind of semantic network for information retrieval. How to construct a knowledge graph that can serve the power system based on the behavior data of dispatchers is a hot research topic in the area of electric power artificial intelligence. In this paper, we propose a method to construct the dispatch knowledge graph for the power grid. By leveraging on dispatch data from the power domain, this method first extracts entities and then identifies dispatching behavior relationship patterns. More specifically, the method includes three steps. First, we construct a corpus of power dispatching behaviors by semi-automated labeling. And then, we propose a model, called the BiLSTM-CRF model, to extract entities and identify the dispatching behavior relationship patterns. Finally, we construct a knowledge graph of power dispatching data. The knowledge graph provides an underlying knowledge model for automated power dispatching and related services and helps dispatchers perform better power dispatch knowledge retrieval and other operations during the dispatch process. [ABSTRACT FROM AUTHOR]

The increased electricity demand amidst inadequate electricity generation in South Africa has plunged the country into frequent power outages and load shedding. However, the country still has the lowest electricity transmission and distribution losses in sub-Saharan Africa. Despite low losses, there is still an opportunity to reduce losses further and reduce power outages and load shedding. This study examines the determinants of electricity transmission and distribution losses in South Africa. The results will inform policymakers on avoiding higher electricity transmission losses to alleviate the current electricity shortfall. Using the time-series data from 1971–2020 and the autoregressive distributed-lag (ARDL) bounds testing approach to cointegration, the study confirmed a long-run relationship between electricity transmission, distribution losses, and income, price, investment, political regime, and economic integration. Regression analysis from the ARDL methods revealed that investments, political administration, and economic integration positively influence electricity transmission and distribution losses. At the same time, income reduces electricity transmission and distribution losses in the long run. However, income, price, and economic integration minimize electricity transmission losses in the short run while the remaining variables maintained their positive effects. The implication is that without proper checks in place, an expansion in South Africa's economic integration, investment, and democracy may negatively affect the electricity sector of the country through an increase in electric power losses, while higher income will help the industry via lower electric power losses. The paper, among other things, recommends building a robust economy to ensure lower levels of electricity transmission and distribution losses. [ABSTRACT FROM AUTHOR]

Traditionally, all working modes of a perfect electric conductor are classified into capacitive modes, resonant modes, and inductive modes, and the resonant modes are further classified into internal resonant modes and external resonant modes. In this paper, the capacitive modes are further classified into intrinsically capacitive modes and nonintrinsically capacitive modes; the resonant modes are alternatively classified into intrinsically resonant modes, which are further classified into nonradiative intrinsically resonant modes and radiative intrinsically resonant modes, and nonintrinsically resonant modes; the inductive modes are further classified into intrinsically inductive modes and nonintrinsically inductive modes. Based on the modal expansion corresponding to these new modal classifications, an alternative modal decomposition method is proposed. In addition, it is also proved that all intrinsically resonant modes and all nonradiative intrinsically resonant modes constitute linear spaces, respectively, but other kinds of resonant modes cannot constitute linear spaces; by including the mode 0 into the intrinsically capacitive mode set and the intrinsically inductive mode set, these two modal sets become linear spaces, respectively, but other kinds of capacitive modes and inductive modes cannot constitute linear spaces. [ABSTRACT FROM AUTHOR]

Increasing contingency issues result in inevitable bus voltage violations and force the system to operate around its limit. This situation creates strains that are faced by the security of the Ethiopian Electric Power (EEP) 400 kV system. In this paper, the static security assessment (SSA) of the system under the base and N-1 contingency scenarios was examined by using Newton-Raphson (NR) method. The Bus and line loading-based composite severity indices (CSI) were utilized to determine the state of the system and to get an optimal location for interline power flow controller (IPFC) placement. In this research, the optimal sizing of IPFC was done by using Grey Wolf Optimization (GWO) algorithm. From N-1 contingency analysis, Gelan to Sebeta and Gelan to Holeta branches were found as optimal places for IPFC placement. The IPFC optimal parameters were the series voltage and angle injection found as Vse1 = 0.1101pu, Vse2=0.1096pu, δse1=-0.1848rad, and δse2=2.7682rad with the optimal size of 50 MVA for each IPFC. The average bus voltage severity index (BVSI) without and with IPFC cases was found as 217.243% and 2.641% respectively. The average line severity index (LSI) without and with IPFC cases were found as 39.448% and 15.283% respectively. Moreover, the total line loss under severe N-1 contingency scenarios without and with IPFC integration was 437.783 MW and 63.136 MW respectively. From analysis, it was observed and concluded that the static security of the EEP 400 kV system under N-1 contingency conditions is effectively improved by the integration of IPFC. [ABSTRACT FROM AUTHOR]

This paper is inspired by the question, "if the multi‐phasetransmission system becomes commercially possible in future, then which type of variable speed induction generators will feed electric power to multi‐phase transmission system?" In this paper, a novel six‐phase DFIG through Asymmetrical Winding Structure is developed to extract electric power from the wind energy generation system (WEGS). Due to the spread of pandemic disease (Covid‐19), the attention of power engineers is increasing toward the reliability (in terms of fault tolerance capability) of AC generators. The principle contribution of this manuscript is the modeling (mathematical and simulation‐based modeling) of novel asymmetrical six phase doubly fed induction generator (ASPDFIG). Furthermore, this paper presents the complete modeling of WEGS which contains the modeling of supporting components (6‐phsase transformer, appropriate rotor side converter, grid side converter, dc link etc.), aerodynamic system. In addition to modeling, the analysis, and the controlling strategy for ASPDFIG, wind turbine also discussed. The elegant advantages such as fault tolerance, high efficiency, reduced torque pulsation and reduced per leg converter rating of ASPDFIG are also discussed. The simulation results, particularly the stator voltage, phase current, active and reactive power, are given to validate the theoretic investigation. [ABSTRACT FROM AUTHOR]

An electrical “Grid” is a network that carries electricity from power plants to customer premises. Smart Grid is an assimilation of electrical and communication infrastructure. Smart Grid is characterized by bidirectional flow of electricity and information. Smart Grid is a complex network with hierarchical architecture. Realization of complete Smart Grid architecture necessitates diverse set of communication standards and protocols. Communication network protocols are engineered and established on the basis of layered approach. Each layer is designed to produce an explicit functionality in association with other layers. Layered approach can be modified with cross layer approach for performance enhancement. Complex and heterogeneous architecture of Smart Grid demands a deviation from primitive approach and reworking of an innovative approach. This paper describes a joint or cross layer optimization of Smart Grid home/building area network based on IEEE 802.11 standard using RIVERBED OPNET network design and simulation tool. The network performance can be improved by selecting various parameters pertaining to different layers. Simulation results are obtained for various parameters such as WLAN throughput, delay, media access delay, and retransmission attempts. The graphical results show that various parameters have divergent effects on network performance. For example, frame aggregation decreases overall delay but the network throughput is also reduced. To prevail over this effect, frame aggregation is used in combination with RTS and fragmentation mechanisms. The results show that this combination notably improves network performance. Higher value of buffer size considerably increases throughput but the delay is also greater and thus the choice of optimum value of buffer size is inevitable for network performance optimization. Parameter optimization significantly enhances the performance of a designed network. This paper is expected to serve as a comprehensive analysis and performance enhancement of communication standard suitable for Smart Grid HAN applications. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a comparative study of novel energy integrations between Multiple‐Effect Desalination (MED) and Organic Rankine Cycle (ORC) systems for the simultaneous production of desalinated water and electric power. Two new MED/ORC configurations are proposed, where part of the sensible and/or latent heat of condensation of the ORC is used to potentiate the generation of steam in the last effect of the MED, taking advantage of the temperature levels present in the MED. A simulator of the proposals was developed and validated with experimental results reported in the literature. These configurations are compared energetically with the MED and ORC systems working individually and against another previously proposed configuration, also considering the impact of the integrations on the heat transfer area required by the systems. The results show a decrease in the thermal energy requirements for the activation of the MED system with the new integrations. The energy requirements of the MED are reduced by 28% when integrated into the first effect with the ORC evaporator. The utilization of the sensible heat of condensation of the ORC in configuration 3 did not present a considerable energy decrease compared to configuration 2, due to the limited sensible heat available for its recovery, of approximately 22 kW. The usage of the latent heat of condensation of the ORC in configuration 4 implies that increasing the condensation temperature and pressure of the ORC, decreases its efficiency by 1.8% and increasing the energy required by 221 kW provides the two services simultaneously. [ABSTRACT FROM AUTHOR]

Not only mathematical statistics, differential equations, and mathematical models were used to analyze and reduce data, but a rough set model is also employed in medical, engineering, and other fields to analyze and reduce them. The goal of this paper is to introduce a minimal structure concept to produce new rough set models and show that it is suitable for analyzing most real-life problems, reduction of attributes, and decision making. We examine the effectiveness of the following method in the problem of electric power generators and decision making. We also offer a comparison of our method and Pawlak's method. Finally, the variable precision model improves the accuracy of decision making. [ABSTRACT FROM AUTHOR]

With the improvement of the intelligence level of the power network and the gradual improvement of the information and communication of the power system, the application of power big data to the corporate credit market is not only an important supplement to the national social credit system but also an important strategic goal for the State Grid to expand profitability. This paper constructs a benefit evaluation model for power data credit investigation products based on the utility of the products that financial institutions experience when they purchase and use financial risk control products. Based on the theory of customer value, comprehensively considering the characteristics of power credit reporting products, and taking profit maximization as the corporate goal, the pricing of a single power credit reporting product is studied, and the method is verified using example products. This article gives the pricing strategy of power credit investigation, which can be used as a reference for enterprises to further develop market strategies. [ABSTRACT FROM AUTHOR]

Summary: As the installed capacity of wind power continues to increase, the problem of curtailed wind power is becoming serious in China, especially in the northern region during the winter heating season. To solve the problem of wind‐heat conflict during the heating period in the Three North area, an electric boiler with thermal storage (EBTS) is installed at the end of the grid where wind power is difficult to accommodate and using curtailed wind power to supply heat promotes local accommodation. In this paper, a multi‐objective optimization model of wind power accommodation based on the wind power–EBTS system for heating is established. The goals of maximizing wind power accommodation, minimizing the number of times EBTS must be adjusted, and minimizing operating costs are presented, and a bi‐level optimization scheme is designed. An improved multi‐objective particle swarm optimization algorithm is used to solve these functions, and an optimal compromise solution from the generated Pareto solution set is filtered using the fuzzy membership method. Based on actual data from a demonstration project in China's Jilin Province, the simulation results verify that this method can effectively reduce operating costs and improve wind power accommodation. [ABSTRACT FROM AUTHOR]

Summary: Basic parameters affecting vehicle safety and performance such as pressure, temperature, friction coefficient, and contact‐patch dimensions are measured in intelligent tires via sensors that require electric power for operation and wireless communication to be synchronized to the vehicle monitoring and control system. Piezoelectric energy harvesters (PEHs) can extract a fraction of energy that is wasted as a result of deflection during rolling of tires, and this extracted energy can be used to power up sensors embedded in intelligent tires. A new design of PEH inspired from Cymbal PEHs is introduced, and its performance is evaluated in this paper. Cymbal PEHs are proven to be useful in vibration energy harvesting, and in this paper, for the first time, the modified shape of Cymbal energy harvester is used as strain‐based energy harvester for the tire application. The shape of the harvester is adjusted in a way that it can be safely embedded on the inner surface of tires. In addition to the high performance, ease of manufacturing is another advantage of this new design. A multiphysics model is developed and validated to determine the output voltage, power, and energy of the designed PEH. The modeling results indicated that the maximum output voltage, the maximum electric power, and the accumulated harvested energy are about 3.5 V, 2.8 mW, and 24 mJ/rev, respectively, which are sufficient to power two sensors. In addition, the possibility is shown to supply power to five sensors by increase in piezoelectric material thickness. The effect of rolling tire temperature on the performance of the proposed PEH is also studied. [ABSTRACT FROM AUTHOR]

The electricity in Gaza, Palestine, is limited and scheduled for 4-10 hours per day due to political reasons. This status represents a real problem for different sectors. This paper presents an effective solution especially for the energy supply problem in the residential sector by using an unconventional PV system which operates in stand-alone and grid-connected modes. The system includes a storage battery block with a proper capacity to secure for continuous power supply of a residential house with a daily energy load of 10 kWh. It was found that an unconventional PV system of 3.2 kWp and a storage battery block of 19.2 kWh will be able to cover the total daily energy demands of the house including the outlined electricity cutoff hours. The design of this system and specifics of its components are presented in this paper. The system was simulated by Matlab software, where the daily load curve, grid cutoff hours, and the monthly solar radiation are considered. The obtained simulation results show that the produced PV energy exceeds the load demands during nine months of the year, and thereby, a high battery state of charge (SOC) in the range of 73-84% is achieved. During the three months of the lowest solar radiation (Dec.-Feb.), the produced PV energy is equal to the load demand while the battery state of charge varies in the range of 40-49% which verifies the appropriateness of the proposed PV system. The daily energy yield of the PV system varies between 2.6 and 5.4 kWh/kWp in January and July, respectively, which corresponds to a performance ratio of 90% and 66.25%, respectively. [ABSTRACT FROM AUTHOR]

Smart interference management methods are required to enhance the throughput, coverage, and energy efficiency of a dense small cell network. In this paper, we propose a transmit power control for energy efficient operation of a dense small cell network. We cast the power control problem as a noncooperative game to satisfy the design requirement that small cells do not need any information exchange among them. We analyze the sufficient condition for the existence of a Nash equilibrium (NE) state of the proposed game. We also analyze that the NE state is unique by transforming the original nonlinear fractional programming problem into a nonlinear parametric programming problem. Through simulation studies, we verify our analysis results. In addition, we show that the proposed method achieves higher energy efficiency of a network and balances the energy efficiency among cells more evenly than the methods based on the AIMD (additive increase and multiplicative decrease) algorithm. [ABSTRACT FROM AUTHOR]

Hydrogen fuel cell technology is identified as one option for allowing efficient vehicular propulsion with the least environmental impact on the path to a carbon-free society. Since more than 20 years, IHI is providing charging systems for stationary fuel cell applications and since 2004 for mobile fuel cell applications. The power density of fuel cells substantially increases if the system is pressurized. However, contaminants from fuel cell system components like structural materials, lubricants, adhesives, sealants, and hoses have been shown to affect the performance and durability of fuel cells. Therefore, the charging system that increases the pressure and the power density of the stacks inevitably needs to be oil-free. For this reason, gas bearings are applied to support the rotor of a fuel cell turbocharger. It furthermore comprises a turbine, a compressor, and, on the same shaft, an electric motor. The turbine utilizes the exhaust energy of the stack to support the compressor and hence lower the required electric power of the air supply system. The presented paper provides an overview of the fuel cell turbocharger technology. Detailed performance investigations show that a single-stage compressor with turbine is more efficient compared to a two-stage compressor system with intercooler. The turbine can provide more than 30% of the required compressor power. Hence, it substantially increases the system efficiency. It is also shown that a fixed geometry turbine design is appropriate for most applications. The compressor is of a low specific speed type with a vaneless diffuser. It is optimized for operating conditions of fuel cell systems, which typically require pressure ratios in the range of 3.0. [ABSTRACT FROM AUTHOR]

It is of great significance to accurately provide electric field information for electric power operators and electric power inspection equipment to ensure the safety of live working robots and electric power workers. In this paper, the principle of parallel plate charge induction is used to detect electric field signals and the influence of the inherent capacitance of the parallel plate on the back-end circuit is considered. An equipotential ring is used as the structure of the sensing unit to eliminate the uncertainty generated by the edge electric field. The parameters of the sensor probe are determined by the principle of differential-integration, and its structure is analyzed and designed. The consistency of the designed sensor probe was analyzed, and the results show that the consistency of the sensor is better. We have measured the sensitivity coefficients of the six probes, and the average absolute deviation reached 0.031556. The fits were all above 0.9995. In addition, a three-dimensional power frequency electric field measurement system that is easy to manufacture is designed using the hexahedral structure, which solves the problem of inaccurate electric field measurement caused by the parallel plate probe and the field source being not perpendicular, and the combined field strength formula of nonuniform electric field was obtained. In the laboratory environment, the three-dimensional power frequency electric field measurement system produced in this paper is compared with the electric field simulated by an electric field simulation tool. The test results show that the deviation between the measurement system and simulation is within ±0.55%, the measurement range is 1 kV/m–200 kV/m, the resolution is ≥1 V/m, and the maximum electric field can be measured at 200 kV/m. The nonlinear error is 2.15%, and the sensitivity coefficient is 19.10 mV / kV · m − 1 , which meets the measurement requirements of the power frequency electric field and can be applied to the actual power frequency electric field measurement. [ABSTRACT FROM AUTHOR]

Summary: This paper presents the energy efficiency performances of a proposed Low Voltage Direct Current supply concept and other conventional on‐grid photovoltaic chains architectures. A photovoltaic power source was considered in the studied nano‐grids. The proposed chain architecture allows the photovoltaic generated energy to take a shorter path heading toward the load. The notion of relative saved energy was introduced to compare the studied PV systems energy performances. The obtained results revealed that the use of the low voltage direct current supply concept is very interesting and the use of direct current loads instead of alternative current loads, when a photovoltaic power is generated locally, is more efficient. The installed PV power source in the building should be well sized regarding to the consumed power in order to register a high system RSE. The efficiency of the new low voltage direct current architecture is 10% higher than the conventional one. [ABSTRACT FROM AUTHOR]

Summary: With the recent proliferation and penetration of wind farms into existing power grids, it is paramount to conduct numerous studies to counter grid disturbances based on operational grid codes. One of the ways of generating electric power from wind energy is by employing the promising technology of the permanent magnet synchronous generator (PMSG) wind turbine. In order to solve the transient stability intricacies posed by the stochastic nature of wind energy during grid faults, this paper proposes a fault current limiter for a PMSG‐based wind turbine control. The fault current limiter used in this study is a series dynamic braking resistor (SDBR). The best location to place the SDBR on the machine‐side and grid‐side converters (GSCs) of the wind generator was investigated, considering the grid voltage as its switching signal during transient state. Efforts have been given to find a suitable sizing of the SDBR. The performance of the SDBR has been investigated at various network strengths in weak and strong grids. A severe three‐line‐to‐ground fault and asymmetrical faults were used to test the robustness and rigidity of the controllers of the wind generator. The system performance was evaluated using power system computer aided design and electromagnetic transient including DC (PSCAD/EMTDC) platform. The same conditions of operation were used in investigating the various scenarios considered in this study, for effective comparison. [ABSTRACT FROM AUTHOR]

Summary: Wind power systems have non‐linear dynamics and contain many uncertainties such as the parametric uncertainty and the unknown external disturbances. For these reasons, it is a crucial task to design the robust control systems to assure a robust response of wind systems during uncertainties without deteriorating the supplied power quality or stressing the static power converters. This paper designs an adaptive fractional‐order proportional‐integral (FOPI) control system for a wind power conversion system (WPCS) equipped by a doubly fed induction generator (DFIG) in the electric power grid (EPG) connected mode. The designed adaptive control system combines the robust and intelligent nature of the fuzzy supervisory system (FSS) and simple structure of FOPI controller, where the fuzzy rules are utilized to adjust the FOPI controller parameters based on the error and its rate, resulting in an adaptive fuzzy FOPI (AFFOPI) control system, which can keep the control action touch of the standard FOPI controller and, at the same time, provides the robustness during uncertainties without effect on the power quality supplied to the EPG. Pursuant to the vector control technology of the DFIG, the designed adaptive fuzzy FOPI controllers are applied in both the external loop of power control and in the internal loop of rotor current control, simultaneously. The feasibility of the presented adaptive FOPI control is validated through the numerical results obtained, under different running conditions, using the Matlab/Simulink software. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a simplified harmonic field‐excitation scheme for the brushless operation of wound‐rotor synchronous machines (WRSMs). The proposed scheme involves two inverters that are operating at different frequencies to provide an input current to the armature winding of the machine. One of these two inverters works at a frequency of 60 Hz and provides the regular three‐phase fundamental current, whereas the second inverter works at 180 Hz frequency to supply the single‐phase third‐harmonic to the common point of the start‐connected stator winding of the machine. The proposed method has the advantage that a conventional single‐phase inverter can be used for generating the third‐harmonic current which only needs to be operated at three times the frequency of the first inverter supplying the three‐phase fundamental current. The fundamental three‐phase current generates the main armature field, and the single‐phase third‐harmonic current produces a pulsating magnetomotive force, that induces electromotive force (EMF) in the harmonic winding of the rotor. The induced EMF is rectified to supply field current to the rotor and generates the rotor field. This field, when interacts with the main armature field, produces torque. The proposed brushless field‐excitation scheme for WRSMs is implemented in JMAG‐Designer version 19.1 using 2‐D finite element analysis to realize its operation and analyze its performance. [ABSTRACT FROM AUTHOR]

This paper focuses on the fault-tolerant control (FTC) problem for an electric power steering (EPS) system subjected to stochastic sensor failures, and a novel fault-tolerant controller is proposed based on the genetic algorithm (GA). A mathematical model of the EPS system with sensor failures is first established, and the state feedback control law is solved by using linear quadratic regulator techniques to stabilize the closed-loop control system. Then, the dynamic response errors of the EPS system with and without sensor faults are chosen as the optimization objective function. Furthermore, the appropriate weighting matrices are evaluated to obtain the optimal fault control law by using GA. Finally, simulation results are presented to illustrate the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Summary: Solar radiation can be converted into electrical energy and generate electric power that can be utilized in multiple ways. The technological improvements have provided enormous solutions to the mankind for utilizing the solar energy although photovoltaic's (PV) by consuming sunlight. Photovoltaic is popularly known by the process of converting light to electricity. The current estimated growth by producing global power around 368 GW in 2017 and projecting 3000 to 10 000 GW by 2030. Looking at all the available solar cells, it has been observed that the dye‐sensitized solar cell (DSSC) when compared to mono‐Si or poly‐Si has been effective in its performance and also reduces production cost to a great extent. The power conversion efficiency (PCE) of DSSC has reached to a better extent and been discussed in the paper. There are other mechanisms through which the efficiency can be improved like applying the antireflection coating. Reflection is a usual phenomenon that happens when light incident from one medium to another varies in refractive index. This reflection is one of the important reasons for the loss of power in the PV Cell. So to improve the PCE, the Mono‐Si or DSSC PV Cells can be applied with a thin film antireflection coating by the nanocomposite film consisting of single‐ or multi‐wall carbon nanotubes with TiO2 and other efficient nanoparticles. This paper discusses on different kinds of nanocomposite materials, and their functionalities has been clearly given. Remarkable improvements have been recorded in the last 1 year by applying the antireflection coating; the PCE has further been increased enormously when compared to the uncoated solar cell for both DSSC and Mono‐Si PV cells. [ABSTRACT FROM AUTHOR]

For electric power steering system (EPS), road interference, noise of the sensor, and the uncertainty of the steering system may make EPS control effect and the driver's road sense worse. EPS system which takes advantage of good current tracking ability, good anti-interference ability, and good operation stability is becoming more and more important in automotive research. The traditional H∞ control algorithm can solve the system uncertainty theoretically, but it cannot solve the contradiction between robustness and performance without considering the performance of the system. Therefore, this paper proposes a EPS current tracking method based on the hybrid sensitivity H∞ control algorithm, which takes the current tracking performance as one of the control objectives, so that the system can maximize the robustness and performance. Firstly, the dynamic model of EPS is established. Then, the two-degree-of-freedom vehicle model and tire model are introduced. The state space equation of the system is constructed on the basis of the system state space with random disturbance signals, the hybrid sensitivity H∞ controller is designed in the sensitivity index design, and the proposed algorithm can use weighting function to minimize the performance of the current tracking error as well as the robustness of the yaw rate error in response to robustness. Simulation analysis and experimental verification of EPS system are also carried out. The results show that the control method of the hybrid sensitivity H∞ can better achieve EPS target current tracking, effectively suppress the effect of external interference and noise, improve the system performance and robustness, ensure the driver get good road sense, and improve the system of steering stability. [ABSTRACT FROM AUTHOR]

Summary The third generation wide bandgap (WBG) semiconductor materials exhibit a prominent role in various applications such as adapters, uninterrupted power supplies, smart grids, and electric vehicles (EVs). They have the phenomenal properties such as high critical breakdown field, WBG, and high‐saturated drift velocity as compared to the silicon (Si). This article throws a light on the classification and recent advancements of the GaN‐based power devices along with their structural features. Moreover, it explores the critical issues that degrade the device performance and also various methods to improve their performance. The recently developed commercial GaN devices are enumerated with their figure of merits (FOMs) comparison with the Si and SiC devices. The outrageous features of GaN devices are well utilized for realizing the high power density and high efficiency power converters in case of EV applications. The updated survey of various GaN devices‐based ac‐dc, dc‐dc, and dc‐ac converters is presented along with their salient features. This article also emphasizes the approaches for resolving the power module issues such as parasitics, layout, and thermal design other than the power converters. This review is ultimately helpful for the design engineers to abridge the technical gaps arising due to the power electronics barriers. [ABSTRACT FROM AUTHOR]

In current large-scale supply chain networks, unexpected disruptions degrade the supply availability and network connectivity for modern enterprises. How to improve the robustness of supply chain networks is very important for modern enterprises. In this paper, we explore how to improve the robustness of supply chain networks from a topological perspective. Firstly, through the empirical data-driven study, we show that the directed betweenness metric is more suitable than the other topological metrics in evaluating the robustness of supply chain networks. Then, we propose a rewiring algorithm based on directed betweenness to improve network robustness under the impact of disruptions. The experimental results in the large-scale supply chain network show that the rewiring algorithm based on directed betweenness effectively improves the network robustness. [ABSTRACT FROM AUTHOR]

Aiming at the high demand for the torque response speed and torque pulsation of the all-terrain vehicle (ATV) Electric Power Steering (EPS) system, this paper proposes to apply the Model Predictive Current Control to the all-terrain vehicle EPS system. A Novel Three-Vector Model Predictive Current Control (N3V-MPCC) is proposed in this paper to reduce the current ripple and reduce the calculation load. Two effective voltage vectors and a zero vector are selected in the control period through only six times of prediction and application of the Sector Vector Selection method. The duration of each voltage is calculated and output to the Voltage Source Inverter (VSI). Simulation and experimental results show that, compared with PID cascade Field Oriented Control (PI-FOC), N3C-MPCC can effectively reduce the ripple current of the d-axis and the q-axis. In the simulated electric power mode, the q-axis current ripple of the N3V-MPCC is reduced by 66.67%. Experimental results show that the current ripple of the motor is reduced by 60%, and the torque pulsation is reduced by 62.5%. Therefore, N3V-MPCC has a faster current response speed and smooth steering torque. [ABSTRACT FROM AUTHOR]

With the trend of low emissions and sustainable development, the demand for hybrid electric vehicles (HEVs) has increased rapidly. By combining a conventional internal combustion engine with one or more electric motors powered by a battery, HEVs have the advantages over traditional vehicles in better fuel economy and lower tailpipe emissions. Nevertheless, the power management strategies (PMSs) for conventional vehicles which mainly focus on the efficiency of internal combustion engine are no longer applicable due to the complex internal structure of HEVs. Hence, a large number of novel strategies appropriate for HEVs have been surveyed, but most of the researches concentrate on discussing the classifications of PMSs and comparing their cons and pros. This paper presents a comprehensive review of power management strategies adopted in HEVs aiming at specific challenges for the first time. The categories of the existing PMSs are presented based on the different algorithms, followed by a brief study of each type including the analysis of its pros and cons. Afterwards, the implementation and optimization of power management strategies aiming at proposed challenges are introduced in detail with the description of their optimization objectives and optimized results. Finally, future directions and open issues of PMSs in HEVs are discussed. [ABSTRACT FROM AUTHOR]

First, using sequence components of the voltages, this paper derives a new voltage sag index, namely, ' S.' Then, with the help of the derived index, ' S,' a new classification of voltage sags and swells is presented. Finally, the voltage sags under the various conditions of unbalanced power systems are considered to conceive the capability of ' S' in the diagnosis of voltage sags and swells. It is shown that the proposed index is capable in (1) joint characterization of voltage sags and swells, (2) identification of faulted phase (s), (3) identification of fault(s), and (4) identification of nature of system grounding, simultaneously. Also, considering the waveform from IEEE database, the accuracy of ' S' has been revealed under noisy conditions. Additionally, the effectiveness and accuracy of ' S' have been evaluated through the comparative analysis of the results, obtained with ' S' and others existing voltage sag indexes, for the same data sets. [ABSTRACT FROM AUTHOR]

In this paper, we study the risk-limiting dispatch (RLD) of power system in the case that the distribution information of random variable is ambiguous. Under an ellipsoidal moment uncertainty of the mean and the covariance matrix, we develop the distributionally robust optimization approach to set up a new RLD model, named robust RLD (RRLD for short). The RRLD considers simultaneously the risk of unsafe operation limit by conditional Value-at-Risk management. We further convert the RRLD model into a solvable convex optimization for a special case that the operation constraint is addressed on the overloading transmission current. The new RRLD model is a development and generalization of the traditionary economic dispatch and the RLD approach. Meanwhile, our research extends the distributionally robust optimization application in portfolio problems to a security economic operation of power system. The IEEE-14 and IEEE-30 bus system are chosen as numerical test systems. Preliminary numerical examples show the validity of the model and its reformulation. [ABSTRACT FROM AUTHOR]

It is well known that predictive direct power control method can be influenced by the presence of modeling errors. Any small variations in model parameters will degrade the performance and stability of the control system. However, this value is difficult to be measured precisely in practical implementation. This paper proposes a predictive direct power control method for three-phase grid-connected converters with online parameter identification technique. Specifically, the proposed method alleviates the parameter mismatch problem by using the online parameter identification technique based on model reference adaptive system estimation theory. The proposed strategy not only identifies the parameter accurately, but also improves dynamic response without any additional sensors. The excellent steady-state and dynamic performance of the proposed method are confirmed through extensive simulations. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The electric power generated from different electricity sources are not used efficiently by end users in the world. This is due to the loss of power supplied in the case of electricity transmission and distribution to residential, commercial, and industrial loads. Even if the loss of power in the power system cannot be avoided 100%, it should be reduced to the minimum optimal value. The loss of power in the radial feeders can be minimized using an optimally allocated photovoltaic (PV) generation system by considering the information of geography, solar irradiance of the site, and space availability, which should not have shadow from large buildings and trees. The PV generation system eliminates the problem of power demand by enhancing the capacity of the power network as well as by reducing the depletion and consumption of fossil fuel resources. To reduce power loss and improve system loading capacity for demand response, the integration and finding the optimal place of photovoltaic generation take high concern from power system operators and technicians. The optimal allocation of PV has been done using the Genetic Algorithm (GA) for optimization of a multiobjective function with different constraints. The main objective of this paper is to minimize the power loss of the radial distribution networks by maintaining the phase voltage of the load in balance and improving the drop in voltage along the phase. So, GA is used to determine the best location and capacity of PV generation that can reduce the loss of power in the system. The IEEE-33 bus system is used to test the proposed method. Generally, using the GA and GIS methods results in a high accuracy for optimal placement of PV generation in the IEEE-33 bus radial feeder and enables to reduce the loss of power during transmission and distribution by maintaining the power quality for consumers. [ABSTRACT FROM AUTHOR]

The power load prediction is significant in a sustainable power system, which is the key to the energy system's economic operation. An accurate prediction of the power load can provide a reliable decision for power system planning. However, it is challenging to predict the power load with a single model, especially for multistep prediction, because the time series load data have multiple periods. This paper presents a deep hybrid model with a serial two‐level decomposition structure. First, the power load data are decomposed into components; then, the gated recurrent unit (GRU) network, with the Bayesian optimization parameters, is used as the subpredictor for each component. Last, the predictions of different components are fused to achieve the final predictions. The power load data of American Electric Power (AEP) were used to verify the proposed predictor. The results showed that the proposed prediction method could effectively improve the accuracy of power load prediction. [ABSTRACT FROM AUTHOR]

The renewable energy plays an increasingly important role in many fields such as lighting, automobile, and electric power. In order to make full use of the renewable energy, various smart Internet of Thing (IoT) devices are deployed. However, in the field of energy management, the two-way mismatch between the demand and the supply of the renewable energy will greatly affect the efficiency of the renewable energy. In addition, the security threat of the energy data and the privacy leakage of the user may hinder the further development of smart IoT devices. Therefore, how to achieve consistency and balance between the demand and the renewable energy supply and how to guarantee the security and privacy of smart IoT devices become the key problems of the energy-efficient smart environment. In this paper, a secure and intelligent energy data management scheme for smart IoT devices is proposed. It is worth noting that, with the help of artificial intelligence (AI) technologies and secure cryptography primitives, the proposed scheme realizes high-efficient and secure energy utilization in a smart environment. Specifically, the proposed scheme aims at improving the efficiency of the energy utilization in the multidimensions of a smart environment. In order to realize the fine-grain energy management of smart IoT devices, strategies of three different dimensions are considered and realized in the proposed scheme. Moreover, technologies in AI are applied and integrated into the energy management scheme. The analysis shows that the proposed scheme can make full use of the renewable energy in smart IoT devices. [ABSTRACT FROM AUTHOR]

Energy is the backbone of the economic development of a country. Ethiopia has bulk energy resource potential, and the effect of energy poverty on well-being, physical health, agriculture automation, education system, industries, and ability to prosper is valuable. A large proportion of the population living in Ethiopia has no electricity access. Among more than 110 million people living in Ethiopia, 46% only use electric energy for day-to-day activities like food preparation and other tasks. In the rural area of the country, where 80% of the population lives, people have no sustainable energy supply. The rural electrification needs special mechanisms to enhance the living standards of the people by opening opportunities for electric power infrastructures. The sustainable development of the country will be achieved when citizens are enabled to live up to the standard of living as humans. This paper investigates energy poverty in Ethiopia and its impact on the living standards of the people, like human resource development, health of individuals, and automation of agriculture, for the purpose of upgrading individual's lifestyle in the country. [ABSTRACT FROM AUTHOR]

The electrical infrastructure around the globe is expanding at a rapid rate for the sake of fulfilling power demands in the domestic, commercial and entertainment industries aiming to boost the living standards. In this regard, renewable energy sources (RES) are globally accepted potential candidates for maintaining inexhaustible, clean, and reliable electricity with a supplementary feature of economic prospect. The efficiency of power distribution at reduced cost to the consumers can be further enhanced by introducing a two-way billing system so-called net-metering which has the potential to overcome issues such as voltage regulation, power blackouts, overstressed grid and need for expensive storage systems thereby making it beneficial for the grid and the end user. This envisioning has encouraged the Government of Pakistan to install net-metering infrastructure at places which accommodate surplus renewable energy reserves. According to the Electric Power Act 1997, the National Electric Power Regulatory Authority (NEPRA) issued the net-metering rules and regulations in September 2015 by the endorsement of Federal Government which allowed the distribution companies in Pakistan to buy surplus electricity units generated by the consumers in order to partly reimburse the units imported from the utility grid. The aim behind this research work is to promote renewable energy utilization through net-metering mechanism in order to achieve maximum power. The export of units from consumer side to utility grid and vice versa can be made through bidirectional energy meter. In this paper, a solar net-metering analysis has been carried out on ETAP software to determine its benefits in a distribution network. Different scenarios have been investigated, and it is concluded that solar net-metering technique has multiple influential benefits, e.g., improvement in voltage regulation, reduction in transmission and distribution losses, increase in power availability, less billing to consumers, and reduction of loading on utility grid. [ABSTRACT FROM AUTHOR]

In the period of transmission maintenance, the phenomenon of power interruption or even progressing to cascading failure occurs commonly just originating from one or some elements out of service. Therefore, reasonable transmission maintenance scheduling is important to ensure the safe and stable operation of power supply system. In this paper, a transmission maintenance scheduling strategy considering potential fault risk balance is proposed. Combined with the theory of value at risk and the character of potential fault, the potential fault risk index is defined to represent the possibility and risk level of the whole system power failure under the condition of the certain element failing. Meanwhile, compared with traditional transmission maintenance scheduling model, the balance index and normalized weight factor shown as the system's risk difference of each maintenance period is considered into the objective function to achieve risk sharing and real operation reliability in the whole schedule. In addition, the random potential fault simulation is applied to solve calculation problem when making the schedule. Numerical results of IEEE-118 bus system show the validity of the methodology proposed. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Recently published International Electrotechnical Committee (IEC) technical reports IEC 61000-3-13 provides detailed methodologies for managing voltage unbalance (VU) in electric power systems. A key aspect in the VU emission allocation process is the estimation of VU propagation in different parts of the network. Although the VU propagation from upstream networks to downstream networks is adequately addressed, the attenuation of VU when propagating from downstream to upstream networks at the same voltage level has not been properly taken into account in the aforementioned methodologies. Furthermore, mains connected three-phase induction motors are known to attenuate VU, and there is no methodology currently available to quantify the attenuation provided by such motors. The main motivation behind the current research is to investigate the VU propagation and attenuation in radial distribution networks and to quantify the attenuation provided by induction motor loads. This paper reports that the VU propagation from downstream to upstream in a radial distribution network with symmetrical distribution lines can be conservatively approximated by the ratio of fault levels of upstream and downstream network. Moreover, an influence coefficient has been introduced as a means of appraising the impact of induction motor loads. The theoretical work presented is verified using unbalanced load flow analysis. The outcomes presented in the paper are useful for the further development of IEC technical report on VU emission allocation. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Triboelectric nanogenerators (TENGs) are advanced energy harvesters that convert mechanical energy in diverse environments into electrical energy via static electrification and electrostatic induction. However, their performance needs to be improved to expand their area of use and become more practical. In this study, we introduced molybdenum disulfide (MoS2) and tungsten disulfide (WS2) as separate additives into polyacrylonitrile (PAN) to produce composite nanofibers via electrospinning, aiming to increase the electrical output of the TENGs. This method increased contact area by narrowing the nanofiber diameters, which is a key factor in enhancing the triboelectric effect. The incorporation of MoS2 or WS2, characterized by high specific surface area, interface polarization, quantum confinement effects, and strong electron acceptance and trapping capabilities, led to a significant increase in the dielectric constant and overall electrical performance of the TENGs. Experimental evaluations, connecting the TENGs to circuits with various resistive loads, determined optimal performance at a load resistance of 4.7 MΩ. In particular, the 5 wt% WS2@PAN & polyvinylbutral (PVB) and 5 wt% MoS2@PAN & PVB TENGs exhibited a remarkable peak power output of 40.5 mW, corresponding to a power density of 25.3 W/m2 and provided open circuit voltage of 1,026 V. The integration of 5 wt% MoS2 or 5 wt% WS2 led to more than a twofold increase in electrical power density compared to pristine PAN. These outcomes demonstrate the significant impact of transition metal dichalcogenides in enhancing the energy conversion efficiency of contact–separation mode TENGs, thereby contributing to the advancement of energy harvesting technology. [ABSTRACT FROM AUTHOR]