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The global trends toward increasing clean and sustainable power generation have brought significant changes and developments in electrical distribution networks. The existing passive networks are being restructured to facilitate renewable distributed generations to ensure high degree of reliability of power supply to customers. A distribution system reliability assessment (DSRA) is crucial to analyze the risk and cost associated with power interruptions and take necessary preventive and corrective actions. This paper presents an approach to DSRA and carries out a comprehensive reliability assessment of an active distribution network (ADN) connected with solar, wind, and tidal energy sources. A multi‐state reliability model is developed, combining the Markov and well‐being frameworks to determine the reliability of a network energized by multiple renewable energy sources (RESs). The load point reliability and worth‐oriented indices, namely SAIDI, SAIFI, CAIDI, AENS, EENS, ECOST and IEAR, are evaluated to analyze the impacts of the three RESs on the reliability of the ADN. The paper also proposes two new metrics to estimate the benefits of adding RESs from the reliability worth and cost perspectives. The analyses have been carried out on the Roy Billinton Test System. The results show that the maximum reliability worth and cost benefits can be extracted from a combined operation of multiple RESs. A combination of solar‐wind‐tidal energy sources, therefore, can be an effective solution for countries having long coastlines to fulfill not only the sustainable energy goals but also to improve the grid's reliability and reduce the monetary losses incurred due to customer interruptions. [ABSTRACT FROM AUTHOR]

Summary: At the present day, the issue of the further development of energy economics, industry, and other areas are quite urgent, taking into account the environmental component and not excluding the economic component of this problem. It is possible to reduce the negative impact on the environment by switching to biofuel. In addition to the environmental benefits, biofuels are beneficial in use, also to the companies themselves, as it is more cost‐efficient. The production and use of energy by the companies involve distributed generation. The purpose of the paper is to analyse the economic prospects of replacing distributed generation with biofuel. Results: within the framework of the study, the concepts of distributed generation, biofuel, its main types, as well as some of the advantages of biofuel are defined. Some economic aspects are considered, mainly in the direction of biofuel‐related benefits. In addition, the paper considers the distributed generation market development and the use of biofuel in Russia in comparison with other countries, the main problems were identified, and the main prospects in this direction were considered. Prospects for further research: due to the subsequent study of biofuel usage, taking into account economic and environmental aspects. The applied value of the material: the paper is of practical value in the framework of generalising data on the distributed generation market and the bioenergy market in the Russian Federation and foreign countries. [ABSTRACT FROM AUTHOR]

Summary: This paper is concerned with the two‐stage hierarchical control of microgrids (MG) that can operate in both grid‐connected and off‐grid modes. The procedure consists in the segmentation of the control scheme in the primary and secondary levels, thus enabling the application of faster and slower dynamics according to the process. A decentralized droop control‐based method is used in the primary level, while a distributed multi‐agent coordination scheme is applied in the secondary one. The multi‐agent system is run on Python Agent DEvelopment (PADE) environment, a FIPA‐compliant agent framework. The key contributions this paper provides are the control strategies of reactive power output of non‐dispatchable sources, dynamic sharing of energy storage systems, and the management of loads of microgrids, all of them based on multi‐agent system. Co‐simulations using PSCADTM and PADE are performed considering several operating scenarios of the MG resources under different load conditions. Simulation results validate the accurate performance of the proposed control strategies. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a novel control scheme for unified power quality conditioner (UPQC) to minimize voltage sag, swell, and total harmonic distortion (THD) in a distribution grid integrated with distributed generation (DG). UPQC is a power electronics module that can provide, both, series and shunt compensation simultaneously in a distribution system. The whole characteristic of UPQC is dependent on the embedded control algorithms, which are used for generating a reference signal. This work focuses on synchrosqueezing wavelet transform (SWT) based identification, detection, and control of power quality (PQ) events. SWT is a time‐frequency analysis method that analyzes multicomponent signals with oscillating modes. In this paper, first, SWT is used for the detection and identification of various kinds of PQ issues in the DG integrated grid. The SWT based identification scheme provides better resolution and increases the readability of time‐frequency representation. This work further explores to control the PQ issues through UPQC's enhanced compensation capabilities which utilize artificial neural network (ANN) with SWT based shunt controller. The effectiveness of the proposed method is validated through MATLAB/ SIMULINK simulations and is seen that ANN with SWT based controller is better performed in comparison to ANN based controller, in terms of accuracy, and also with respect to THD. Performance analysis of UPQC is carried out with ANN based controller as well as ANN with SWT based controller, under different voltage and current disturbances, in time and frequency domain. [ABSTRACT FROM AUTHOR]

Summary: Identifying maximum penetration level (MPL) of Solar Photo‐Voltaic Generation (SPVG) in the distribution system is crucial for system planners and network operators to keep the system parameters within the acceptable limits. This paper provides a systematic and extensive procedure for evaluation of MPL of SPVG and its enhancement methods in distribution systems. More importantly, it illustrates a novel algorithm for affixing the penetration cap of Solar PV. The methodology initially derives a new stability index for locating SPVGs in the system, including both active and reactive power support from SPVGs. The study shows the utility of redefining MPL of SPVGs by considering the reactive power injection through solar inverters also. Deviating from the practice of considering the conventional constraints of voltage rise and maximum loading for MPL estimation, the paper considers loss minimization as the major constraint. The algorithm has potential to identify distributed integration scenario of SPVGs compared to the limited number of placements in the extant algorithms. This paper provides a novel algorithm to evaluate of maximum penetration level (MPL) of solar photo voltaic generation (SPVG) and its enhancement methods in distribution systems. The utility of redefining the MPL of SPVG by considering the reactive power injection through solar inverter is illustrated. The algorithm has distinct advantages over the extant algorithms in identifying a distributed integration scenario. [ABSTRACT FROM AUTHOR]

Summary: Reconfiguration is one of the most important functions in the distribution network's automation system. Reconfiguration is formulated as an optimization problem with a large number of scenarios, which demands high central processing unit time to check all of them. Therefore, it is necessary to utilize a high‐efficiency optimization method. In this paper, the minimization of active power losses, total voltage deviations of buses, and maximization of system loading margin are integrated as three objective functions of the proposed reconfiguration model. Also, to improve the voltage profile, reduce power losses, and increase system loading margin, shunt capacitors (SCs) and distributed generations (DGs) are located. Seasonal daily load curves are applied to better simulate networks' real conditions. This paper uses the nondominated sorting genetic algorithm II, which generates a set of nondominated solutions. This set includes a wide range of solutions with different weighting coefficients. The multicriteria decision‐making (MCDM) algorithm, as a powerful and flexible decision‐making tool, is utilized to select the best solution based on tuning parameters. Also, it is assumed that DGs are wind farms, thus the uncertainty of DGs' power output is taken into the account, and the three‐point estimate method (3PEM) is utilized to reduce the number of subscenarios generated by 3PEM. Finally, the subscenario aggregation method is utilized to extract the value of objective functions in subscenarios. The developed model determines the optimal location of SCs and DGs in conjunction with the optimal network reconfiguration. The proposed method is implemented on the typical 33 and 69 bus radial distribution systems. [ABSTRACT FROM AUTHOR]

Summary: The continuous increase of the distributed energy resources (DERs) penetration levels leads to voltage stability problems in the distribution system. One of the approaches for the mentioned emerging challenge is the proper placement of automatic voltage regulators (AVRs). This paper investigates the optimal placement and sizing of AVRs in a distribution network by presenting a new modification of the teaching‐learning‐based optimization (TLBO) algorithm. The objective functions consist of minimizing the distribution system voltage deviation, energy generation cost, and electrical losses. The modification improves the convergence velocity and accuracy of the TLBO algorithm using the combination of mutation technique and quasi‐opposition‐based‐learning concept. This paper compares the performance of the proposed algorithm with other famous evolutionary algorithms. The test distribution system contains installed DERs that work more efficiently after the placement of AVRs based on the mentioned objective functions by the proposed optimization algorithm. The simulation results display the best optimization algorithms for AVRs placement with a significant level of less than 0.10 (ie, probability‐value). The proposed multiobjective optimization algorithm's considerable merit is the accuracy and convergence velocity in solving this specific optimization problem. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a new concept for district energy (DE) systems using central battery storage and decentralized hybrid renewable systems. The proposed DE consists of five buildings integrated with centralized 300 (V), 6.5 (A.h) batteries, locally distributed generations such as 8.5 (kW) wind turbines and 10 (kW) solar cells rated at 110 (V) and three‐phase loads. The central battery can be connected to each home for exchanging power. To increase the resilience, the proposed control system connects home 1 to the external 380 (V) and 50 (Hz) network when the battery is operating above the limited power (ie, 50 [kW] for this case study). The connection to the external grid is done by assessing and confirming a set of necessary conditions. The proposed integrated control system for DE is designed to achieve the following objectives: maximum power extraction from local wind/solar units by maximum power point tracking, optimal charge‐discharge process for central battery, connecting DE to the upstream network under outages and supplying the loads under all operating conditions. These goals are investigated by implementing six different scenarios of performance. In scenario 1, wind and solar units produce 6.6 (kW) and 24 (kW), respectively. Therefore, to feed the total load of 42.5 (kW), the central battery produces 15 (kW). In scenario 2, the total load in the first step is 35 (kW) and it is 95 (kW) in the second step. The power of solar and wind units in both loading steps is constant. The battery power increases from 6.5 (kW) absorption in the first step to 22 (kW) injections in the second step. In scenario 3, the solar units are switched off and in scenario 4, both solar and wind units are switched off. In such conditions, the battery responds to supply loads of homes. Increasing the energy resilience of DE during grid outages is modeled in scenario 5; where the battery power increases from 21 to 51.7 (kW) to deal with such outages. The nonlinear simulations in MATLAB/SIMULINK software show that the developed control strategy is able to control local renewable energies as well as the central battery while increasing the resilience and harvesting maximum power from decentralized wind/solar units. [ABSTRACT FROM AUTHOR]

Summary: "Electric vehicles (EVs) are one of the most promising technologies to green the transportation systems. However, high penetration of EVs brings heavy electricity demand to the power grid." Due to the intermittent character of renewable energy sources (RESs), it turns out to be very challenging to manage EV charging with other renewable generation and grid load. This paper aims to introduce a dispatch strategy assisted with the optimization concept for enhancing the economy of the microgrid system. The major objective is to minimize the cost of system operation and environmental control when meeting system load requirements. The output constraints related to the distributed power supply, such as power limits, are subjected to optimization. To solve this optimization issue, a new "Fitness Sorted Moth Search algorithm (FS‐MSA)" is introduced. Finally, the proposed work is compared and validated with other existing works with respect to various measures. The enhanced outcomes prove the efficacy of the implemented FS‐MSA model. This work concentrates on EV adoption integrated with RES for sustainable mobility. On observing the result, it can be noticed that the adopted scheme was 81.97%, 82.03%, 82%, and 81.97%, better than existing genetic algorithm, particle swarm optimization (PSO), moth search algorithm, and lagrange multiplier optimization (LMO) models for mean case scenario. [ABSTRACT FROM AUTHOR]

Summary: Increased green energy presence in distribution power networks can resolve rising energy demands while also reducing fossil fuel consumption, as well as providing economic and technical merits. However, the quantity of power delivered to customers cannot be allowed to take precedence over the quality of that electricity. This is because the high penetration of the renewable‐based power generation comes at the cost of power quality (PQ) deterioration, especially at the point of common coupling (PCC), owing to nonlinear characteristics of renewable‐based distributed generation (DG) systems and pre‐contamination of modern distribution grids. So, the function of PQ improvement technologies unavoidably comes into the picture when the hosting capacity (HC) of renewable energy systems is constrained by any of the grid PQ performance indices. This paper investigates the application of a hybrid active power filter (HAPF) to improve the harmonic‐constrained HC (HC‐HC) of renewable DG systems in harmonically polluted distribution systems. A shunt type HAPF designing to achieve maximum HC‐HC is considered as an optimization problem and solved by the firefly algorithm under several constraints the system particularly related to individual and total harmonic distortion limits in line current and voltage at PCC, harmonic currents overloading of distribution feeder. Aside from harmonics, constraints related to voltage rise and drop limits, as well as power factor, are also taken into account to ensure the optimal use of the HAPF as a compensator. The proposed HAPF‐based HC‐HC improvement approach's findings and efficacy are compared to those acquired from one of the literature's recently proposed techniques. Besides, the study is extended for HC‐HC enhancement by HAPF, under changing the utility‐side's background voltage distortion and the load‐side's nonlinearity level conditions. The performed extended investigation of the system shows that HC‐HC enhancing the effectiveness of the proposed approach is rather less susceptible to utility or customer side contribution to background distortion at the PCC. Highlights: Hybrid harmonic filtering is investigated for enhancing the harmonic‐constrained hosting capacity of distributed generation in distorted grid conditions.A hybrid filter has been found as a better alternative to achieve an advanced level of renewable energy penetration in distorted distribution grids.Along with increasing the penetration level of distributed generation the harmonic distortion is also substantially reduced instead of being at the verge of the standard limit.With the increase in background voltage distortion, the percentage enhancement in HC‐HC degrades.High load‐side's nonlinearity also results in a relatively lower enhancement in HC‐HC.Background nonlinearity negatively influences the percentage of HC enhancement more than the load‐side's nonlinearity.Though the utility, as well as customer‐side nonlinearity, affect the performance of the hybrid filter also yet the variation is significantly lesser than the purely passive filtering‐based approach. [ABSTRACT FROM AUTHOR]

Summary: In an active distribution network (ADN), the optimal micro‐PMU (μPMU) placement problem (OμPP) determines the location set with the minimal number of μPMUs that make the system observable. However, most approaches that solve OμPP provide a single location set limiting electrical power monitoring planning flexibility. Therefore, this paper presents an approach based on nonlinear programming (NLP) for solving the OμPP. The objective of the approach is to provide multiple location sets for OμPP that provide flexibility to electrical power monitoring planning. New constraints of the presence of distributed generations (DGs), zero injection buses (ZIBs), and communication line unavailability are taken into account for OμPP formulation. The proposed approach is implemented on the IEEE 69 distribution feeder, Indian‐85 distribution feeder, and IEEE 123 distribution feeder. The results show that the proposed approach successfully provides multiple location sets with a minimal number of μPMUs. [ABSTRACT FROM AUTHOR]

Summary: This paper aims to advance the recently progressing research on solid‐state transformers (SST) and examines the impact of uncertainty on load demand and distributed generation (DG) units while integrating SST into an active distribution network (ADN) in the presence of battery backed solar photovoltaic (BBSPV) and wind generation. The uncertainty related to solar irradiance, wind speeds, and load demands is modeled through kernel density estimation (KDE), generalized extreme value (GEV) distribution, and Gaussian distribution summed with the maximum likelihood estimation (MLE) approach respectively. The K‐medoid clustering procedure is utilized to group the load demand into multiple load levels, which helps in the intelligent scheduling of charging and discharging of battery energy storage systems (BESS). The distribution network planning process manages two conflicting criterion functions of real power loss (RPL) reduction and sum of square deviations of the expected voltage values, which are optimized utilizing the non‐dominated sorting genetic algorithm (NSGA‐II). The probabilistic power flow uses the direct load flow procedure utilizing bus injection to branch current (BIBC) matrix along with the forward sweep method to investigate each run of Monte Carlo simulation (MCS). Multiple case studies have been conducted on the IEEE 33 bus radial distribution network (RDN). The outcomes demonstrate the potential benefits of RPL reduction and voltage profile improvement (VPI) with increasing penetration levels of SST. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a multi‐objective coyote optimization algorithm (MOCOA) is proposed for techno‐economic benefits of simultaneous placement of fault current limiters (FCLs) and distributed generation (DGs) units in distribution grids. The proposed MOCOA is employed for mitigating the loss of power in distribution lines, minimizing the fault current levels, minimizing the economic costs of FCL size, enhancing the total voltage stability index, and reducing the total voltage deviation. The MOCOA is enhanced by emerging the concept of pareto‐optimal dominance to store the distinguished solutions in each generation. Finally, TOPSIS, a technique is implemented to find the compromise solution to fulfill the operator requirements. Applications are carried out in three distribution systems. The simulation results show the great effectiveness of the proposed MOCOA in acquiring several alternatives with appropriate technical improvements in power losses, voltage stability, fault level reduction, and economical FCL size. [ABSTRACT FROM AUTHOR]

Summary: Brazil has one of the richest river basins in the world and an energy matrix based mostly on hydroelectric power. Current national policy is increasingly promoting renewable sources like wind power and solar photovoltaic power. Technologies used in generation energy via hydro, wind, and solar sources can be employed in decentralized generation. This study describes a turbine for hydro energetic use in flow sites with heads up to 2.5 (m). A hydrodynamic study was performed using computational fluid dynamics (CFD) analysis to evaluate the performance of this turbine. For this, a three‐dimensional model of the turbine was considered, consisting of three computational domains, using periodicity to decrease the computational cost. The variation of the angular velocity and the volumetric flow were the boundary conditions. The turbulence model used was the shear stress transport. The draft tube geometry was modified according to the results of the CFD analysis. The efficiency and performance of the turbine improved from 79% to 80% as a result of this modification. A case study was conducted for a comparative financial analysis of the turbine proposed in this paper, with wind power and solar photovoltaic decentralized generation in Brazil. [ABSTRACT FROM AUTHOR]

Summary: The distributed generation (DG) units are used more in the electrical networks to reduce the energy demand along with reducing the system losses and increasing the voltage profile of the load points. This paper presents an unintentional islanding detection, reliability enhancement, and power loss minimization of the restored system after an islanding event. The detection of islanding is performed by passive detection method of simultaneous measurement of voltage and frequency (vf)‐based technique. The reliability of the system is increased along with the power loss minimization through reconfiguration by Particle Swarm Optimization (PSO) technique. The network reconfiguration is performed by identifying the most vulnerable buses, and reliability indices of the system are evaluated after the islanding event. The reliability indices act as a marker for validating the proposed reconfiguration strategy. The proposed method for islanding detection, reliability enhancement, and power loss minimization is compared with the existing methods available in the literature. The proposed method is tested on two different radial distribution system such as IEEE‐69 and 118 bus systems, and the results obtained after the validation are promising. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a two‐stage planning approach for the efficient utilization of distributed generation (DG) and capacitor banks (CBs) for the simultaneous grid‐connected and islanded operations of the balanced distribution networks. The first stage determines the optimal installation locations and capacities of DGs and CBs using an improved variant of the Jaya optimization algorithm (IJaya) to minimize the power losses and voltage deviation during the grid‐connected operation of the distribution network. The second stage identifies the optimal operating point of the DG and CB combination to carry the maximum load during the islanded network operation under power supply‐demand imbalance conditions. For the second stage, an analytical approach is proposed to identify the best operating power factor for the DG‐CB combination, calculating the power loss, and under‐utilization of the installed real‐reactive power sources. Simulation results on the IEEE 33‐bus grid‐dependent distribution network demonstrate that the proposed IJaya significantly improved the network performance and outperforms various existing optimization methods. In addition, during the network's islanded operation, maintaining the network power factor equal to the source power factor (pfsource) makes the DG‐CB combination operate at their rated capacities. [ABSTRACT FROM AUTHOR]

The vigorous development of renewable energy has induced multiple sources connected to regional distribution networks. However, the high impedance ground fault that appeared in the distribution network can induce an intermittent arc accompanied by extinction and reignition, which may damage renewable energy generation devices severely. Therefore, the detection of arcing faults is profoundly necessary for practice, but the high impedance arcing fault is easily confused with switches of motor or capacitor bank. In order to solve high impedance arcing fault detection, this paper proposes a high impedance arcing fault detection method that combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and Teager energy operator (TEO). Firstly, CEEMDAN algorithm is used to extract the intrinsic mode function (IMF) of transient zero‐sequence current (TZSC). According to the correlation coefficient and waveform similarity, which can select maximum correlation coefficient value as the characteristic component of TZSC; Then, TEO is utilized to track the change of signal in real‐time, meanwhile, wavelet singular entropy (WSE) is applied to calculate the characteristic component of TZSC. In particular, high impedance arcing fault (HIAF), motor switching (MS), and capacitor bank switching (CBS) can be effectively identified by comparing WSE. The effectiveness of CEEMDAN‐TEO detection method is verified by simulation, which owns advantages in extraction accuracy, calculation efficiency, and detection accuracy of feature components. [ABSTRACT FROM AUTHOR]