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This paper mainly focuses on investigating a stochastic energy management approach to enhance reliability of the networked microgrids within a smart island. In this regard, a networked microgrid is considered in the smart island, which is integrated with a multi-Energy Hub (multi-EH) system aiming to simultaneously support the electrical, thermal, and water demands of the island, isolated from the main grid. The objective function is organized as the cost of operation and reliability of the networked microgrid and is solved using a modified bat algorithm. The uncertainty of stochastic parameters is modelled using the 2-m point estimate method, which has notable advantages in terms of accuracy and simplicity of implementation. Different scenarios are considered both in critical and normal conditions to evaluate the performance of the studied model. Both GAMS and MATLAB software's were employed to study the proposed model. The proposed model improved the reliability of the networked microgrid system and results reveal that the proposed cooperative approach could decrease total operation and investment cost of the networked microgrid by 13%. [ABSTRACT FROM AUTHOR]

In order to prevent wastage of generated power of renewable resources, the energy storage systems can be utilized in the stand‐alone micro grids to store the excess produced power of the renewable generation units. When the generated power of the renewable resources is less than the required load, the energy storage systems can help to compensate all or part of the power shortage. In the current study, a stand‐alone micro grid including wind and tidal turbines, PV systems, batteries and fuel‐based generation units is considered to supply the required load of the micro grid. The generated power of each dispatch‐able generation units is determined in such a way as to minimize the operating cost. In the operating cost of the micro grid, the operating cost of the fuel‐based generation units and the reliability cost associated to the penalty of the curtailed loads are considered. To calculate the reliability cost of the micro grid, a comprehensive reliability evaluation of the micro grid considering the resource‐dependent failure rates for all composed components is performed. To study the effectiveness of the proposed reliability‐based scheduling approach, the numerical results associated to a stand‐alone micro grid containing wind, tidal, PV and fuel‐based generation units connected to the batteries are given. [ABSTRACT FROM AUTHOR]

Various wind turbines have been manufactured for converting wind power into electric energy. They are fixed speed concepts with squirrel cage induction generators, limited variable speed concepts with wound rotor induction generators, variable speed concepts with double fed induction generators, direct-drive concepts with electrically excited synchronous generators and gearbox-free concepts with permanent magnet induction technologies. The composed components and the power curve of these technologies are different and to select an appropriate wind turbine for a wind site, in addition to the economic parameter, reliability criterion must be considered. To address this, a reliability model is developed in this paper that considers both component failure and the unpredictable nature of wind speed for different types of wind turbines. The optimal state number of reliability presentations is determined using XB index calculation and fuzzy c-means clustering method to create multi-state presentations for wind turbines. The proposed approach can be used to determine the most reliable wind turbine for a given wind site by assessing the adequacy of the electric network containing various types of wind turbines. The approach's effectiveness is demonstrated through adequacy assessments of the RBTS and IEEE-RTS, which contain various types of wind turbines. [ABSTRACT FROM AUTHOR]

A run‐of‐the‐river power plant is a renewable energy source used for electricity production. Its power output depends on the varying water flow rate over time, which can impact the reliability of the electric network. Previous research has not studied the effects of water flow rate and temperature variations on the hazard rate of the plant's components. This paper addresses this gap by investigating the impact of these variations on the reliability of electric networks with run‐of‐the‐river power plants. The analysis considers the hazard rates of the plant's components, incorporating the relationship between hazard rate and temperature based on the Arrhenius law. Parameters such as power output, current, power loss, operating temperature, and hazard rate are calculated for different water flow rates and ambient temperatures. Numerical simulations on a test system are conducted to examine the influence of water flow rate and temperature on the reliability indices of the electric network. The results demonstrate that water flow rate and temperature significantly affect the hazard rates of run‐of‐the‐river power plants. This highlights the need to consider these factors in the reliability analysis of electric networks incorporating these renewable resources. [ABSTRACT FROM AUTHOR]

The ocean thermal energy conversion (OTEC) systems, as renewable energy-based power plants, have the potential to play a significant role in meeting future electricity demands due to the vast expanse of the world's oceans. These systems employ the temperature difference between surface ocean waters and deep ocean waters to drive a thermodynamic cycle and produce electricity. The temperature of deep ocean waters, approximately 1000 m below the surface, is approximately 4 °C, while surface ocean temperatures typically range between 20 and 30 °C. The generated power of OTEC systems is dependent on these temperature differences and may vary with changes in surface ocean temperatures. In this study, the main focus is to find the impact of temperature variation on the failure rates of OTEC system components and the generated power output of these plants. The findings indicate that as the demand for the power system increases, its reliability decreases. In order to improve the reliability of the power system, the integration of a new generation unit, such as the close cycle OTEC power plant under investigation, could be necessary. The findings also indicate the importance of considering temperature variation in the evaluation of the reliability of such types of power plants based on renewable energy. [ABSTRACT FROM AUTHOR]

The use of underground XLPE cables in power systems is increasing due to the problems associated with overhead lines. However, insulation failure, primarily caused by partial discharge (PD) phenomenon, remains a major concern. Monitoring the insulation condition of XLPE cables, particularly the magnitude and trend of PDs, is crucial. Detecting and localizing PDs online pose a significant challenge due to the presence of various environmental noises that must be eliminated. In recent years, power systemintroduced noises like impulse noises generated by power electronic devices have become prevalent. Therefore, effective de-noising techniques are required to filter out the noises and interferences from the detected PD signals. This paper proposes the use of wavelet transform, which encompasses both time and frequency domains, to remove various noises from PDs, considering their non-stationary nature. To determine the suitable mother wavelet transform, threshold, and number of decompositions, experimental tests are conducted to investigate the characteristics of PD signals in the cables. Additionally, background noise at the measurement site, which varies in substations, is recorded as reference noise for the wavelet-based noise removal process. Using MATLAB software, reference noises and noisy PD signals are measured in substations, and the de-noising process is executed. The proposed method is evaluated on a sample cable, and the results are discussed. Furthermore, the method is applied to detect PD signals in multiple 20 kV substations in Iran, utilizing high-frequency current transformers connected to shield wires, oscilloscopes with high-frequency bandwidth, and MATLAB software. [ABSTRACT FROM AUTHOR]

Due to the advantages of micro‐grids including power losses and voltage drops reduction, reliability improvement and reduction in transmission network cost, numerous clean micro‐grids including renewable resources such as wind, tidal and solar are developed in different countries of world. The investment cost of the renewable energy‐based generation units especially tidal barrages is high, and to generate the cost‐effective electricity from the renewable resources, the optimization process should be performed. In this paper, optimal planning of a micro‐grid containing energy storage device and new mixture of renewable resources including tidal barrage and photovoltaic system is performed. For this purpose, optimal characteristics of barrage type tidal plant including number of turbines, sluices and hydro‐pumps, turbine tip and hub diameters and sluice width are determined to maximum energy of tidal plant is generated. Then, number of photovoltaic systems and batteries are obtained to supply the required load in minimum cost. To optimize the objective functions, different heuristic approaches including particle swarm, genetic and imperial competitive algorithms (ICAs) are applied, in the paper. To study the effectiveness of the proposed approach, optimal planning of a micro‐grid is performed. It is deduced from the numerical results that the particle swarm method has performed best in determining the optimal solution. [ABSTRACT FROM AUTHOR]

The production and consumption of non-renewable energy resources have disrupted the environment's biodiversity cycle. Global climate change, including worldwide warming, has made human life both now and in the future. The construction industry in the world has a significant share in the demand for energy consumption in these challenges. Therefore, the primary purpose of this paper is to implement standards to save and prevent energy loss to control and limit the demand for energy requested from the power network. Constructing a building with self-sufficient energy production that meets its energy needs by producing clean energy becomes more important. It also sells the excess energy to the grid, known as zero energy buildings. In the present paper, the issue is a constrained optimization problem that aims to minimize the total annual cost, including the initial investment cost for PV and batteries and their maintenance costs, as well as the cost of network exchanges. Among the limitations, the proposed model can mention the restrictions governing the battery, such as the limitations of the battery state of charge (SoC). The problem under optimization is a mixed integers nonlinear programming (MINLP) that will be solved by a particle swarm optimization (PSO) algorithm considering the total cost minimization. [ABSTRACT FROM AUTHOR]

A suitable mixture of renewable resources including wind, tidal and photovoltaic units can be used in the microgrids installed in coastal areas or islands. However, the variation in the renewable resources such as wind speed, tidal current speed and solar radiation is significant that affects the reliability performance of these microgrids. Thus, to accurately evaluate the reliability of the microgrids, the failure rate of composed components affected by the variation in the renewable resources must be considered. To study the impact of variation in the temperature and renewable resources on the failure rate of components, different equations including Arrhenius law, temperature modification factor, fatigue strength, bending and contact stress, limit state function of turbine, thermal loss of semiconductor devices, the temperature rise of transformer and cable, the temperature coefficient of voltage and power of photovoltaic panels are developed. According to the developed equations, the components failure rate, and consequently the failure rate of the microgrid considering the variation in the temperature and renewable resources are determined. Then, by simulation, the impact of different energy sources on the failure rate of the whole system is evaluated and the reliability in a microgrid consisting of offshore renewable energy sources will be studied. [ABSTRACT FROM AUTHOR]

Due to their clean nature, high capacity and accurate predictability, the basin‐based tidal units can be integrated into bulk power systems. The major problem of these units is the high investment cost of barrage construction, and so, among different renewable resources, these units are less developed. For making the tidal barrages cost‐effective, new methods should be implemented to increase their produced power that results in the reduction of electricity price. For this purpose, this study suggests new techniques for optimally operating tidal units equipped with hydro pumps. Here, to increase the produced power and energy of the tidal units, at each time, the number of turbines, sluices and hydro‐pumps is optimally determined to yield the maximum produced energy of the generation unit using the particle swarm optimization method. For satisfying the effectiveness of the suggested approach, the optimal operation of a tidal unit installed on the estuary of Bacanga is done and the impact of optimal dispatching of the turbines, sluices and hydro‐pumps on produced energy of the unit is studied. It is deduced from numerical results that the generated energy of the plant during 24 h is increased by 17% with the implementation of the proposed operation plan. For satisfying the effectiveness of the suggested approach, optimal operating of a basin‐based tidal unit installed on the estuary of Bacanga is done and impact of optimally dispatching of the turbines, sluices and hydro‐pumps on produced energy of tidal unit is studied. [ABSTRACT FROM AUTHOR]

The transportation system contains many fossil fuel-based automobiles equipped with the internal combustion engine that results in the pollution of the environment and greenhouse gas emissions. In recent years, to replace these automobiles with clean choices, electric vehicles are developed. So far, three kinds of electric vehicles including hybrid, plug-in, and full-electric vehicles are introduced. In the hybrid and plug-in electric vehicles, both the internal combustion engine and electric motor are used to move the vehicle. However, in the full-electric vehicle, the movement of the vehicle is done only by the electric motor. Due to the development of the electric vehicles in the transportation system, different aspects of these vehicles such as reliability must be studied. The reliability indices of the electric vehicles are affected by the failure rate of the composed components. Thus, to exactly determine the reliability performance of the electric vehicles, the failure rate of the main composed components affected by different parameters such as speed of the vehicle and temperature is taken into account. In the present paper, to accurately study the reliability of all-electric vehicles, the impact of variation in the temperature and vehicle speed on the failure rate of the composed components including battery, inverter, electric motor, and other static and rotation parts of the full-electric vehicle and consequently the failure rate of the vehicle is investigated. To determine the impact of operating temperature on the failure rate of composed components, the Arrhenius law is proposed. Based on the variation in the vehicle failure rate in terms of the vehicle speed and temperature, the reliability of the electric vehicle at different conditions is determined. It is concluded from numerical results performed in the paper that the failure rate of the understudied full-electric vehicle varies between 3.5 and 6 failures per year when the temperature varies between 0 and 50°C and the vehicle speed varies between 0 and 200 km/h. [ABSTRACT FROM AUTHOR]

As an independent unit, microgrid operator (MGO) should respond to the energy demand of customers with an optimal provision cost. Apart from the ability to guarantee the security of microgrid (MG) operation, the MGO should consider the greenhouse emission effects as well. This paper investigates a novel energy management system for an islanded MG. In the proposed strategy, two efficient methodologies are used to maximize the profit and security of the MG: (a) the optimal operation of electric vehicles (EVs) in vehicle‐to‐grid (V2G) mode and (b) the use of demand response (DR) program. Besides, a hierarchical control structure is proposed to manage frequency and voltage uncertainties in a permissible range. Improving the reliability of the MG is another goal of the proposed strategy, which is pursued by the studied controlling framework. The proposed approach is evaluated through a set of simulation and GAMS tests. Results indicate that the frequency deviation of the MG is damped through the proposed management strategy and place the spotlight on the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

In recent years, renewable resources, especially large‐scale wind farms, are increasingly used to generate clean electricity in modern power systems. The penetration level of these power plants is significant, and due to the variation in the wind speed resulting in the variation in the generated power, different aspects of power systems, especially reliability, may be affected that must be studied. Reliability evaluation of power system considering large‐scale wind farms is dependent on two factors: the failure rates of composed components including turbine, generator, electrical converters, transformer, and cable and the variation in the generated power arising from variation in the wind speed. In this paper, dependency of different component failure rates on the wind speed variation is considered, and using the Monte Carlo simulation, reliability evaluation of power systems containing large‐scale wind farms is performed. A wind farm equipped with the permanent magnet synchronous generator is considered, and the failure rate of composed components considering wind speed variation is determined. Based on the wind speed‐dependent failure rate associated with the components of energy conversion system, the important reliability indices of Roy Billinton test system including loss of load expectation and expected energy not supplied are calculated. [ABSTRACT FROM AUTHOR]

Wind and solar as two renewable energy resources are largely used to generate clean and sustainable energy in the power systems. To integrate these renewable energies in the power system, different aspects of the power system such as reliability and operation are affected that must be investigated. It is due to the variation in the generated power of these resources that are arisen from the variation in the wind speed and solar radiation. To model the uncertainty nature of large-scale wind and photovoltaic farms in the operation studies of the power system, an appropriate multistate reliability model is developed for the wind and photovoltaic farms considering both failure of main components and variation in the wind speed and solar radiation. To determine the optimum number of states in the reliability model of wind and photovoltaic farms, XB index is calculated and based on the fuzzy c-means clustering technique the transition rates among different states are obtained. To calculate the transition rates among different states, the fuzzy numbers are used that results in the accurate and reduced reliability model for wind and photovoltaic farms. To determine the probability of different states in the reliability model of wind and photovoltaic farms in the operation studies, matrix multiplication technique is utilized to determine important indices of the power system such as the unit commitment risk. In this paper, the amount of required spinning reserve of a power system containing wind and solar generation units can be determined based on the reliability criterion. The proposed technique is applied to the two reliability test systems including RBTS and IEEE-RTS and the reliability-based operation indices of these systems are calculated to present the effectiveness of the proposed analytical method. [ABSTRACT FROM AUTHOR]

Summary: In recent years, the penetration level of renewable energy generation units such as wave, wind, photovoltaic, and tidal power plants in the modern power systems has increased. It began in the 1970s with a sudden change in oil price and today, large‐scale power plants based on renewable resources have been put into operation. Due to large potential of wave energies, the share of these power plants is expected to increase. Hence, it is necessary to study the effect of wave energy converters on the different aspects of power systems. For this purpose, the effect of variation in the generated power of wave energy converters arising from variation in the wave height and wave period on the operation studies of power‐system must be investigated. For the first time, the impact of large‐scale wave energy converters on the operation studies of the power system is investigated, and the numerical results of the wave dragon converter as a commercial‐scale wave energy power plant are considered. For this purpose, a multistate reliability model of wave energy converters is developed considering both uncertain nature of the generated power and failures of composed components of the wave power plant. To determine the optimal reliability model of the wave generator, XB‐index is calculated and fuzzy c‐means clustering technique is utilized. Then, the proposed multistate reliability model based on the modified PJM method, which is suitable for short‐term studies, is used to perform the operation studies of the power systems containing large‐scale wave energy converters. Numerical results of the operation studies of RBTS and IEEE‐RTS including wave farms are given in the paper to study the impact of wave energy converters on the reliability‐based operation indices of the power systems. [ABSTRACT FROM AUTHOR]

The generated power of the Sea‐wave slot‐coned generator (SSG), as one of the high capacity wave energy converters, is dependent on the period and height of the waves. The generated power of this energy‐converter changes due to the variation in these parameters. Thus, different aspects of the power systems may be affected that must be investigated by increasing the share of these converters for electricity production in the power‐systems. In this paper reliability assessment of composite power‐systems containing large‐scale SSG is performed. The Xie and Beni (XB) index is calculated for obtaining the optimum number of states in the reliability model of the SSG and fuzzy c‐means clustering technique is proposed for reducing the number of states of the model. The contingency analysis method is proposed for calculating the reliability indices of the composite power‐system incorporating large‐scale SSG. DC load‐flow method, which considers line capacity, is used to perform load‐flow in the composite power‐system including generation and transmission networks. In the proposed technique, a load‐shedding program with the goal of minimizing the value of lost load is solved using of the linear programming method based on the interior‐point‐approach for contingency states leading to the load curtailment. [ABSTRACT FROM AUTHOR]

Summary: Increase in the penetration level of renewable resources results in the development of new approaches to assess the impacts of these variable resources on the power network. Large‐scale barrage‐type tidal generation units can integrate to the power network for reliability improvement. For reliability analysis of a power system containing these plants, a reliability model is developed. This model is considered the components' failure and also the output power variation arisen from the change of the tidal height should be developed. For this purpose, a multi‐state reliability model considering variation of tidal height that affects components failure rate and also the value of generated power is developed. The failure rate of components including transformer, turbine, cable, generator and electrical converter is dependent on the tidal height and turbine velocity. The paper is focused on the developing a thermal model for each component. Based on the temperature rise of the components, the associated variable failure rate is calculated from the Arrhenius law. The multi‐state reliability model of barrage‐type tidal energy conversion system considering variable failure rate of components is utilized to study the generation power systems adequacy based on the analytical and Monte Carlo approaches. This model is also utilized for reliability assessment of composite power system containing barrage‐type tidal power plants. Considering variable failure rate of components results in a more accurate reliability model for tidal power plant. Based on this model, the effects of different tidal patterns on the reliability indices can accurately be studied. [ABSTRACT FROM AUTHOR]

Tidal as renewable energy is increasingly utilised for power generation in many countries. Integration of tidal plants into the power system requires considering the intermittent nature of the generated power caused by variable tide levels. Thus, the reliability studies of power systems including high tidal generation affected by the uncertain nature resulting from the variable water levels are investigated. The reliability model of a tidal power plant on a barrage is proposed. In the proposed model, the failure rates of the composed components (especially the hydro pump and gate) and the effect of tidal height variation on the components failure rate are considered. Also, the reliability model with numerous states resulting from the variability of the generated power through the fuzzy C‐means clustering technique and Xie‐Beni index is reduced to a multi‐state reliability model. The resulting reliability model is utilised for the generation adequacy studies of a power system containing large‐scale tidal power plants. Moreover, different reliability indices are calculated for future generation expansion planning. The proposed technique is applied to the two‐test systems, and sensitivity analysis is performed. Besides, the effects of the hydro pump, maintenance, ageing of the components and peak load are investigated. [ABSTRACT FROM AUTHOR]

The penetration level of renewable and sustainable energy resources such as geothermal, wind, solar energies and ocean in power generation systems has been increasing. One of the important renewable energy resources is tidal power plants which makes use of rising and falling sea water level. Because of the uncertain nature of the generated power arisen from the variation in the water level in flood and ebb situations, integration of large-scale reservoir-based tidal power plants to power systems can involve numerous types of challenges. Thus, large-scale tidal power plants can be effectively used in operation studies of power systems. For this purpose, a probabilistic approach is introduced in this paper based on the modified PJM technique for integrating the large-scale tidal plant into operation studies of the power system. In this regard, a multi-state reliability model considering both the failure rate of composed components and the variation in the tide levels is developed and used in the operation studies. For evaluation of the impact of the tidal power plant on operation studies of a power system, numerical examples are put forward in which important indices such as unit commitment risk, required spinning reserve and peak load carrying capability can be calculated. [ABSTRACT FROM AUTHOR]

Optimal feeder reconfiguration is a precious and valuable strategy that can improve the distribution system from different aspects such as power loss reduction, reliability enhancement, load balance improvement and power quality. Nevertheless, the charging demand of electric vehicles (EVs) can affect the optimal switching greatly. Therefore, this paper introduces a new stochastic framework to solve the optimal feeder reconfiguration in the presence of plug-in hybrid electric vehicles (PHEVs). The high volatile stochastic behavior of PHEVs is modeled in the proposed formulation and is considered in determining the optimal status of remotely controlled switches (RCSs). Also, a stochastic framework is constructed based on point estimate method (PEM) with 2m-scheme whereinmis the number of uncertain parameters to capture the uncertainty effects. In addition, a new optimization algorithm based on teacher learning optimization (TLO) algorithm with a two-stage modification method are proposed to explore the entire search space globally. The objective function to be optimized is the total cost of the network incorporating the cost of supplying loads and PHEVs charging demand, cost of power losses and the cost of switching. The performance of the proposed method is examined on the IEEE standard distribution test system. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) signal detection can be used for insulation monitoring of power system equipment. Online PD detection as a main task of insulation monitoring for different power system equipment is a difficult procedure when environmental noise is taken into account. This paper proposes a new scheme based on the Bhattacharyya distance approach with the main goal of detecting the PD signals in cross-linked polyethylene cables. The proposed method, as a preprocessing tool, can be used for detection of PD signals in a noisy environment. The simplicity and the accuracy of the Bhattacharyya distance approach are the 2 main advantages that grab the attention of the authors in introducing this technique for detection of PD signals in different high-voltage power devices. In order to remove noises between intervals of measurement of PD signals, other simple signal processing techniques should be used as postprocessing tools. Detection of PD signals in a noisy environment, i.e. finding the times of PD occurrences, is the most important and difficult stage in online PD signal monitoring. The Bhattacharyya distance method is a simple approach that can be used for detection of short duration time signals such as PDs. [ABSTRACT FROM AUTHOR]

The appearance of Plug-in Electric Vehicles (PEVs) in the electric grids is providing new opportunities when some new challenges are also created. Technically, PEVs are movable loads that can benefit to both owners and utilities in case of using Vehicle-to-Grid (V2G) technology. Therefore, this article aims to investigate the Distribution Feeder Reconfiguration (DFR) effect to optimally manage PEV performance in a probabilistic framework. The proposed stochastic framework will capture the uncertainties of location of PEVs as well as driving pattern and battery State-of-Charge (SOC). In addition, a new self-adaptive evolutionary swarm algorithm based on Social Spider Optimization (SSO) algorithm is proposed that will search the problem space globally. The simulation results on the IEEE standard test system shows the high performance of the proposed method. [ABSTRACT FROM AUTHOR]

Deployment of renewable energies for the electricity generation is on the rise around the world, among which is the run-of-the-river (ROR) power plant whose output power is variable throughout the year depending on the water flow of the respective river. The inherent uncertainty associated with renewable energy resources calls for new stochastic modeling approaches to measure the impacts of using these energies on the power system performance. This paper develops an analytical reliability model for ROR power plants. The model is based on the state-space analysis and is devised with the intention of being used in adequacy studies of power systems. Failure of related components and the intermittent nature of water flow are concurrently accommodated in the proposed model. The well-known technique of fuzzy c-means clustering is employed to find the optimal states of the resultant multistate reliability model. Two reliability test systems, i.e., Roy Billinton Test System (RBTS) and IEEE Reliability Test System (IEEE-RTS), are examined to demonstrate the effectiveness of the proposed reliability model. The water-flow data of Sheshpir River in Pars province of Iran are used. [ABSTRACT FROM PUBLISHER]

With the current focus on energy and environment, efficient integration of renewable energies, especially wind energy into power systems, is becoming essential. Furthermore, to fully capture wind potentials and to recognize the unique characteristics associated with wind energy in power systems adequacy analysis, a profound inquiry is required. In this way, this paper tries to establish a comprehensive analytical approach for reliability modeling of doubly-fed induction generator (DFIG)-based wind farms. First, the most impressive components of wind turbines are introduced. It then continues with integrating developed state space model of wind turbines and their production uncertainties, thereby allowing a wind farm to be represented as a multistate model in analytical studies. To this end, a well-known clustering method, fuzzy c-means clustering, is employed to find the optimal states. The model priorities are then put under investigation via two various case studies, the RBTS and the IEEE-RTS. A wind farm in the northern region of Iran with its historical data is utilized to validate the main features of the proposed analytical model more efficiently. The adequacy studies at Hierarchical Level one (HLI) of the modified test systems are presented and general conclusions are then drawn. [ABSTRACT FROM PUBLISHER]