Optimal scheduling of distributed energy resources in a distribution system based on imperialist competitive algorithm considering reliability worth

A model for energy trading in microgrids (MGs) is proposed in this paper. Imperialist competitive algorithm is used as a powerful method to determine the optimal schedule of all generation units in MG. The optimal scheduling is determined over a planning horizon considering all the constraints of the MG elements and the load demands. Scheduling of distributed generations (DGs) in the MG affects the risk of blackout in the power system. Therefore, a new objective function is presented to investigate the effect of DGs scheduling on the risk of partial or total blackout. DGs generate the great portion of energy in a MG. Generated power by some of DGs is strongly dependent on the weather and ambient conditions. Therefore, the power generation forecast is the major concern for constructing the model for MG energy trading. In this paper, artificial neural network (ANN) is used to predict hourly power outputs of DGs in the MG. Based on the ANN-based forecast module, the imperialist competitive algorithm is developed to determine the MG scheduling, by which the sources can be managed and an optimal operation can be achieved.     

Stochastic models for transfer point location problem

The transfer point location problem has been introduced recently and for the case of minimax objective and planar topology, has only been studied for situations in which demand points are not weighted and have known coordinates. In this paper, we consider the case in which demand points are weighted and their coordinates have bivariate uniform distribution. Also, the problem is developed from a conceptual view and different distance measures are used to make models more applicable in real world situations. The problem is to find the best location for the transfer point such that the maximum expected weighted distance to all demand points through the transfer point is minimized. Depending on assumptions for uniform distributions, two models are considered, convexity conditions are discussed, properties of the optimal solution are obtained and methods to solve the problems are proposed. Finally, numerical examples are given.     

A goal programming-TOPSIS approach to multiple response optimization using the concepts of non-dominated solutions and prediction intervals

Multiple response problems include three stages: data gathering, modeling and optimization. Most approaches to multiple response optimization ignore the effects of the modeling stage; the model is taken as given and subjected to multi-objective optimization. Moreover, these approaches use subjective methods for the trade off between responses to obtain one or more solutions. In contradistinction, in this paper we use the Prediction Intervals (PIs) from the model building stage to trade off between responses in an objective manner. Our new method combines concepts from the goal programming approach with normalization based on negative and positive ideal solutions as well as the use of prediction intervals for obtaining a set of non-dominated and efficient solutions. Then, the non-dominated solutions (alternatives) are ranked by the TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution) approach. Since some suggested settings of the input variables may not be possible in practice or may lead to unstable operating conditions, this ranking can be extremely helpful to Decision Makers (DMs). The consideration of statistical results together with the selection of the preferred solution among the efficient solutions by Multiple Attribute Decision Making (MADM) distinguishes our approach from others in the literature. We also show, through a numerical example, how the solutions of other methods can be obtained by modifying the relevant approach according to the DM’s requirements.     

Amidohydroxylated polyester with biophotoactivity along with retarding alkali hydrolysis through in situ synthesis of Cu/Cu2O nanoparticles using diethanolamine

In this research, polyester fabric was modified through in situ synthesis of Cu/Cu₂O nanoparticles (NPs) in one single step processing using diethanolamine. This introduced amide and hydroxyl active groups on the polyester surface, adjusted pH, aminolyzed, and improved the surface activity of polyester. Copper sulfate was used as precursor, sodium hypophosphite as a reducing agent and polyvinylpyrrolidone as a stabilizer in a chemical reduction route at boil as a facile and cost‐effective approach. The central composite design was also utilized to optimize the processing conditions and study the effect of each variables on the weight gain, color change, and wettability of the treated fabrics. FESEM and mapping, EDX, XRD, and FTIR analysis confirmed effective assembling of Cu/Cu₂O NPs on the amidohydroxylated polyester surface. The optimum treated fabric showed excellent antibacterial properties on both Staphylococcus aureus and Escherichia coli. In addition, a very good photocatalytic activity towards degradation of methylene blue solution obtained after 24 h sunlight irradiation. Further, the hydrophilicity, mechanical properties and stability of the treated fabrics in concentrated sodium hydroxide improved through formation of amidohydroxyl active groups, amidoester cross‐linking and nanocross‐linking within polymeric chains through in situ synthesis of Cu/Cu₂O. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 44856.     

Comparative simulation of a fluidised bed reformer using industrial process simulators

A simulation model is developed by commercial simulators in order to predict the performance of a fluidised bed reformer. As many physical and chemical phenomena take place in the reformer, two sub-models (hydrodynamic and reaction sub-models) are needed. The hydrodynamic sub-model is based on the dynamic two-phase model and the reaction sub-model is derived from the literature. In the overall model, the bed is divided into several sections. In each section, the flow of the gas is considered as plug flow through the bubble phase and perfectly mixed through the emulsion phase. Experimental data from the literature were used to validate the model. Close agreement was found between the model of both ASPEN Plus (ASPEN PLUS 2004 ©) and HYSYS (ASPEN HYSYS 2004 ©) and the experimental data using various sectioning of the reactor ranged from one to four. The experimental conversion lies between one and four sections as expected. The model proposed in this work can be used as a framework in developing the complicated models for non-ideal reactors inside of the process simulators.     

Reflection and transmission coefficient of yttrium iron garnet filled polyvinylidene fluoride composite using rectangular waveguide at microwave frequencies

The sol-gel method was carried out to synthesize nanosized Yttrium Iron Garnet (YIG). The nanomaterials with ferrite structure were heat-treated at different temperatures from 500 to 1000 °C. The phase identification, morphology and functional groups of the prepared samples were characterized by powder X-ray diffraction (PXRD), scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FT-IR), respectively. The YIG ferrite nanopowder was composited with polyvinylidene fluoride (PVDF) by a solution casting method. The magnitudes of reflection and transmission coefficients of PVDF/YIG containing 6, 10 and 13% YIG, respectively, were measured using rectangular waveguide in conjunction with a microwave vector network analyzer (VNA) in X-band frequencies. The results indicate that the presence of YIG in polymer composites causes an increase in reflection coefficient and decrease in transmission coefficient of the polymer.     

Wettability and Its Effects on Oil Recovery in Fractured and Conventional Reservoirs

Abstract

The lattice Boltzmann method is a relatively new simulation technique of computational fluid dynamic class. Its several advantages such as dealing with complex boundary and incorporating of microscopic interaction make it an alternative and promising numerical scheme for simulating fluid flow in porous media. Three lattice Boltzmann equation models are introduced and used for calculating permeability of a 2D porous media. Analytical solutions of Poiseuille flow between infinite parallel plates is used for validating lattice Boltzmann equation models. In the numerical simulations the effects of grid resolution and viscosity on the predicted permeability are checked.     

Interface engineering of high efficiency perovskite solar cells based on ZnO nanorods using atomic layer deposition

Despite the considerably improved efficiency of inorganic-organic metal hybrid perovskite solar cells (PSCs),electron transport is still a challenging issue.In this paper,we report the use of ZnO nanorods prepared by hydrothermal selfassembly as the electron transport layer in perovskite solar cells.The efficiency of the perovskite solar cells is significantly enhanced by passivating the interfacial defects via atomic layer deposition of A12O3 monolayers on the ZnO nanorods.By employing the A12O3 monolayers,the average power conversion efficiency of methylammonium lead iodide PSCs was increased from 10.33％ to 15.06％,and the highest efficiency obtained was 16.08％.We suggest that the passivation of defects using the atomic layer deposition of monolayers might provide a new pathway for the improvement of all types of PSCs..