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Abstract Although the sunshine-based models generally have a better performance than temperature-based models for estimating solar radiation, the limited availability of sunshine duration records makes the development of temperature-based methods inevitable. This paper presents a comparative study between Artificial Neural Networks (ANNs), Gene Expression Programming (GEP), Wavelet Regression (WR) and 5 selected temperature-based empirical models for estimating the daily global solar radiation. A new combination of inputs including four readily accessible parameters have been employed: daily mean clearness index (K T ), temperature range (ΔT), theoretical sunshine duration (N) and extraterrestrial radiation (R a ). Ten statistical indicators in a form of GPI (Global Performance Indicator) is used to ascertain the suitability of the models. The performance of selected models across the range of solar radiation values, was depicted by the quantile-quantile (Q-Q) plots. Comparing these plots makes it evident that ANNs can cover a broader range of solar radiation values. The results shown indicate that the performance of ANN model was clearly superior to the other models. The findings also demonstrated that WR model performed well and presented high accuracy in estimations of daily global solar radiation.

Abstract In this paper, we designed, simulated and analyzed high efficiency of SnS-based solar cells. This work is related to the influence of a glancing angle deposition (GLAD) technique for deposition of SnS layer, on the photovoltaic performance of SnS-based solar cells. The photovoltaic parameters have been calculated for the samples prepared at different oblique incident flux angles (α = 0°, 45°, 55°, 65°, 75°, and 85°). The best efficiency was found for the sample prepared at α = 85°. We simulated the J-V characteristics and showed how the absorber layer that prepared by GLAD technique at different incident flux angles, influence the short-circuit current density (Jsc), open-circuit voltage (Voc), fill factor (FF), and efficiency (η) of solar cell.

This paper investigates the problem of a combined study on both electric vehicle (EV) drivers’ behavior in charging station selection (DSS) and station’s behavior in charging service management (CSM) that are considered in the proposed DSS-CSM algorithm. The motivation behind the proposed algorithm is to comprehensively describe a charging market from both drivers’ point of view to reduce range anxiety and enhance customer satisfaction and charge station providers’ point of view to manage charging station properly and improve profitability. The proposed DSS algorithm obtains all the required data to find the best station in terms of the influential parameters given by the candidate stations. A fuzzy multi-criteria decision-making (FMCDM) method that is utilized in the proposed DSS algorithm is deployed for evaluating the parameters and deciding for the best charging station. Moreover, the proposed CSM algorithm of each charging station is utilized to maximize its profitability by taking into account the charge, reserve, and discount prices with respect to the reaction of EV drivers. The Monte Carlo simulations are applied to demonstrate the effectiveness of the proposed algorithm in comparison with a designed benchmark algorithm.

In this chapter, turbulent flow of water-based optimization (TFWO) inspired based on whirlpools created in turbulent flow of water is used to solve the maximum power point tracking (MPPT) of photovoltaic systems in partial shading conditions. The TFWO is used to determine the optimal duty cycle of the DC/DC converter with the objective of maximizing the extracted power of the photovoltaic system. The capability of proposed method is evaluated in different patterns of partial shading to achieve global optimal power. The simulation results showed that TFWO is able to track the global maximum power point (GMPP), successfully in PSC and also fast climate changing. The TFWO has a better tracking capability with faster tracking speed and accuracy than particle swarm optimization (PSO) in obtaining the GMPP. Moreover, the results indicate that the use of buck–boost converter led to faster and more accurate access to the global optimal point than the system equipped with boost converter. The results showed that photovoltaic system with boost converter cannot obtain global maximum power in climate changing condition and limited the efficiency of the MPPT algorithm, while the photovoltaic system with buck–boost converter could be tracked GMPP due to its wider operating area.

Distributed energy resources (DERs) change the supply-demand balance of power systems. To better manage these resources, they are operated as micro-grids (MGs) in both grid-connected and standalone modes. In the presence of MGs, the decision making framework in power systems is changing from the centralized structure into the decentralized one. In such framework, modeling the operation problem of MGs with the participation in energy and reserve markets in the presence of uncertainties is considered as an important challenge. Most of the studies use probability distribution functions (PDFs) to model the uncertain parameters. In this paper, the operation problem of a grid-connected MG is modeled while the MG operator (MGO) faces with uncertainties of renewable energy sources (RESs) without considering their PDFs. For this purpose, an information gap decision theory (IGDT)-based approach is employed to model the uncertain behavior of RESs as well as to control the risk level of the MGO on the optimal scheduling of DERs. To investigate the effectiveness of the model, a modified 15-bus low voltage MG us used as a test system. The results show that optimal decisions of the risk-averse MGO are different from those of a risky one.

Abstract In this study, fluctuation with three frequencies is applied to the boundaries of the cyclone separator. Flow field including dominant frequency and amplitude of vortices at three regions (vortex finder, main cylinder, and cone) and performance parameters (collection efficiency and pressure drop) are studied. Lagrangian-Eulerian Frame is applied, and Large Eddy Simulation (LES) is used for turbulence modeling. Three inlet flows of 4, 6, and 8 [g/s] are considered. The Power Spectral Density (PSD) of fluctuations has been increased by increasing the inlet flow rate. The fluctuating flow will make vortices with higher amplitude than the uniform flow. Because the high amplitude vortex breaks into and transmits energy to the small vortex, pressure drop increases with frequency increment. The pressure drop equations in the non-uniform flow of the cyclone are derived based on the existing pressure drop relationships. The average separation efficiency in non-uniform flow is less than uniform flow.

Abstract In conventional Ancillary Service Markets (ASM), where Independent System Operator (ISO) purchases requirements for system safe and reliable operation, capacity and energy of reserves have always been considered as individual commodities. Market participants offer their capacity bids and energy bids to the ASM, then the ISO decides on purchasing the required capacity using an optimization model based on capacity bids while energy bids are neglected. During the operation time, the ISO has to call some of the purchased capacity to provide required energy for frequency response, and pay them accordingly at their reserve bids. Therefore, the ISO has to pay for both capacity and applied energy while ISO’s model considers capacity costs alone, thus it cannot reach the overall optimum point. To develop ISO’s model for considering energy bids, an Artificial Neural Network (ANN) based method is proposed in this paper to define a combination of energy and capacity bids to be substituted for solo capacity bids in ISO’s model of market. A modified 24-bus IEEE test system is employed to illustrate the proposed methodology.

AbstractIn this study, a mathematical model has been developed to evaluate the performance of industrial‐scale bioethanol production from molasses by Saccharomyces cerevisiae in a series of bioreactors operated in continuous mode. The model was based on three simple metabolic pathways in the yeast including molasses fermentation, molasses oxidation, and ethanol oxidation. The model parameters were estimated using the AQUASIM software by minimizing the error between measurements and calculated results. Results show that the model predictions agree well with the experimental results. The model is a useful tool to predict volumes, levels, discharge flow, bioethanol, and biomass concentrations in each bioreactor during the process. As it is not practical to make changes in steady‐state continuous operation processes, the model can also be used to optimize the process performance and to increase the biomass and ethanol yields. © 2018 Society of Chemical Industry and John Wiley & Sons, Ltd

A new aggregation method is developed that is similar to singular perturbation but can be used for systems that do not have a two time scale property and experience fault disturbances. The optimal modal coherent aggregation method is derived by formulating a parameter optimization problem that attempts to estimate the parameters of a reduced order model of fixed form. The performance index measures the squared error in all internal generator angle differances in the reduced and unreduced model for the study system where disturbances occur. The optimal modal coherent aggregation method is shown to have an identical form for both step and impulse disturbances which is similar to that for the singular perturbation aggregation method.