• Energy Research
• Closed Access close
• Open Source close
• Embargo close
• IR close

Abstract In this paper, a reintegration-based multi-objective intentional controlled islanding (ICI) model is proposed to enhance resiliency of electrical power systems under catastrophic events. This remedial measure plan relies on a mixed-integer linear programming model with two objective functions including reintegration risk and total load shedding value. While ensuring that each island includes only coherent generators, the proposed multi-objective model solves the controlled islanding problem using lexicographic optimization approach. To ease the islands’ reintegration, charging reactive power, reliability, capacity, and power flow disruption of transmission lines are considered in the model. After implementation of controlled islanding, each resulted island may face temporary active/reactive load-generation imbalance, which may put the islands at the risk of frequency instability, transient voltage instability or a combination of both. The proposed model reduces these risks by modeling energy storage systems (ESSs) and static VAR compensators (SVCs) as fast corrective control actions. In addition to modeling voltage dependent loads in the controlled islanding problem, a linear island frequency response (IFR) model is proposed for frequency stability assessment. The test results of the proposed ICI model on the IEEE 39-bus and IEEE 118-bus test systems demonstrate its performance.

It has been known for a very long time that chemical energy may be converted into electrical energy by using biomass, considered a renewable energy source. In the study that is being presented here, an explanation and a presentation are offered on a one-of-a-kind hybrid system that generates dependable power and cooling by harnessing the chemical energy of biomass. An anaerobic digester takes in organic material and converts it into biomass by using the high-energy content of cow manure as fuel. The Rankin cycle is the primary engine that drives the system that produces energy, and its combustion-based byproducts are routed to an ammonia absorption refrigeration system in order to provide sufficient cooling for the process of pasteurizing and drying the milk. It is expected that solar panels might contribute to the production of sufficient amounts of power for necessary activities. The technical and financial facets of the system are both being investigated at the moment. In addition, the optimal working conditions are determined by employing a forward-thinking multi-objective optimization strategy. This method simultaneously raises the operational effectiveness to the greatest extent that is practically possible while simultaneously lowering both expenses and emissions. The findings indicate that under ideal conditions, the levelized cost of the product (LCOP), efficiency, and emission of the system are, respectively, 0.087 $/kWh, 38.2%, and 0.249 kg/kWh. The digester and the combustion chamber both have very high exergy destruction rates, with the digester having the highest rate and the combustion chamber having the second-highest rate among all of the system's components. This assertion is supported by every one of these components.

Abstract In developed countries, buildings are involved in almost 50% of total energy use and 30% of global green-house gas emissions. Buildings' operational energy is highly dependent on various building physical, operational, and functional characteristics, as well as meteorological and temporal properties. Besides physics-based building energy modeling, machine learning techniques can provide faster and higher accuracy estimates, given buildings' historic energy consumption data. Looking beyond individual building levels, forecasting buildings’ energy performance helps city and community managers have a better understanding of their future energy needs, and plan for satisfying them more efficiently. Focusing on an urban-scale, this study systematically reviews 70 journal articles, published in the field of building energy performance forecasting between 2015 and 2018. The recent literature have been categorized according to five criteria: 1. Learning Method, 2. Building Type, 3. Energy Type, 4. Input Data, and 5. Time-scale. The scarcity of building energy performance forecasting studies in urban-scale versus individual level is considerable. There is no study incorporating building functionality in terms of space functionality share percentages, nor assessing the effects of climate change on urban buildings energy performance using machine learning approaches and future weather scenarios. There is no optimal criteria combination for achieving the most accurate machine learning-based forecast, as there is no universal measure able to provide such global comparison. Accuracy levels are highly correlated with the characteristics of forecasting problems. The goal is to provide a comprehensive status of machine learning applications in urban building energy performance forecasting, during 2015–2018.

Abstract Clamping mechanisms have significant effect on the performance of polymer electrolyte membrane (PEM) fuel cells. In this paper, PEM fuel cell with new clamping mechanism is designed to study the contact pressure distribution over the active area of PEM fuel cell's membrane electrode assembly (MEA). The clamping pressure is pneumatically exerted on the PEM fuel cell assembly. A comparison between the conventional and new clamping mechanism is carried out with simulation, and the numerical results are validated against experimental investigation performed in the fuel cell technology research laboratory. The experimental results are gathered using embedded pressure measurement films in the designed single cell. The results achieved via finite element method are in good agreement with experimental results. It is concluded that the contact pressure distribution of MEA for the new clamping mechanism is more uniform than the conventional one.

A quasi two dimensional (1D + 1D), multi-component model is developed in order to analyze the two-phase transport in polymer electrolyte fuel cell. Different operating parameters, including temperature and wettability are examined and their effects are discussed. The present simple and easy to implement model can be as accurate as a complete two dimensional model. Furthermore, it is seen that the simplification made in this model reduce the computational time.

Abstract Experimental study on the effects of solid volume fraction and temperature on thermal conductivity of DWCNT(inner diameter of 3 nm)-ZnO(diameter of 10-30 nm)/water-ethylene glycol (60:40) nanofluids have been performed using KD2-Pro thermal analyzer in details. The experiments are carried out at solid concentration up to 1% and temperature ranging from 25 to 50 °C. Based on experimental results, using non-linear regression on results of experiments, a correlation as a function of temperature and solid volume fraction has been proposed. Measured data show that the relative thermal conductivity enhances with increasing concentration of nanoparticles. The increasing temperatures also increase the thermal conductivity of nanofluids, although its effect on the thermal conductivity compared to the effect of volume fraction is lower.

CFD predictions of laminar mixed convection of Al2O3–water nanofluids by single-phase and three different two-phase models (volume of fluid, mixture, Eulerian) are compared. The elliptical, coupled, steady-state, three-dimensional governing partial differential equations for laminar mixed convection in a horizontal tube with uniform heat flux are solved numerically using the finite volume approach. It is found that single-phase and two-phase models predict almost identical hydrodynamic fields but very different thermal ones. The predictions by the three two-phase models are essentially the same. For the problem under consideration the two-phase models give closer predictions of the convective heat transfer coefficient to the experimental data than the single-phase model; nevertheless, the two-phase models over-predict the enhancement of the convective heat transfer coefficient resulting from the increase of the alumina volume fraction. The results are calculated for two Reynolds numbers (1050 and 1600) and three nanoparticle volume concentrations (<2%). Although single-phase and two-phase models have been used before to analyze mixed convection of nanofluids, this is the first systematic comparison of their predictions for a laminar mixed convection flow which includes the hydrodynamic characteristics and the effect of temperature dependent properties.