Evgueniy Entchev

Performance comparison of Fick’s, dusty-gas and Stefan–Maxwell models to predict the concentration overpotential of a SOFC anode

Models for mass transport inside a porous SOFC anode were developed based on Fick’s model (FM), the dusty-gas model (DGM) and the Stefan–Maxwell model (SMM) to predict the concentration overpotential. All models were validated with experimental data for H2–H2O–Ar and CO–CO2 systems. The effect of pore size on all model predictions was discussed. It was concluded that the dusty-gas model is the most appropriate model to simulate gas transport phenomena inside a SOFC anode. However, this model requires numerical solution, whereas Fick’s and Stefan–Maxwell’s do not. It was found that the SMM, rather than the FM, is a good approximation of the dusty-gas model for H2–H2O system, except in the case of high current density, low H2 concentration and low porosity, where only the DGM is recommended. For the CO–CO2 system, there is no simple rule for selecting an alternate model to DGM. Depending on the CO concentration, porosity and current density, the FM or the SMM could be used. The only restriction is for small porosities where only the DGM should be used. This paper also demonstrated that only the DGM is recommended for a multicomponent system (H2–H2O–CO–CO2).

Residential fuel cell energy systems performance optimization using soft computing techniques

Abstract Stationary residential and commercial fuel cell cogeneration systems have received increasing attention by the general public due to their great potential to supply both thermal and electrical loads to the dwellings. The reported number of field demonstration trials with grid connected and off-grid applications are under way and valuable and unique data are collected to describe the system’s performance. While the single electricity mode of operation is relatively easy to introduce, it is characterized with relatively low efficiency performance (20–35%). The combined heat and power generation mode is more attractive due to higher efficiency +60%, better resources and fuel utilization, and the advantage of using a compact one box/single fuel approach for supplying all energy needs of the dwellings. While commercial fuel cell cogeneration applications are easy to adopt in combined mode of operation, due to the relatively stable base power/heat load throughout the day, the residential fuel cell cogeneration systems face a different environment with uneven load, usually two peaks in the morning and in the evening and the fact that the triple load: space, water and power occur at almost the same time. In most of the cases, the fuel cell system is not able to satisfy the triple demand and additional back up heater/burner is used. The developed ‘’soft computing” control strategy for FC integrated systems would be able to optimize the combined system operation while satisfying combination of demands. The simulation results showed that by employing a generic fuzzy logic control strategy the management of the power supply and thermal loads could be done appropriately in an optimal way, satisfying homeowners’ power and comfort needs.

Investigation of a hybrid renewable–microgeneration energy system for power and thermal generation with reduced emissions

A conceptual study is described into the hybridization of Stirling engine-based residential cogeneration systems with solar thermal systems. Simulation results of four hybrid system configurations applied in various locations in Canada are presented and compared to Base Case systems without solar input. Additional optimization cases are discussed. Adding solar collectors to a residential cogeneration system has a clear potential to reduce natural gas consumption and greenhouse gas emissions. The simulated cases showed a 10%–15% decrease in the consumption of natural gas, which corresponds to a greenhouse gas emission reduction of approximately 700–1200 kg/house/year (depending on configuration and location). Hybrid systems are complex and highly integrated systems. A full system optimization was therefore not possible in this study. Recommendations are given for further optimization of this type of systems.

Prediction of the performance of a solar thermal energy system using adaptive neuro-fuzzy inference system

This study investigates the applicability of adaptive neuro-fuzzy inference system (ANFIS) approach for predicting the performance parameters of a solar thermal energy system (STES). Experiments were conducted on the STES during the summer season and for different Canadian weather conditions in Ottawa. The experimental data were used for training and testing the ANFIS network model. The model was then optimised. The predicted values were found to be in very good agreement with the experimental values with mean relative error less than 0.18% and 3.26% for the preheat tank stratification temperatures and the solar fractions, respectively. The results demonstrate that the ANFIS approach can provide high accuracy and reliability for predicting the performance of thermal energy systems.

Artificial neural network modelling for performance prediction of solar energy system

This study sought to investigate the effect of the number of input variables on both the accuracy and the robustness of the artificial neural network (ANN) method for predicting the performance parameters of a solar energy system. Tests were conducted on a solar energy system in Ottawa, Canada during summer under different weather conditions. Three different ANN models, i.e., one each with nine, eight and seven input variables, were developed and compared to a baseline ANN model previously developed by the authors [14]. The experimental data were used for constructing the ANN models in order to assess their reliability. Each of the models was applied in an effort to predict several performance parameters of the system. The data revealed that the optimal algorithms and topologies were the Levenberg-Marquardt algorithm and the structure with 9/8/7 inputs, 20 hidden and 8 outputs, respectively. The simulation results demonstrated the efficiency of this approach and delivered a good measure of precision, even with models employing reduced input variables. However, it is likely true that the degree of model accuracy would gradually decrease with reduced inputs. Overall, the results of this contribution reveal that the ANN technique provides both high precision and strong robustness for predicting the performance of highly nonlinear energy systems.

Zonal analysis of PHEVs/EVs penetration: A mean to mitigate vehicle emissions

Due to the increasing threat of the environmental impact of vehicle emissions, actions need to be taken against them. As a response, Electric Vehicles (EV) and Plug-in Hybrid Electric Vehicles (PHEVs) are introduced to the market with the purpose of mitigating the quantity of pollutions emitted by fuel consumption. This paper shows that with the increasing adoption of EVs and PHEVs, emissions decrease significantly through 2012 - 2050, specifically in three zones which are The Metropolitan Area of Toronto, Hamilton, and Ottawa Ontario. This is presented by assuming three different scenarios considering different rate of EVs and PHEVs penetration. The number of related EVs and PHEVs through each scenario is forecasted. To show the quantity of emissions produced in the zones considering the scenarios, the emissions factor for Greenhouse Gases pollutants are found. In the next step, the total commuting distances of vehicles in each scenario are found and when combined with the emissions factors, the total emissions produced in each scenario are presented. The main contribution of this paper confirms that by adopting modern vehicles in three main zones of Ontario, Canada, the total emissions could decrease by almost 40% to 50%, which would be very significant amount.

Experimental and Simulation Study on a Solar Domestic Hot Water System With Flat-Plate Collectors for the Canadian Climatic Conditions

This paper presents thermal performance results of an experimental and numerical simulation study of a solar domestic hot water system (SDHW) for Canadian weather conditions. The experimental test setup includes two solar panels, a solar preheat tank, and an auxiliary propane-fired storage water heater, and an air handler unit for space heating. Experiments were performed on the SDHW system during a different season of the year, over the period March through October 2011 to assess the system performance for different solar gain and water draw schedules. Sunny, partly cloudy and cloudy conditions were explored. The test results were analysed in terms of solar fraction, solar efficiency, and the effects of thermosyphoning and stratification in the solar storage tank.Modelling and simulation of the solar thermal energy system using TRNSYS software was performed. The objective was to optimise key design parameters and to suggest an effective control strategy to maximise the heat extraction from solar collectors. The developed model was based on the experimental test setup. It was first adjusted and verified with the solar gain and water draw schedule experimental data. The results of the numerical simulations were then validated with experimental results obtained with other water draw schedule and weather conditions. Acceptable agreements between the predicted and measured values were obtained at this early stage of development. Further refinements in system and model validation are in progress in order to improve the accuracy of the predictions. Ultimately, as the final product of this investigation, this model will be used to predict the performance of solar domestic hot water and space heating systems in different Canadian locations, different operating conditions and water draw schedules.Copyright

Investigation of energy, emission, and cost advantages of energy hubs: A simulation case study for Toronto

This work uses TRNSYS simulations with archetypal residential and commercial building energy demand data to evaluate the performance of a multi-building energy hub with a combined cooling, heating, and power (CCHP) system. Within this framework, conventional and combined heat and power (CHP) energy generation technologies are implemented in different scenarios to meet the cooling, heating, and domestic hot water demands of the energy hub system. Detailed system performances over the year are simulated using an annual simulation. The energy generation, environmental impact, and economic implications of the energy hub simulation results are analyzed and compared against the same multi-building system with independent heating, ventilation, and air conditioning (HVAC) systems. The results indicate that a combined system has potential benefits in local energy security, in emission reduction, and in lowering the operating costs of CHP systems in an energy hub as compared to independent systems. The results also indicate that application of CHP technology in a Toronto scenario results in a decrease in operational costs compared to a conventional HVAC system. Lastly, evaluation of the environmental impact of each simulated system indicates that the CHP system would increase emissions in an Ontario scenario.

Forecasting the Impact of Plug-In Hybrid Electric Vehicles Penetration on Ontario’s Electricity Grid

Vehicle emissions are a major concern in the development of new automobiles. Plug-in hybrid electric vehicles (PHEVs) have a large potential to reduce greenhouse gases emissions and increase fuel economy and fuel flexibility. PHEVs are propelled by the energy from both gasoline and electric power sources. Penetration of PHEVs into the automobile market affects the electrical grid and increasing the electricity demand has not been fully investigated. This paper studies effects of the wide spread adoption of PHEVs on peak and base load demands in Ontario, Canada. Long-term forecasting models of peak and base load demands and the number of light-duty vehicles sold are developed. To create proper forecasting models, both linear regression (LR) and non-linear regression (NLR) techniques are employed, considering different ranges in the demographic, climate and economic variables. The results from the LR and NLR models (LRM and NLRM) are compared and the most accurate one is selected. Furthermore, forecasting the effects of PHEVs penetration is done through consideration of various scenarios of penetration levels, such as mild, normal and aggressive ones. Finally, the additional electricity demand on the Ontario electricity grid from charging PHEVs is incorporated for electricity production planning purposes.Copyright