Energy, exergy and economic analysis of a novel solar driven CCHP system powered by organic Rankine cycle and photovoltaic thermal collector

Zarei, Ahmad; Akhavan, S.; Rabiee, Marzie Babaie; Elahi, Sohail

doi:10.1016/j.applthermaleng.2021.117091

Cited by 49 publications

(2 citation statements)

References 46 publications

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“…Other works [14][15][16] have been devoted to optimizing the operating and design parameters of CHPCs that incorporate a PEMFC towards a best performance recovery. Organic Rankine Cycle systems (ORC) were used to recover heat from Diesel engines exhaust [17], stationary gas engines [18], geothermal energy [19,20] and solar thermal energy [21][22].…”

Section: Erevmentioning

confidence: 99%

Performance prediction and optimization of a combined PEMFC-ORC system

ABDI,

KETFI,

MAPENGO

et al. 2023

Preprint

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The present paper is devoted to prediction of the performance of a combined system, consisting of a proton exchange membrane fuel cell integrated with an organic Rankine cycle (PEMFC-ORC). The PEMFC waste heat is recovered by an ORC system for electricity generation. The R123 was firstly selected as a working fluid which improved the net power and overall efficiency of the combined system by 13.76% and 5.19%, respectively, in comparison with the PEMFC system. Here, a validation study was conducted by comparing the obtained results with a literature based case. It was also outlined how stack operating pressures and temperatures affect the system performances. Moreover, an optimization study was performed to select the operating parameters values that maximize the system's power output. Accordingly, the net electrical output and overall efficiency of the system using the basic working fluid R123, have been improved by 16.17% and 5.47%, respectively.

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Section: Erevmentioning

confidence: 99%

Performance prediction and optimization of a combined PEMFC-ORC system

ABDI,

KETFI,

MAPENGO

et al. 2023

Preprint

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“…The results show that using the glass cover has generally reduced the energy produced by 3.18% and increased the energy exergy by 31.3%. Zarei et al 19 used a combined solar-ORC-VCRC system for the demand loads of a building. The results show that the best operation of the system is when using R123 refrigerant.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence‐based tri‐objective optimization of different demand load patterns on the optimal sizing of a smart educational buildings

Hosseini

Naserabad

Keshavarzzadeh

et al. 2022

Intl J of Energy Research

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In this research, an integrated energy system for providing triple loads of an educational building has been modeled, analyzed, and optimized using artificial intelligence The use of different load supply patterns for the integrated system was optimized and evaluated from the point of view of exergy, economics, and environment. SketchUp, OpenStudio, and EnergyPlus are used to obtain the building loads, and the integrated system is dynamically modeled by the MATLAB software. The integrated system has been optimized based on different patterns, including monthly, seasonal, and two-state constant loads compared with hourly dynamic loads using an artificial intelligence genetic algorithm. The results show that the dynamic load pattern gives the highest exergy efficiency and the lowest total cost rate. The exergy efficiency and total cost rate in this scenario are 58.78% and 5.022 $/h, respectively. Also, the CO 2 emission index has reached 363.1 g/kWh. Due to the subsidized price of grid electricity, there is a tendency to use low-capacity gas turbines and buy electricity from the grid for the dynamic model. The gas turbine capacity is 35.56 kW, and the system has purchased 242.4 MWh from the grid. Due to high heating loads, the capacity of the gas turbine significantly increases, and no electricity is purchased from the grid in the fixed-load pattern. The highest capacity of the gas turbine is 161.6 kW, which is obtained by a seasonal fixedload pattern. Also, 994.9 MWh of electricity is sold to the grid in this mode.

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