Modeling of advanced alkaline electrolyzers: a system simulation approach

Ulleberg, Øystein

doi:10.1016/s0360-3199(02)00033-2

Cited by 738 publications

(392 citation statements)

References 6 publications

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“…Based on these assumptions the change in enthalpy δh, entropy δs, and Gibbs energy δg of the water splitting reaction can be calculated with reference to pure hydrogen (H 2 ), oxygen (O 2 ), and water (H 2 O) at standard temperature and pressure (25 • C and 1 bar) [2]. The total change in enthalpy for splitting water is the enthalpy difference between the products (H 2 and O 2 ) and the reactants (H 2 O).…”

Section: Thermodynamic Modelmentioning

confidence: 99%

“…That is, the curve of the cell voltage against the current. The basic form of the I-V curve is given by [2]:…”

Section: Electrochemical Modelmentioning

confidence: 99%

“…In order to properly model the temperature dependence of overvoltages, the following form was proposed by Ulleberg [2] and presented graphically in Figure 2…”

Section: Electrochemical Modelmentioning

confidence: 99%

“…In order to find the six parameters needed in the proposed empirical I-V relationship (6b), a systematic sevenstep procedure for obtaining the best possible curve fit is presented as follows [2].…”

Section: Electrochemical Modelmentioning

confidence: 99%

“…The efficiency of water electrolysis is defined as the ratio of hydrogen energy content (the energy that can be recovered by reoxidation of the hydrogen and oxygen to water) to the electrical energy supplied to the electrolyzer [2]. In terms of voltage, the efficiency can be expressed as…”

Section: Electrochemical Modelmentioning

confidence: 99%

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Optimum Alkaline Electrolyzer-Proton Exchange Membrane Fuel Cell Coupling in a Residential Solar Stand-Alone Power System

Khater

Abdelraouf

Beshr

2011

ISRN Renewable Energy

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Modeling of an alkaline electrolyzer and a proton exchange membrane fuel cell (PEMFC) is presented. Also, a parametric study is performed for both components in order to determine the effect of variable operating conditions on their performance. The aim of this study is to determine the optimum operating conditions when the electrolyzer and the PEMFC are coupled together as part of a residential solar powered stand-alone power system comprising photovoltaic (PV) arrays, an alkaline electrolyzer, storage tanks, a secondary battery, and a PEMFC. The optimum conditions are determined based on an economic study which is performed to determine the cost of electricity (COE) produced from this system so as to determine the lowest possible COE. All of the calculations are performed using a computer code developed by using MATLAB. The code is designed so that any user can easily change the data concerning the location of the system or the working parameters of any of the system's components to estimate the performance of a modified system. Cairo city in Egypt was used as the place at which the output of the system will be determined. It was found that the optimum operating temperature of the electrolyzer is 25 • C. Also, the optimum coupling pressure of the electrolyzer and the PEMFC is 4 bars. The operating temperature of the PEMFC had a slight effect on its performance while an optimum current density of 400 mA/cm 2 was detected. By operating the fuel cell at optimum conditions, its efficiency was found to be 64.66% with a need of 0.5168 Nm 3 (Nm 3 is a m 3 measured at temperature of 0 • C and pressure of 1 bar) of hydrogen to produce 1 kWh of electricity while its cogeneration efficiency was found to be 84.34%. The COE of the system was found to be 49 cents/kWh, at an overall efficiency of 9.87%, for an operational life of 20 years.

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Section: Thermodynamic Modelmentioning

confidence: 99%

“…That is, the curve of the cell voltage against the current. The basic form of the I-V curve is given by [2]:…”

Section: Electrochemical Modelmentioning

confidence: 99%

“…In order to properly model the temperature dependence of overvoltages, the following form was proposed by Ulleberg [2] and presented graphically in Figure 2…”

Section: Electrochemical Modelmentioning

confidence: 99%

Section: Electrochemical Modelmentioning

confidence: 99%

Section: Electrochemical Modelmentioning

confidence: 99%

See 3 more Smart Citations

Optimum Alkaline Electrolyzer-Proton Exchange Membrane Fuel Cell Coupling in a Residential Solar Stand-Alone Power System

Khater

Abdelraouf

Beshr

2011

ISRN Renewable Energy

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Frequency regulation in islanded microgrid considering stochastic model of wind and PV

Kumar

Mathur

Bhanot

et al. 2019

Int Trans Electr Energ Syst

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Summary This paper addresses the method of forecasting the wind and solar power and its application to an islanded microgrid (MG) model for load frequency control. Due to high penetration of renewable energy sources, the islanded MG suffers from lower equivalent inertia. The islanded MG faces several challenges in order to ensure the stable operation by maintaining the frequency and voltage at nominal value. The supply and demand power mismatch is mainly due to continuously changing solar irradiance, fluctuating wind speed, variable inertia, and load fluctuations. The intermittent nature of RESs can significantly affect the system stability; hence, the challenge lies in accurate forecasting of power from the renewable energy sources (RESs) so that a proactive arrangement is made available for compensation of active power or frequency variations. The forecasting will determine the correct estimate of power availability so that the power reserves can be activated prior to large variations in active power affecting the stability of the MGs. To address these challenges, a stochastic model of wind and solar has been developed using “Time series modeling” of the data obtained from Charanka Solar Park under Gujarat Energy Development Agency, India. Wind and solar power availability are forecasted using autoregressive integrated moving average (ARIMA) method including the seasonality factor. The proportional and integral (PI) controller is used for regulating the frequency fluctuations caused due to intermittency in the output of RESs and load power. Various load patterns are applied to the MG model to analyze its load frequency behavior along with variations in secondary sources.

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