- Email: [email protected]
Performance assessment of solar-powered high pressure proton exchange membrane electrolyzer: A case study for Erzincan
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 4 ( 2 0 1 9 ) 9 7 0 1 e9 7 0 7
Available online at www.sciencedirect.com
ScienceDirect journal homepage: www.elsevier.com/locate/he
Performance assessment of solar-powered high pressure proton exchange membrane electrolyzer: A case study for Erzincan Hadi Ganjehsarabi Campus, Erzincan Binali Yıldırım University, Faculty of Engineering, Mechanical Engineering Department, Yalnızbag Erzincan, 24100, Turkey
article info
abstract
Article history:
In this study, a performance assessment of a solar-powered high-pressure proton ex-
Received 1 October 2018
change membrane (PEM) electrolyzer for hydrogen production is conducted. The feasibility
Received in revised form
analysis of photovoltaic systems equipped with a high pressure PEM electrolyzer is pre-
11 November 2018
sented for a university campus-scale community in Erzincan- Turkey. Variable solar irra-
Accepted 4 December 2018
diance data sets are utilized to assess the performance of the proposed system. A
Available online 28 December 2018
parametric study is conducted in order to evaluate the influence of some design parameters as well as operating conditions on the efficiency of the system. Efficiency of the overall
Keywords:
system in the case of relevant inverter sizing is in the range of 11e12%. An ascent of the
Efficiency
number of stacks leads to an increase in production rate which is almost linear by
PEM electrolysis
photovoltaic (PV) array size. The results shows that in order to have a higher efficiency, the
High-pressure hydrogen production
inverter size should be higher than 0.75% of maximum excess power. The proposed system
Photovoltaics
investigated in this study shows great promise of opening up opportunity to develop the high pressure PEM electrolyzer. © 2018 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
Introduction Hydrogen energy as an energy carrier is likely to play key role in the future due to its advantages such as high energy content, bountiful in supply, non-toxic and efficient [1e3]. However, the generality of hydrogen is generated by fossil fuels. Taking into account hydrogen as potential transportation fuel, it is required to be generated by integrating with renewable resources. The proton exchange membrane (PEM) electrolyzers are a mature technology for hydrogen production due to their incorporating with alternative energy sources and higher efficiency.
The idea of decomposing of water by supplying a voltage to electrodes submerged in a solution, as a water electrolysis, was proposed in the nineteenth century. Hydrogen is produced on the cathode side. The production of hydrogen in higher pressure enables storage of hydrogen in tanks without using compressors. Acar and Dincer [4] examined experimentally and thermodynamically a hybrid photoelectrochemical hydrogen production reactor using energy and exergy analyses. Their result showed that the energy and exergy efficiencies of the proposed system are 36% and 32%, respectively. Nie et al. [5] developed a photo-electrochemical model for the generation of hydrogen in the PEM electrolyzer. The Effect of temperature on the voltage and hydrogen
E-mail address: [email protected]. https://doi.org/10.1016/j.ijhydene.2018.12.007 0360-3199/© 2018 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
9702
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 4 ( 2 0 1 9 ) 9 7 0 1 e9 7 0 7
generation is investigated. Olivier et al. [6] developed a dynamic and multiphysic model under bond graph modeling formalism for a proton exchange membrane electrolysis. Awasthi et al. [7] developed a computational model of PEM electrolyzer system examining the influence of operating conditions and electrolyzer components on its performance. Marangio et al. [8] show a comprehensive model of electrolyzer. They developed a model to provide insights into the characteristics of electrolyzer, such as activation, ohmic and diffusion overvoltages. The high-pressure PEM electrolyzers integrated with renewable energy sources are the best candidate for energy storage methods. This technology has received considerable attention, specifically due to the enormous cost reduction and process simplification which do not require mechanical compression stages [9]. In addition, the power needed to generate hydrogen by using electrolysis with mechanical compression stages is higher than the high pressure electrolysis. Hereby, the efficiency of high pressure electrolysis is higher than the integrated electrolysis with mechanical compression process. Extensive research efforts in all aspects of the high pressure electrolysis field have advanced the technology, and have brought high pressure electrolysis to a state of pre-commercial viability [10]. Roy et al. [11] investigated and compared the impact of operating pressure of practical water electrolysis on energy consumption and reversible voltage. Santarelli et al. [12] experimentally studied the effect of various parameters such as temperature, pressure, and water flow on the performance of a highpressure PEM electrolyzer. They concluded that an increase in the operating pressure would require higher operating temperature to decrease the decline of performance. Laoun [13] performed a comprehensive model for high pressure electrolysis based on the free Gibbs energy of water electrolysis. Bensmann et al. [14] assessed and compared three methods for high pressure hydrogen production via water electrolysis. Their results showed that solid polymer electrolyte (SPE) water electrolysis presents better performance for hydrogen production. Schalenbach et al. [15] studied the effect of cathode and anode pressures during PEM water electrolysis on the gas crossover. Their results showed that the membrane thickness affects efficiency loss which must be optimized. Selamet et al. [16] designed and tested a PEM electrolyzer, which is comprised of a10 cell stack. They reported that the efficiency of the system is 87% at 1 A/cm2 current density. Aouali et al. [17] investigated the performance assessment of a PEM electrolyzer integrated with solar photovoltaic technology. Sarrias-Mena et al. [18] proposed and examined four different configurations of electrolyzer powered by wind turbine. These configurations are assessed, as well as their performances compared with respect to various wind speeds and grid demand. Grigoriev et al. [19] conducted a numerical optimization of (PEM) water electrolyzers operating in higher pressures. They examined the impact of different design on efficiency and heat generation through water electrolysis. The efficiency of their system is 80% at 0.8 A/cm2 current density and 80 C operating temperature. Performance analysis of a system is a crucial step in system planning and production estimation in the feasibility study phase. Especially when a system shows extremely dynamic behavior as is the common case in renewable energy systems.
Single design point calculation with average data is a very common approach in system performance evaluation but this type of calculations provides reasonable results when the plan is mostly operating in full or near full design load. Dynamic systems usually show significant deviations from single design point approaches. Systems including winds and solar energy are very time-dependent and sensitive to weather conditions. A method to evaluate the performance of dynamic systems is transient and off design performance evaluation. These methods usually need detailed approaches for components which is time consuming and costly and not proper at the planning and feasibility stage. An original approach for quick but accurate dynamic performance estimation is proposed in this research. This method shows how all resources are implemented to achieve an accurate model. Solar panels are vastly analyzed and the technology is mature enough to be modeled by software packages reliably. So here, solar panels are modeled dynamically using the National Renewable Energy Laboratory (NREL) System Advisor Model (SAM) solar array system planning and sizing package which provides fast, free, and reliable data [20]. In this current study, planning of a solar PV-PEM electrolyzer is carried out for feasibility and basic design of system. A brief synopsis of the objective and motivation for this current study may be listed as follows: To examine the effect of design parameters such as array size, number of stacks, and electrolyzer capacity on the performance of a solar PV-PEM electrolyzer; To develop a dynamic model for analyzing a hybrid system consisting of a solar PV collector and a high pressure PEM electrolyzer
System description The hybrid solar-electrolyzer energy system investigated in this study (see Fig. 1) comprises a PV solar panel to generate electricity and a high pressure PEM electrolyzer to produce hydrogen [21].
Model The electrolyzer voltage, Velectrolyzer, is defined as: Velectrolyzer ¼ Vact þ VU þ E
(1)
here Vact, VU and E are the activation polarization, ohmic polarization, and the open circuit voltage, respectively. The open circuit voltage, E, can be expressed as follows [22]: " # PH2 $PO0:52 RTelectrolyzer ln E ¼ Eo þ 2F aH2 O
(2)
where, R, Telectrolyzer, F and aH2O refer to the universal gas constant, the electrolyzer temperature, Faraday's constant, and the water activity between anode and electrolyte, respectively. The standard voltage, Eo, can be calculated as follows:
9703
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 4 ( 2 0 1 9 ) 9 7 0 1 e9 7 0 7
Table 1 e SAM simulation configuration. Parameter Location Weather data Solar panel model Nominal Efficiency Tracking Losses Heat transfer System modeling
Value Erzincan, Turkey Erzincan measuring site data 1SolTech 260-M60-BLK 15.55% 2 Axis Tracking Total losses: 4.4% DC- 1% AC NOCT model System desired array size
Fig. 2 illustrates the schematic for the system concept. The main system performance modeling is shown in Fig. 3. For a given number of stacks and a given PV array size the number of stacks and working temperature and pressure for a stack, the first step is to calculate the power output of solar cell. DC power then divided over stacks and current density and voltage cell are calculated. In stack modeling, the algorithm in Fig. 4 is used. By using an initial estimate for voltage, cell current density are calculated. Fig. 1 e Schematic of the PV-PEM system. i¼
Eo ¼
DG 2F
(3)
The activation polarizations can be expressed as follows [23]: Vact ¼
RTelectrolyzer i ln io 2aF
(4)
where, a, i and io refer to the charge transfer coefficient, current density, and the exchange current density, respectively. The membrane resistance can be computed as: Rohm ¼
tm sm
(5)
WDC nstack Vstack ncps Ac
Then according to Eqs. (1)e(3), new voltage is calculated and the process is repeated until voltage and current density converge to the final value. After current density evaluation, hydrogen production rate is calculated as followings: (F is Faraday constant.) n_H2 ¼
i nstack ncps Ac 2F
(6)
For solar cell modeling, SAM software is used. The California Energy Commission (CEC) performance model with model database is used for the module (array) modeling which uses the set of available PV technologies database. The SOLTECH solar panel with a nominal efficiency of 15.5% is used as the reference PV for the calculation. According to the database, the average efficiency is between 15 and 16%. Table 1 provides the model parameters in the software: Simulation is carried out for Erzincan, Turkey, using the measured weather data. Fig. 1 shows annual variations for plane of array irradiance (POA) calculated by SAM from weather data. After model setup for the PV system, DC power output is calculated. DC output power is then converted in hydrogen by the electrolyzer. The electrolyzer is set to 1 A/cm2 current density. So, in the case that the current is higher, the excess power is directed to an inverter to convert it into AC power.
(8)
In case that the calculated current density is greater than 1, we have excess power since the current density is limited to 1. Excess power is calculated by:
where, sm is the conductivity of the membrane. The ohmic polarization can be obtained by using the following equation VU ¼ iRohm
(7)
Fig. 2 e Plan of array (POA) in different hours of a year. Calculated from weather data.
9704
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 4 ( 2 0 1 9 ) 9 7 0 1 e9 7 0 7
and average excess power as maximum and yearly average excess power produced: P8759 WExAVE ¼
WEx 8760
0
(10)
WExMAX ¼ Maxð WEx Þ
(11)
WExMAX WExAVE
(12)
ExR ¼
CapRatio ¼
ExR 1 *ðj 1Þ þ 1j j ¼ 1; 2; ::; 5 4
InvCap ¼ CapRatio WExAVE
(13)
(14)
By using the above definition, we have five inverter capacity for each design (stack and array size). After inverter sizing is carried out, AC output power is calculated from the inverter performance curve (Fig. 5. In order to analyze the sensitivity of hydrogen production with respect to the operating temperature and pressure the following parameter is defined here:
Fig. 3 e Flowchart of system performance evaluation algorithm.
S¼
nH2 Max nH2 Min 100 nH2Ave
(15)
here nH2 Max , nH2 Min , and nH2Ave are maximum, minimum and average hydrogen production when operating temperature and pressure changes. To evaluate the performance efficiency of the system is defined as followings: h¼
WExAC þ nH2 LHV ESolYearly
Yearly
(16)
In Which, ESolYearly is the solar energy input to the panel array. Efficiency of the system is a function of various variables such as solar cell, inverter, and electrolyzer
Fig. 4 e Electrolyzer modeling approach.
WEx ¼ WDC I* V*
(9)
I* and V* are current and voltage at maximum current allowed current density (1 A/cm2). Excess power is converted to AC power using an inverter. Sizing of the inverter is important. If it is oversized, output AC power reduces due to lower efficiency. If the inventor capacity is small, the inverter will not be capable of converting all available DC power. Since the power produced by system changes over time, excess power will also show the same behavior. So, we define max
Fig. 5 e Representative comparison of the numerical polarization curve results with the experimental data for 30 bar.
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 4 ( 2 0 1 9 ) 9 7 0 1 e9 7 0 7
9705
performance, which are functions of electrolyzer and array size. Here the energy ratio (ER) is defined to help us better characterize the efficiency behavior while system size is changing. ER ¼
Eelecyearly Eelecyearly þ EExyearly
(17)
Inverter sizing is carried out according to Eqs. (10)e(14).
Results and discussion The provided methodology is performed on the data for Erzincan Binali Yıldırım university campus. Fig. 5 illustrates the numerical and experimental polarization curves of a highpressure proton exchange membrane electrolyzer operating at 30 bar. The deviation between the experimental data and numerical results appeared to be less than 5%. Deviations between the predictions and experimental results primarily appear at low current densities and at high current densities. Fig. 6 shows the hydrogen production rate as function of PV array size for two different numbers of stacks. The continuous line shows the production rate for average operating conditions and dashed lines shows the bonds for production rate due to temperature and pressure variations. Hydrogen production in one stack at first increases by the array size sharply then remains almost constant since current density is reaching its maximum. When the number of stacks is significantly higher, production rate rises almost linearly by PV array size. The power limit for each stack is around 2 kW and these curves show that the PV system size before electrolyzer current density limit is reached (near 2 kW per stack) shows a linear correlation with PV array size. At high production rate, absolute production changes due to temperature and pressure are higher than small system size and production rate. Fig. 7 illustrates the sensitivity of hydrogen production at different array and electrolyzer sizes. As can be seen in this figure, the higher number of stack and power leads to higher sensitivity. Temperature and pressure variations directly
Fig. 6 e Hydrogen production rate for different array size. Dashed lines shows bonds of minimum and maximum production rate due to temperature and pressure change.
Fig. 7 e Sensitivity of production at different array and electrolyzer size.
affect the amount of power per cell, as well as hydrogen production rate. Sensitivity of hydrogen production is 8% and a reasonable value to be considered here. Excess power variation is a function of stack size and desired array size. Fig. 8 illustrates the excess AC power at different array sizes and for 1 and 13 stack sizes. In this figure the average excess power and maximum excess power are plotted for stack sizes of 1 and 13. Each graph shows a zero value at the start, a curved behavior for the next range and after that a linear relationship between the excess power and array size is observed. At the starting point, the power from the array is small and no excess power is available. After the power increases to surplus of the stack's capacity, excess power starts rising gradually until it reaches a point that all stacks operate with max current during almost all working hours. At this point a linear relationship between excess power and array size is observed. Another important observation from the figure is the effect of inverter sizing. As it is seen from Fig. 7, the total energy produced yearly when the inverter sizing is based on average or maximum power, shows a dramatic difference for both stack sizes. This figure shows that the effect of lower efficiency due to oversizing is neglectable in comparison to power loss due to small inverter size. The reason behind this is the considerably flattened invertor efficiency in a wide range of partial load which promotes using higher nominal capacity. Inverter sizing shows a significant effect on the AC output power. After obtaining and processing the results, it is observed that inverter sizing can be expressed as the following corresponding ranges (Table 2) in terms of maximum excess power: Fig. 9 shows efficiency as function of energy ratio and inverter size. It is shown that for different energy ratio values and inverter sizes, efficiency shows different trends. However, all curves reach almost the same value when the energy ratio tends to 1 which means at no excess power. Inverter sizing with AVE, or average excess power, has the lowest and inverter sizing with max or 0.75e0.8 Max excess power, shows the maximum efficiency. For almost all curves, the maximum
9706
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 4 ( 2 0 1 9 ) 9 7 0 1 e9 7 0 7
Fig. 8 e Excess energy for different array size.
Fig. 10 e Efficiency versus specific Power (array size (kW) per stack).
Table 2 e Inverter size range and its corresponding capacity ratio. CapRatio (Eqs (3)e(8)) 1 ExR 1 ðj 1Þ j ¼ 2 4 ExR 1 ðj 1Þ j ¼ 3 4 ExR 1 ðj 1Þ j ¼ 4 4 ExR
Inverter size
Notation on graph legend
WExAVE (0.25,0.4) WExMAX
AVE (0.25,0.4) Max
(0.5,0.6) WExMAX
(0.5,0.6) Max
(0.75,0.8) WExMAX
(0.75,0.8) Max
WExMAX
Max
To understand the efficiency behavior, it is also useful to plot the efficiency versus electrolyzer specific power. In Fig. 10, system efficiency is plotted as function of specific power. All curves pass through an almost identical point at a specific power of 2 kW per stack, which is the current density limit. After that, all curves shows an optimal value before reaching the 5 kW per stack. After the optimal point, for low inverter size, the efficiency reduces until a certain minimal point, after which a constant efficiency line is observed.
Conclusions In this research, the performance analysis of a solar PV-PEM electrolyzer is carried out. Array and stack sizing effects are evaluated for better understanding and system planning. With the model provided here, a quick and accurate dynamic modeling for the system is presented. The results of dynamic modeling for this work can be listed as follows:
Fig. 9 e Efficiency of the system versus ER and at different inverter sizing.
point efficiency reaches ER values in range of 0.8e0.95. Since efficiency is a function of specific desired power as well as ER, for each ER value there is more than one point in Efficiency curve, corresponding to different specific desired work. This can be interpreted from Fig. 10 and they (Figs. 9 and 10) should be considered together.
The dynamic model developed for the integrated PV system to high pressure PEM electrolyzer provide a method for calculating the amount of hydrogen production for varying operating conditions. As the energy ratio (ER) changes from 0 to 1, the value of efficiency increases up to a point and then decreases. The maximum efficiency is found at ER values in the range of 0.8e0.95. Hydrogen production rate (annually) is sensitive to cell operating temperature and pressure and the sensitivity of hydrogen production as obtained here, is 8%. Efficiency of the overall system in case of relevant inverter sizing is in range of 11e12%. AVE Excess energy is not a suitable inverter sizing move and instead inverter size should be higher than 0.75% of maximum excess power.
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 4 ( 2 0 1 9 ) 9 7 0 1 e9 7 0 7
Acknowledgements The authors would like to thank Hamed Jahed and SOLEOSEnerji Company for providing the hourly demand data for Erzincan University Campus.
Nomenclature Abbreviation AC Alternating current Ave Average CapRatio Capacity ratio CEC California Energy Commission DC Direct current ER Energy ratio Inv Invertor NREL National Renewable Energy Laboratory POA Plan of array PV Photovoltaic SAM System advisor model Sol Solar Variable I I E F R S t V W
Current generated by the PV module, A Cell current density, A cm2 Energy, kj Faraday's constant, 96485 C/mol Universal gas constant, 8.31446 J/mol/K Sensitivity of production rate Thickness, m Voltage generated by the PV module, V Power, kW
Greek variable a Charge transfer coefficient h Efficiency U Ohmic Superscripts/Subscripts act Activation Cap Capacity ex Excess power m Membrane Min Minimum Max Maximum
references
[1] Dincer I. Technical, environmental and exergetic aspects of hydrogen energy systems. Int J Hydrogen Energy 2002;27(3):265e85. [2] Momirlan M, Veziroglu TN. The properties of hydrogen as fuel tomorrow in sustainable energy system for a cleaner planet. Int J Hydrogen Energy 2005;30:795e802.
9707
[3] Ball M, Wietschel M. The future of hydrogen opportunities and challenges. Int J Hydrogen Energy 2009;34:615e27. [4] Acar C, Dincer I. Energy and exergy analyses of a novel photoelectrochemical hydrogen production system. Int J Hydrogen Energy 2017;42:30550e8. [5] Nie J, Chen Y, Boehm RF, Katukota S. A photochemical model of proton exchange water electrolysis for hydrogen production. J Heat Tran 2008;130. 042409-1-042409-6. [6] Oliviera P, Bourasseau C, Bouamam B. Dynamic and multiphysic PEM electrolysis system modelling: a bond graph approach. Int J Hydrogen Energy 2017;42:14872e904. [7] Awasthi A, Scott K, Basu S. Dynamic modeling and simulation of a proton exchange membrane electrolyzer for hydrogen production. Int J Hydrogen Energy 2011;36:14779e86. [8] Marangio F, Santarelli M, Cali M. Theoretical model and experimental analysis of a high pressure PEM water electrolyzer for hydrogen production. Int J Hydrogen Energy 2009;34:1143e58. [9] Zoulias E, Varkaraki E, Lymberopoulos N, Christodoulou CN, Karagiorgis GN. A review on water electrolysis. TCJST 2004;4:41e71. [10] Onda K, Kyakuno T, Hattori K, Ito K. Prediction of production power for high-pressure hydrogen by high-pressure water electrolysis. J Power Sources 2004;132:64e70. [11] Roy A, Watson S, Infield D. Comparison of electrical energy efficiency of atmospheric and high-pressure electrolysers. Int J Hydrogen Energy 2006;31:1964e79. [12] Santarelli M, Medina P, Calı M. Fitting regression model and experimental validation for a high pressure PEM electrolyzer. Int J Hydrogen Energy 2009;34:2519e30. [13] Laoun B. Thermodynamics aspect of high pressure hydrogen production by water electrolysis. Revue des Energies Renouvelables 2007;10:435e44. [14] Bensmann B, Hanke-Rauschenbach R, PenaeArias IK, Sundmacher K. Energetic evaluation of high pressure PEM electrolyzer systems. Electrochim Acta 2013;110:570e80. [15] Schalenbach M, Carmo M, Fritz DL, Mergel J, Stolten D. Pressurized PEM water electrolysis: efficiency and gas crossover. Int J Hydrogen Energy 2013;38:14921e33. [16] Selamet OF, Acar MC, Mat MD, Kaplan Y. Effects of operating parameters on the performance of a high-pressure proton exchange membrane electrolyzer. Int J Energy Res 2013;37:457e67. [17] Aouali FZ, Becherif M, Tabanjat A, Emziane M, Mohammedi K, Krehi S, Khellaf A. Modelling and experimental analysis of a PEM electrolyser powered by a solar photovoltaic panel. Energy Procedia 2014;62:714e22. [18] Sarrias-Mena R, Fernandez-Ramırez LM, Garcı a-Vazquez CA, Jurado F. Electrolyzer models for hydrogen production from wind energy systems. Int J Hydrogen Energy 2015;40:2927e38. [19] Grigoriev SA, Kalinnikov AA, Millet P, Porembsky VI, Fateev VN. Mathematical modeling of high-pressure PEM water electrolysis. J Appl Electrochem 2010;40:921e32. [20] https://sam.nrel.gov/. [21] Barbir F. PEM electrolysis for production of hydrogen from renewable energy sources. Sol Energy 2005;78:661e9. € rgu¨n H. Dynamic modelling of a proton exchange [22] Go membrane (PEM) electrolyser. Int J Hydrogen Energy 2006;31:29e38. [23] Gilman P. SAM photovoltaic model technical reference, 59. National Renewable Energy Laboratory; 2015. NREL/TP-6A2064102.
Available online at www.sciencedirect.com
ScienceDirect journal homepage: www.elsevier.com/locate/he
Performance assessment of solar-powered high pressure proton exchange membrane electrolyzer: A case study for Erzincan Hadi Ganjehsarabi Campus, Erzincan Binali Yıldırım University, Faculty of Engineering, Mechanical Engineering Department, Yalnızbag Erzincan, 24100, Turkey
article info
abstract
Article history:
In this study, a performance assessment of a solar-powered high-pressure proton ex-
Received 1 October 2018
change membrane (PEM) electrolyzer for hydrogen production is conducted. The feasibility
Received in revised form
analysis of photovoltaic systems equipped with a high pressure PEM electrolyzer is pre-
11 November 2018
sented for a university campus-scale community in Erzincan- Turkey. Variable solar irra-
Accepted 4 December 2018
diance data sets are utilized to assess the performance of the proposed system. A
Available online 28 December 2018
parametric study is conducted in order to evaluate the influence of some design parameters as well as operating conditions on the efficiency of the system. Efficiency of the overall
Keywords:
system in the case of relevant inverter sizing is in the range of 11e12%. An ascent of the
Efficiency
number of stacks leads to an increase in production rate which is almost linear by
PEM electrolysis
photovoltaic (PV) array size. The results shows that in order to have a higher efficiency, the
High-pressure hydrogen production
inverter size should be higher than 0.75% of maximum excess power. The proposed system
Photovoltaics
investigated in this study shows great promise of opening up opportunity to develop the high pressure PEM electrolyzer. © 2018 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
Introduction Hydrogen energy as an energy carrier is likely to play key role in the future due to its advantages such as high energy content, bountiful in supply, non-toxic and efficient [1e3]. However, the generality of hydrogen is generated by fossil fuels. Taking into account hydrogen as potential transportation fuel, it is required to be generated by integrating with renewable resources. The proton exchange membrane (PEM) electrolyzers are a mature technology for hydrogen production due to their incorporating with alternative energy sources and higher efficiency.
The idea of decomposing of water by supplying a voltage to electrodes submerged in a solution, as a water electrolysis, was proposed in the nineteenth century. Hydrogen is produced on the cathode side. The production of hydrogen in higher pressure enables storage of hydrogen in tanks without using compressors. Acar and Dincer [4] examined experimentally and thermodynamically a hybrid photoelectrochemical hydrogen production reactor using energy and exergy analyses. Their result showed that the energy and exergy efficiencies of the proposed system are 36% and 32%, respectively. Nie et al. [5] developed a photo-electrochemical model for the generation of hydrogen in the PEM electrolyzer. The Effect of temperature on the voltage and hydrogen
E-mail address: [email protected]. https://doi.org/10.1016/j.ijhydene.2018.12.007 0360-3199/© 2018 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
9702
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 4 ( 2 0 1 9 ) 9 7 0 1 e9 7 0 7
generation is investigated. Olivier et al. [6] developed a dynamic and multiphysic model under bond graph modeling formalism for a proton exchange membrane electrolysis. Awasthi et al. [7] developed a computational model of PEM electrolyzer system examining the influence of operating conditions and electrolyzer components on its performance. Marangio et al. [8] show a comprehensive model of electrolyzer. They developed a model to provide insights into the characteristics of electrolyzer, such as activation, ohmic and diffusion overvoltages. The high-pressure PEM electrolyzers integrated with renewable energy sources are the best candidate for energy storage methods. This technology has received considerable attention, specifically due to the enormous cost reduction and process simplification which do not require mechanical compression stages [9]. In addition, the power needed to generate hydrogen by using electrolysis with mechanical compression stages is higher than the high pressure electrolysis. Hereby, the efficiency of high pressure electrolysis is higher than the integrated electrolysis with mechanical compression process. Extensive research efforts in all aspects of the high pressure electrolysis field have advanced the technology, and have brought high pressure electrolysis to a state of pre-commercial viability [10]. Roy et al. [11] investigated and compared the impact of operating pressure of practical water electrolysis on energy consumption and reversible voltage. Santarelli et al. [12] experimentally studied the effect of various parameters such as temperature, pressure, and water flow on the performance of a highpressure PEM electrolyzer. They concluded that an increase in the operating pressure would require higher operating temperature to decrease the decline of performance. Laoun [13] performed a comprehensive model for high pressure electrolysis based on the free Gibbs energy of water electrolysis. Bensmann et al. [14] assessed and compared three methods for high pressure hydrogen production via water electrolysis. Their results showed that solid polymer electrolyte (SPE) water electrolysis presents better performance for hydrogen production. Schalenbach et al. [15] studied the effect of cathode and anode pressures during PEM water electrolysis on the gas crossover. Their results showed that the membrane thickness affects efficiency loss which must be optimized. Selamet et al. [16] designed and tested a PEM electrolyzer, which is comprised of a10 cell stack. They reported that the efficiency of the system is 87% at 1 A/cm2 current density. Aouali et al. [17] investigated the performance assessment of a PEM electrolyzer integrated with solar photovoltaic technology. Sarrias-Mena et al. [18] proposed and examined four different configurations of electrolyzer powered by wind turbine. These configurations are assessed, as well as their performances compared with respect to various wind speeds and grid demand. Grigoriev et al. [19] conducted a numerical optimization of (PEM) water electrolyzers operating in higher pressures. They examined the impact of different design on efficiency and heat generation through water electrolysis. The efficiency of their system is 80% at 0.8 A/cm2 current density and 80 C operating temperature. Performance analysis of a system is a crucial step in system planning and production estimation in the feasibility study phase. Especially when a system shows extremely dynamic behavior as is the common case in renewable energy systems.
Single design point calculation with average data is a very common approach in system performance evaluation but this type of calculations provides reasonable results when the plan is mostly operating in full or near full design load. Dynamic systems usually show significant deviations from single design point approaches. Systems including winds and solar energy are very time-dependent and sensitive to weather conditions. A method to evaluate the performance of dynamic systems is transient and off design performance evaluation. These methods usually need detailed approaches for components which is time consuming and costly and not proper at the planning and feasibility stage. An original approach for quick but accurate dynamic performance estimation is proposed in this research. This method shows how all resources are implemented to achieve an accurate model. Solar panels are vastly analyzed and the technology is mature enough to be modeled by software packages reliably. So here, solar panels are modeled dynamically using the National Renewable Energy Laboratory (NREL) System Advisor Model (SAM) solar array system planning and sizing package which provides fast, free, and reliable data [20]. In this current study, planning of a solar PV-PEM electrolyzer is carried out for feasibility and basic design of system. A brief synopsis of the objective and motivation for this current study may be listed as follows: To examine the effect of design parameters such as array size, number of stacks, and electrolyzer capacity on the performance of a solar PV-PEM electrolyzer; To develop a dynamic model for analyzing a hybrid system consisting of a solar PV collector and a high pressure PEM electrolyzer
System description The hybrid solar-electrolyzer energy system investigated in this study (see Fig. 1) comprises a PV solar panel to generate electricity and a high pressure PEM electrolyzer to produce hydrogen [21].
Model The electrolyzer voltage, Velectrolyzer, is defined as: Velectrolyzer ¼ Vact þ VU þ E
(1)
here Vact, VU and E are the activation polarization, ohmic polarization, and the open circuit voltage, respectively. The open circuit voltage, E, can be expressed as follows [22]: " # PH2 $PO0:52 RTelectrolyzer ln E ¼ Eo þ 2F aH2 O
(2)
where, R, Telectrolyzer, F and aH2O refer to the universal gas constant, the electrolyzer temperature, Faraday's constant, and the water activity between anode and electrolyte, respectively. The standard voltage, Eo, can be calculated as follows:
9703
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 4 ( 2 0 1 9 ) 9 7 0 1 e9 7 0 7
Table 1 e SAM simulation configuration. Parameter Location Weather data Solar panel model Nominal Efficiency Tracking Losses Heat transfer System modeling
Value Erzincan, Turkey Erzincan measuring site data 1SolTech 260-M60-BLK 15.55% 2 Axis Tracking Total losses: 4.4% DC- 1% AC NOCT model System desired array size
Fig. 2 illustrates the schematic for the system concept. The main system performance modeling is shown in Fig. 3. For a given number of stacks and a given PV array size the number of stacks and working temperature and pressure for a stack, the first step is to calculate the power output of solar cell. DC power then divided over stacks and current density and voltage cell are calculated. In stack modeling, the algorithm in Fig. 4 is used. By using an initial estimate for voltage, cell current density are calculated. Fig. 1 e Schematic of the PV-PEM system. i¼
Eo ¼
DG 2F
(3)
The activation polarizations can be expressed as follows [23]: Vact ¼
RTelectrolyzer i ln io 2aF
(4)
where, a, i and io refer to the charge transfer coefficient, current density, and the exchange current density, respectively. The membrane resistance can be computed as: Rohm ¼
tm sm
(5)
WDC nstack Vstack ncps Ac
Then according to Eqs. (1)e(3), new voltage is calculated and the process is repeated until voltage and current density converge to the final value. After current density evaluation, hydrogen production rate is calculated as followings: (F is Faraday constant.) n_H2 ¼
i nstack ncps Ac 2F
(6)
For solar cell modeling, SAM software is used. The California Energy Commission (CEC) performance model with model database is used for the module (array) modeling which uses the set of available PV technologies database. The SOLTECH solar panel with a nominal efficiency of 15.5% is used as the reference PV for the calculation. According to the database, the average efficiency is between 15 and 16%. Table 1 provides the model parameters in the software: Simulation is carried out for Erzincan, Turkey, using the measured weather data. Fig. 1 shows annual variations for plane of array irradiance (POA) calculated by SAM from weather data. After model setup for the PV system, DC power output is calculated. DC output power is then converted in hydrogen by the electrolyzer. The electrolyzer is set to 1 A/cm2 current density. So, in the case that the current is higher, the excess power is directed to an inverter to convert it into AC power.
(8)
In case that the calculated current density is greater than 1, we have excess power since the current density is limited to 1. Excess power is calculated by:
where, sm is the conductivity of the membrane. The ohmic polarization can be obtained by using the following equation VU ¼ iRohm
(7)
Fig. 2 e Plan of array (POA) in different hours of a year. Calculated from weather data.
9704
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 4 ( 2 0 1 9 ) 9 7 0 1 e9 7 0 7
and average excess power as maximum and yearly average excess power produced: P8759 WExAVE ¼
WEx 8760
0
(10)
WExMAX ¼ Maxð WEx Þ
(11)
WExMAX WExAVE
(12)
ExR ¼
CapRatio ¼
ExR 1 *ðj 1Þ þ 1j j ¼ 1; 2; ::; 5 4
InvCap ¼ CapRatio WExAVE
(13)
(14)
By using the above definition, we have five inverter capacity for each design (stack and array size). After inverter sizing is carried out, AC output power is calculated from the inverter performance curve (Fig. 5. In order to analyze the sensitivity of hydrogen production with respect to the operating temperature and pressure the following parameter is defined here:
Fig. 3 e Flowchart of system performance evaluation algorithm.
S¼
nH2 Max nH2 Min 100 nH2Ave
(15)
here nH2 Max , nH2 Min , and nH2Ave are maximum, minimum and average hydrogen production when operating temperature and pressure changes. To evaluate the performance efficiency of the system is defined as followings: h¼
WExAC þ nH2 LHV ESolYearly
Yearly
(16)
In Which, ESolYearly is the solar energy input to the panel array. Efficiency of the system is a function of various variables such as solar cell, inverter, and electrolyzer
Fig. 4 e Electrolyzer modeling approach.
WEx ¼ WDC I* V*
(9)
I* and V* are current and voltage at maximum current allowed current density (1 A/cm2). Excess power is converted to AC power using an inverter. Sizing of the inverter is important. If it is oversized, output AC power reduces due to lower efficiency. If the inventor capacity is small, the inverter will not be capable of converting all available DC power. Since the power produced by system changes over time, excess power will also show the same behavior. So, we define max
Fig. 5 e Representative comparison of the numerical polarization curve results with the experimental data for 30 bar.
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 4 ( 2 0 1 9 ) 9 7 0 1 e9 7 0 7
9705
performance, which are functions of electrolyzer and array size. Here the energy ratio (ER) is defined to help us better characterize the efficiency behavior while system size is changing. ER ¼
Eelecyearly Eelecyearly þ EExyearly
(17)
Inverter sizing is carried out according to Eqs. (10)e(14).
Results and discussion The provided methodology is performed on the data for Erzincan Binali Yıldırım university campus. Fig. 5 illustrates the numerical and experimental polarization curves of a highpressure proton exchange membrane electrolyzer operating at 30 bar. The deviation between the experimental data and numerical results appeared to be less than 5%. Deviations between the predictions and experimental results primarily appear at low current densities and at high current densities. Fig. 6 shows the hydrogen production rate as function of PV array size for two different numbers of stacks. The continuous line shows the production rate for average operating conditions and dashed lines shows the bonds for production rate due to temperature and pressure variations. Hydrogen production in one stack at first increases by the array size sharply then remains almost constant since current density is reaching its maximum. When the number of stacks is significantly higher, production rate rises almost linearly by PV array size. The power limit for each stack is around 2 kW and these curves show that the PV system size before electrolyzer current density limit is reached (near 2 kW per stack) shows a linear correlation with PV array size. At high production rate, absolute production changes due to temperature and pressure are higher than small system size and production rate. Fig. 7 illustrates the sensitivity of hydrogen production at different array and electrolyzer sizes. As can be seen in this figure, the higher number of stack and power leads to higher sensitivity. Temperature and pressure variations directly
Fig. 6 e Hydrogen production rate for different array size. Dashed lines shows bonds of minimum and maximum production rate due to temperature and pressure change.
Fig. 7 e Sensitivity of production at different array and electrolyzer size.
affect the amount of power per cell, as well as hydrogen production rate. Sensitivity of hydrogen production is 8% and a reasonable value to be considered here. Excess power variation is a function of stack size and desired array size. Fig. 8 illustrates the excess AC power at different array sizes and for 1 and 13 stack sizes. In this figure the average excess power and maximum excess power are plotted for stack sizes of 1 and 13. Each graph shows a zero value at the start, a curved behavior for the next range and after that a linear relationship between the excess power and array size is observed. At the starting point, the power from the array is small and no excess power is available. After the power increases to surplus of the stack's capacity, excess power starts rising gradually until it reaches a point that all stacks operate with max current during almost all working hours. At this point a linear relationship between excess power and array size is observed. Another important observation from the figure is the effect of inverter sizing. As it is seen from Fig. 7, the total energy produced yearly when the inverter sizing is based on average or maximum power, shows a dramatic difference for both stack sizes. This figure shows that the effect of lower efficiency due to oversizing is neglectable in comparison to power loss due to small inverter size. The reason behind this is the considerably flattened invertor efficiency in a wide range of partial load which promotes using higher nominal capacity. Inverter sizing shows a significant effect on the AC output power. After obtaining and processing the results, it is observed that inverter sizing can be expressed as the following corresponding ranges (Table 2) in terms of maximum excess power: Fig. 9 shows efficiency as function of energy ratio and inverter size. It is shown that for different energy ratio values and inverter sizes, efficiency shows different trends. However, all curves reach almost the same value when the energy ratio tends to 1 which means at no excess power. Inverter sizing with AVE, or average excess power, has the lowest and inverter sizing with max or 0.75e0.8 Max excess power, shows the maximum efficiency. For almost all curves, the maximum
9706
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 4 ( 2 0 1 9 ) 9 7 0 1 e9 7 0 7
Fig. 8 e Excess energy for different array size.
Fig. 10 e Efficiency versus specific Power (array size (kW) per stack).
Table 2 e Inverter size range and its corresponding capacity ratio. CapRatio (Eqs (3)e(8)) 1 ExR 1 ðj 1Þ j ¼ 2 4 ExR 1 ðj 1Þ j ¼ 3 4 ExR 1 ðj 1Þ j ¼ 4 4 ExR
Inverter size
Notation on graph legend
WExAVE (0.25,0.4) WExMAX
AVE (0.25,0.4) Max
(0.5,0.6) WExMAX
(0.5,0.6) Max
(0.75,0.8) WExMAX
(0.75,0.8) Max
WExMAX
Max
To understand the efficiency behavior, it is also useful to plot the efficiency versus electrolyzer specific power. In Fig. 10, system efficiency is plotted as function of specific power. All curves pass through an almost identical point at a specific power of 2 kW per stack, which is the current density limit. After that, all curves shows an optimal value before reaching the 5 kW per stack. After the optimal point, for low inverter size, the efficiency reduces until a certain minimal point, after which a constant efficiency line is observed.
Conclusions In this research, the performance analysis of a solar PV-PEM electrolyzer is carried out. Array and stack sizing effects are evaluated for better understanding and system planning. With the model provided here, a quick and accurate dynamic modeling for the system is presented. The results of dynamic modeling for this work can be listed as follows:
Fig. 9 e Efficiency of the system versus ER and at different inverter sizing.
point efficiency reaches ER values in range of 0.8e0.95. Since efficiency is a function of specific desired power as well as ER, for each ER value there is more than one point in Efficiency curve, corresponding to different specific desired work. This can be interpreted from Fig. 10 and they (Figs. 9 and 10) should be considered together.
The dynamic model developed for the integrated PV system to high pressure PEM electrolyzer provide a method for calculating the amount of hydrogen production for varying operating conditions. As the energy ratio (ER) changes from 0 to 1, the value of efficiency increases up to a point and then decreases. The maximum efficiency is found at ER values in the range of 0.8e0.95. Hydrogen production rate (annually) is sensitive to cell operating temperature and pressure and the sensitivity of hydrogen production as obtained here, is 8%. Efficiency of the overall system in case of relevant inverter sizing is in range of 11e12%. AVE Excess energy is not a suitable inverter sizing move and instead inverter size should be higher than 0.75% of maximum excess power.
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 4 ( 2 0 1 9 ) 9 7 0 1 e9 7 0 7
Acknowledgements The authors would like to thank Hamed Jahed and SOLEOSEnerji Company for providing the hourly demand data for Erzincan University Campus.
Nomenclature Abbreviation AC Alternating current Ave Average CapRatio Capacity ratio CEC California Energy Commission DC Direct current ER Energy ratio Inv Invertor NREL National Renewable Energy Laboratory POA Plan of array PV Photovoltaic SAM System advisor model Sol Solar Variable I I E F R S t V W
Current generated by the PV module, A Cell current density, A cm2 Energy, kj Faraday's constant, 96485 C/mol Universal gas constant, 8.31446 J/mol/K Sensitivity of production rate Thickness, m Voltage generated by the PV module, V Power, kW
Greek variable a Charge transfer coefficient h Efficiency U Ohmic Superscripts/Subscripts act Activation Cap Capacity ex Excess power m Membrane Min Minimum Max Maximum
references
[1] Dincer I. Technical, environmental and exergetic aspects of hydrogen energy systems. Int J Hydrogen Energy 2002;27(3):265e85. [2] Momirlan M, Veziroglu TN. The properties of hydrogen as fuel tomorrow in sustainable energy system for a cleaner planet. Int J Hydrogen Energy 2005;30:795e802.
9707
[3] Ball M, Wietschel M. The future of hydrogen opportunities and challenges. Int J Hydrogen Energy 2009;34:615e27. [4] Acar C, Dincer I. Energy and exergy analyses of a novel photoelectrochemical hydrogen production system. Int J Hydrogen Energy 2017;42:30550e8. [5] Nie J, Chen Y, Boehm RF, Katukota S. A photochemical model of proton exchange water electrolysis for hydrogen production. J Heat Tran 2008;130. 042409-1-042409-6. [6] Oliviera P, Bourasseau C, Bouamam B. Dynamic and multiphysic PEM electrolysis system modelling: a bond graph approach. Int J Hydrogen Energy 2017;42:14872e904. [7] Awasthi A, Scott K, Basu S. Dynamic modeling and simulation of a proton exchange membrane electrolyzer for hydrogen production. Int J Hydrogen Energy 2011;36:14779e86. [8] Marangio F, Santarelli M, Cali M. Theoretical model and experimental analysis of a high pressure PEM water electrolyzer for hydrogen production. Int J Hydrogen Energy 2009;34:1143e58. [9] Zoulias E, Varkaraki E, Lymberopoulos N, Christodoulou CN, Karagiorgis GN. A review on water electrolysis. TCJST 2004;4:41e71. [10] Onda K, Kyakuno T, Hattori K, Ito K. Prediction of production power for high-pressure hydrogen by high-pressure water electrolysis. J Power Sources 2004;132:64e70. [11] Roy A, Watson S, Infield D. Comparison of electrical energy efficiency of atmospheric and high-pressure electrolysers. Int J Hydrogen Energy 2006;31:1964e79. [12] Santarelli M, Medina P, Calı M. Fitting regression model and experimental validation for a high pressure PEM electrolyzer. Int J Hydrogen Energy 2009;34:2519e30. [13] Laoun B. Thermodynamics aspect of high pressure hydrogen production by water electrolysis. Revue des Energies Renouvelables 2007;10:435e44. [14] Bensmann B, Hanke-Rauschenbach R, PenaeArias IK, Sundmacher K. Energetic evaluation of high pressure PEM electrolyzer systems. Electrochim Acta 2013;110:570e80. [15] Schalenbach M, Carmo M, Fritz DL, Mergel J, Stolten D. Pressurized PEM water electrolysis: efficiency and gas crossover. Int J Hydrogen Energy 2013;38:14921e33. [16] Selamet OF, Acar MC, Mat MD, Kaplan Y. Effects of operating parameters on the performance of a high-pressure proton exchange membrane electrolyzer. Int J Energy Res 2013;37:457e67. [17] Aouali FZ, Becherif M, Tabanjat A, Emziane M, Mohammedi K, Krehi S, Khellaf A. Modelling and experimental analysis of a PEM electrolyser powered by a solar photovoltaic panel. Energy Procedia 2014;62:714e22. [18] Sarrias-Mena R, Fernandez-Ramırez LM, Garcı a-Vazquez CA, Jurado F. Electrolyzer models for hydrogen production from wind energy systems. Int J Hydrogen Energy 2015;40:2927e38. [19] Grigoriev SA, Kalinnikov AA, Millet P, Porembsky VI, Fateev VN. Mathematical modeling of high-pressure PEM water electrolysis. J Appl Electrochem 2010;40:921e32. [20] https://sam.nrel.gov/. [21] Barbir F. PEM electrolysis for production of hydrogen from renewable energy sources. Sol Energy 2005;78:661e9. € rgu¨n H. Dynamic modelling of a proton exchange [22] Go membrane (PEM) electrolyser. Int J Hydrogen Energy 2006;31:29e38. [23] Gilman P. SAM photovoltaic model technical reference, 59. National Renewable Energy Laboratory; 2015. NREL/TP-6A2064102.