Infrastructure, automation and model-based operation strategy in a stand-alone hydrolytic solar-hydrogen production unit

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 3 7 ( 2 0 1 2 ) 1 6 5 9 1 e1 6 6 0 3

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Infrastructure, automation and model-based operation strategy in a stand-alone hydrolytic solar-hydrogen production unit Chrysovalantou Ziogou a, Dimitris Ipsakis a, Fotis Stergiopoulos a,b, Simira Papadopoulou a,b, Stella Bezergianni a, Spyros Voutetakis a,* a

Chemical Process Engineering Research Institute, Centre for Research and Technology Hellas, P.O. Box 60361, 57001 Thermi-Thessaloniki, Greece b Department of Automation, Alexander Technological Educational Institute of Thessaloniki, P.O. Box 141, 54700 Thessaloniki, Greece

article info

abstract

Article history:

An autonomous power system that exploits solar energy for the production of hydrogen

Received 22 November 2011

through water electrolysis is fully assessed in terms of system implementation and

Received in revised form

optimal operating strategy. A 10 kWp photovoltaic array supplies energy to a PEM elec-

24 February 2012

trolyzer rated at 6.9 kWp. In order to maintain a smooth operation regardless of the

Accepted 28 February 2012

inherent weather fluctuations, a 1000 Ah/24 V leadeacid accumulator stores energy excess

Available online 30 March 2012

and provides it when needed. The monitoring and control of the system is implemented through a Supervisory Control and Data Acquisition system (SCADA), while the interac-

Keywords:

tions between electrical and chemical subsystems are addressed by a complete automation

Solar-hydrogen production

infrastructure. The mathematical models of each subsystem are validated based on real

Water electrolysis

operational data and a model-based power management strategy is proposed and assessed

Automation and integrated control

through a parameter sensitivity analysis. Further on, an off-line optimization framework is

Stand-alone operation

evaluated regarding the optimal operation of the system in two diverse, but representative

Model-based power management

time periods. The optimal parameters are identified and their effect on hydrogen

strategy

production and accumulator utilization is reported. Copyright ª 2012, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.

1.

Introduction

The excessive consumption of fossil fuels for energy production has led not only to their continuous reserve depletion, but also to the exaggeration of the greenhouse effect, through the accumulation of carbon and nitrous oxides in the atmosphere. One of the most promising solutions that is expected to contribute significantly to the minimization of such harmful effects is the combined use of Renewable Energy Sources (RES) and biofuels towards a sustainable energy future. Several

biomass to biofuels conversion technologies, either in experimental or industrial scale, are envisioned as a prominent immediate solution for mitigating the climate change that is related mainly to the transportation sector. Catalytic hydrotreatment is considered as the most promising conversion technology of fatty acids into biofuels, as it incorporates a network of key reactions that reduce their undesirable oxygen content and saturate the problematic double bonds [1]. However, the operating costs of such a process depend primarily on the hydrogen required for these reactions. In the

* Corresponding author. Tel.: þ30 2310498317; fax: þ30 2310498380. E-mail address: [email protected] (S. Voutetakis). 0360-3199/$ e see front matter Copyright ª 2012, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhydene.2012.02.179

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BIOFUELS-2G project funded by the EU Program LIFEþ, the incorporation of hydrogen originating from solar energy is examined as a technology for reducing the overall production cost and simultaneously improving the associated sustainability of the produced biofuels. Thus, the core aim of the presented study is the overall analysis of an autonomous unit that utilizes solar energy for the production of high pressure hydrogen, which is required for the abovementioned biofuel production process. Special caution is to be provided on the automation and system control aspects. Autonomous power systems utilizing renewable energy sources (RES) have been installed across continents in the past two decades. Meurer et al. [2] presented a holistic approach regarding the operation of an integrated power system with main target the electrification of a library in Germany. Software tools and automation issues were well presented and based on such results, Ulleberg [3,4] developed concrete mathematical models for all subsystems and validated them based on existed experimental data. Another similar system utilizing RES and hydrogen storage was designed and installed at the Hydrogen Research Institute (HRI) in Quebec, Canada [5]. An optimal power management was proposed consisting of two regulatory levels: the lower level regarding the individual subsystem operation (powering on and off) and the supervisory level referring to decisions on power utilization according to the accumulator State-of-Charge (SOC) and the availability of RES. Technical feasibility and financial analysis issues on a stand-alone windePV hybrid system incorporating a hydrogen storage unit was carried out for Cooma in Australia [6], while an interesting approach of an autonomous system utilizing RES was installed in Madrid, Spain to provide energy for remote locations [7]. A rather innovative application and combination of RES along with various hydrogen energy systems, was presented by Hollmuller et al. [8] and provided power for a residential house in Switzerland. This system combines PV arrays with an alkaline electrolyzer, a compressor, metal hydride tanks and a fuel cell powered mini bus. In Greece, the “HELPS” project [9] aims to meet the energy needs in remote telecommunication units, with particular emphasis to be given in the utilization of an electrolyzer and a fuel cell. The aforementioned studies can be enhanced in this paper significantly by addressing new and challenging issues. The main aim is to present the development and progress

made on a newly installed solar-electrolyzer autonomous power system with an accumulator as a short-term storage unit. The stand-alone system is installed at the Laboratory of Process Systems Design and Implementation (PSDI) of CPERI/ CERTH in Thessaloniki. Firstly, the design and development of an automation infrastructure that will take into account the unique operating features of each subsystem addresses issues related to unit integration, power conditioning units (converters, inverters) and software development. Secondly, a validated mathematical model and its utilization in a power management strategy will allow parametric sensitivity analysis and further optimization studies in order to determine optimal operation levels, which can supply feedback for an effective real time operation. The authors’ previous work has contributed to the mathematical modelling and power management development [10,11], as well as, to the design and evaluation of automation and implementation issues [12,13] on a RES-hydrogen power system located at N. Olvio Xanthi, Greece. The paper is organized as follows: Section 2 provides a full description of the autonomous system along with the automation infrastructure that coordinates the solar-hydrogen unit. In Section 3 the mathematical modelling of the autonomous system is briefly presented. Section 4 delves into the development of the power management strategy, followed by a thorough sensitivity analysis on the main operation parameters in Section 5. Finally, the development of an optimal operation framework is discussed in Section 6 along with the framework capabilities.

2. Stand-alone solar hydrogen production system Fig. 1 presents the installed integrated autonomous hydrogen production unit that is designed in order to evaluate the potential of solar exploitation in hydrogen production. The main activities involve the effective implementation of an automation system that lies on the one hand, in monitoring subsystem operation and efficiencies and on the other hand, in applying suitable decisions for their operational control. The autonomous system is housed inside and around a 20ft container and can be divided in two major subsystem that are integrated under an automated monitoring and

Fig. 1 e Integrated solar-hydrogen system installed at PSDI/CERTH.

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control infrastructure: a) the one incorporates electrical equipment and components and b) the second subsystem incorporates electrochemical equipment along with mechanical and chemical components. Fig. 2 presents the subsystem connections.

2.1.

PV 10kWp

Inverter

Electrical and electronic subsystems

Inverter

Inverter L3 L2 L1 N

The main source of energy in the system is a PV-array comprised of 42 polycrystalline panels with a total installed power of around 10 kWp. Based on the recurrent solar radiation, the PV panels provide the power needed to produce hydrogen. However, due to the continuous fluctuation of weather conditions, a short-term storage system is installed in the form of a leadeacid accumulator. Three (3) solar inverters (SMA SB 3300TL) are used to convert the DC into AC power, while in order to minimize the risk of electrical asymmetries, each inverter is connected with the same number of panels (14) at its DC input (Fig. 3). The AC three-phase system is built using three inverters/ chargers (SMA SI 2224). The DC input of the chargers is connected to the lead acid accumulator bank with a total nominal capacity of 1.000 Ah at 24 V. The output of each inverter/ charger forms the first of the three-phase system. Thus, the whole system (PV, accumulator and power conditioners, see also Fig. 2) is built using an AC bus architecture. Furthermore, all power conditioning equipment utilize power-frequency droop control, as a means of regulating the power flow in each device. The accumulator bank is used as an energy buffer, providing energy to the load in case of deficit from solar energy and absorbing excess energy in times of high solar energy yields. The load refers to a 6.9 kWp polymer electrolyte membrane (PEM) electrolyzer system (HyLyzer by Hydrogenics), which is supplied by a variable power supply unit (AC/DC converter).

Due to the inherent moisture of the produced hydrogen and in addition to the dehydrators incorporated inside the electrolyzer unit, two extra dehydration columns of same volume (10 l each) are utilized before storage. Hydrogen is directed initially to the low-pressure storage stage with a volume of 100 l, where pressure is set to vary between 2 and 8 bar. At the high pressure level the compressor increases the pressure towards the second stage to accumulate the produced hydrogen at pressures up to 200 bar into 2e4 common high-pressure bottles of 50 l each. Fig. 4 shows separately the hydrogen production subsystem.

2.2.

2.3.

Electrochemical subsystems

The flow diagram of the electrochemical hydrogen system, as presented in Fig. 4, consists of the electrolyzer that produces the hydrogen and the two-stage compression and storage unit. The necessary water for the operation of the PEM electrolyzer is fed with a pump from the water storage tank of 100 l, while the demand for low resistivity (>2.0 ΜU) is maintained through a deionization unit. The operation of the electrolyzer is monitored by the control system as will be explained later.

Inverter Charger

Inverter Charger

Inverter Charger + Lead Acid Battery 2 x 490Ah 24V

Fig. 3 e Electrical subsystem.

Automation and control infrastructure

The operating challenge is to guarantee that power will be available at a reasonable cost and whenever needed, without PV

AUXILIARY UTILITY LOAD

UPS

AC BAR

Power Supply

WATER

Dehydrator

Deionizer

HY/ZR

2nd Stage

Compressor DRAIN

Fig. 2 e Overview of subsystem connections.

HP

LP

1st Stage

HYDROGEN STORAGE

Fig. 4 e Hydrogen subsystem.

DRAIN

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compromising reliability. In such an extended and multicomponent system, the size of data and information produced is quite large, therefore proper monitoring and supervision should be performed by the automation system. Based on the device specifications and the operating details of each subsystem, an automation system that handles their interconnection and control in a centralized scheme is proposed. Hardware components from different vendors were integrated to create the hardware platform for the system, while software was developed and parameterized on the targeted system. The need for data acquisition and information management along with the heterogeneity of the devices dictated the use of a control system able to provide high level monitoring functions and discrete control. As such, an industrial Supervisory Control and Data Acquisition (SCADA) system was deemed appropriate for the unit. The control strategy and system monitoring is based on the SCADA (iFIX by GE) system, which communicates with all the necessary components in gathering the required input information (the sensors and the final control elements). Various network and industrial protocols were used to integrate the system devices into one central control system, managing in this way the heterogeneity between the chemical and electrical subsystems. The electrolyzer uses CAN protocol, the inverters and the chargers form a RS-485 network among them and use Ethernet to transmit information to the SCADA system, while the Power Supply of the electrolyzer uses RS-232C serial protocol. Finally, the signals from each accumulator cell are transmitted through RS-485. Also, in order to transmit data from/to the I/O field, a Profibus network is used. All these different protocols are translated to an OPC based format due to homogeneity reasons. The schematic representation and the interconnection of the devices are presented in Fig. 5. The input and output signals related to the aforementioned subsystems are presented via a Human Machine Interface (HMI) to the operator. The overall system can be operated in manual (locally or remotely) or in automatic mode and the user can make changes of key variables, such as the power supply level of the PEM electrolyzer, and monitor online the system response. Figs. 6 and 7 show screenshots of the HMI of the integrated system during real time operation. In Fig. 6 the overall electric energy flow is illustrated along with the interconnections of each subsystem to the main AC bus. The flow diagram of the electrochemical subsystem is presented in

Fig. 7 and it consists of the electrolyzer (HY/ZR R-101), the compressor (P-81) and the storage units (V-83 and V-84). Other subunits include the water storage (V-21) and dehydrators (V81, V-82). As can be seen in the presented screenshots, the electrolyzer is in actual operation at 6.1 kW and hydrogen is produced. The aforementioned infrastructure enables the study of the effects that any selected operation strategy has to the system performance and also provides the ability to implement a multitude of flexible control algorithms.

3.

System modelling and validation

The mathematical models of the electrical and electrochemical subsystems are briefly discussed in this section, while a full description can be found in respective references.

3.1.

Photovoltaic system modelling

The mathematical analysis for the operation of a PV-cell relies on the currentevoltage (IeV) relationship that is calculated based on the incident solar radiation and temperature on the surface of the cell. Most mathematical models refer to the equivalent circuit (one-diode model). The relationship between operation current (I ¼ Ipv) and operation voltage (V ¼ Vpv) is given by [4]:     Vpv þ Ipv ,Rs Ipv ¼ IL  IO , exp 1 a IL ¼


 GT  $ IL;ref þ mI;sc $ Tc  Tc;ref GT;ref 

Io ¼ Io;ref

3

    egap $Ns Tc;ref $ 1 $exp aref Tc

(2)

(3)

where Ipv, IL, Io denote the operation current, light current and diode reverse saturation current in A respectively, Rs the series resistance in U, Vpv the operation voltage in V, a the curve fitting parameter in V, aaGT, GT,ref the solar radiation and reference solar radiation in W/m2, Ns the number of cells in series, Tc, Tc,ref the cell temperature and reference temperature in K, Io,ref, IL,ref the diode reverse saturation reference current and short circuit current respectively in A, and egap the band gap of Si in eV. The curve fitting parameter a and circuit resistance is given by [4]: a ¼ aref

aref ¼

Tc Tc;ref

mV;oc $Tc;ref  Voc;ref þ egap$ Ns mI;sc $Tc;ref 3 IL;ref

Rs;ref ¼

Fig. 5 e Control System Architecture.

Tc Tc;ref

(1)


aref $ln 1  Imp;ref =IL;ref  Vmp;ref þ Voc;ref Imp;ref

(4)

(5)

(6)

here mΙ,sc, msV,oc are the temperature coefficients in conditions of constant radiation and Voc,ref the open circuit reference

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16595

Fig. 6 e SCADA- PV Measurements.

voltage and ref indicates the reference conditions of G ¼ 1000 W/m2 and T ¼ 25  C, and Imp,ref, Vmp,ref are the current and voltage at conditions of maximum power point (MPP) in A and V respectively.

Finally, the output power from the PV-array (the maximum pair of currentevoltage measurements) is given by: Ppv ¼ Vpv $Ipv $npv

Fig. 7 e SCADA e Electrochemical subsystem.

(7)

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where Ppv denotes the output power from the PV-array in W and npv denotes the efficiency as a lumped parameter that combines all electrical parasitic losses w90%. The aforementioned model was validated prior to its use in simulation studies for a 69.4 Wp panel [11]. The agreement between the simulated and the experimental data was considered satisfactory and only a minor deviation occurs at low voltages (relative errorw1%).

3.2.

Lead-acid accumulator

The operation of the accumulator is represented as the equivalent to the operation of two tanks that are connected with a regulation valve, so that a liquid can flow in and out of the tanks (Ki-BaM model). One tank represents the charge that is readily available to be used and the other represents the charge that is chemically bound [14]. The equations for the above system are: q1 ¼ q1;0 $ek$t þ




Iac $c$ k$t  1 þ ek$t q0 $k$c  Iac $ 1  ek$t  k k (8)

q2 ¼ q2;0 $ek$t þ q0 $ð1  cÞ$ 1  ek$t
Iac $ð1  cÞ$ k$t  1 þ ek$t  k

(9)

where q1, q2 denote the available and bound charge respectively in Ah, q1,0 and q2,0 the initial available and bound charge respectively in Ah, k, c are manufacturer provided parameters, t is the time in h and Ιac the charging or discharging current in A. The voltageecurrent relation takes the following form [14]: (10)

where Eac denotes the source voltage in V and Ro is the internal resistance in U. The source voltage depends differently on accumulator parameters and their full relation is provided in [10e14]. The SOC of the accumulator refers to the available capacity of the accumulator and is given by a function of time and operation current and is simply the fraction of the reported capacity of the accumulator at each time instant divided by its nominal capacity [10,11]: SOCðt þ 1Þ ¼ SOCðtÞ$ð1  sac Þ þ

Iac $hac $ðDtÞ Qnom

(11)

where hac is the efficiency factor, w95%, Ιac is the charge/ discharge current in Α, sac is the discharging rate of the accumulator w3.5%, Qnom is the nominal accumulator capacity in Ah (¼q1,0 þ q2,0) and t is the time in h.

3.3.

PEM electrolyzer

The PEM electrolyzer VeI characteristic is given by a parametric non-linear equation that takes into account the various phenomena taking place in the electrodes and in the polymer membrane [4]:

(12)

where Velec and Vrev,elec denote the operation cell voltage and reversible voltage respectively in V, ri are parameters for the ohmic resistance of the electrolyte, si and ti are parameters for the overvoltages that occur in the electrodes, Aelec is the electrode area in m2, Telec is the temperature of electrolyte in  C and Ielec is the current through the cell in A. The parameters ri, si and ti can be estimated by experimental data of voltageecurrent measurements for constant temperatures.

3.4.

Hydrogen storage

Storage under pressure requires the use of a compressor in order to increase the hydrogen pressure to the storage tanks pressure. The basic mathematical model (Van der Waals Law) that describes the storage of hydrogen is described by the following equation: PT ¼




Vac ¼ Eac  Iac ,Ro


r1 þ r2 $Telec Velec ¼ Vrev;elec þ $Ielec þ s1 þ s2 $Telec þ s3 $T2elec Aelec   t1 þ t2 =Telec þ t3 =T2elec $Ielec þ 1  log Aelec

n$R$Tstor n2  a$ 2 VT  n$b VT

(13)

where PT denotes the tank pressure in bar, n is the number of stored moles of hydrogen, R is the universal gas constant in bar m3/mol∙K, Tstor is the storage temperature in K, VT is tank volume in m3, a,b are parameters depending on hydrogen critical temperature and pressure in bar and lt2/mol2, respectively. The polytropic work (DWpol) for the compression of hydrogen is: DWpol ¼

k $nH2 ;comp $R$T1 $ k1

 k1  P2 k 1 ncomp P1

(14)

where DWpol is the compressor power in W, nH2 ,comp is the hydrogen flow at the compressor in mol/s, T1 the inlet hydrogen temperature in K, P1 and P2 the inlet and outlet hydrogen pressure, k the polytropic coefficient of hydrogen (1.609) and nH2 ,comp the compressor efficiency. In the aforementioned model the value of the parameters provided by the system manufacturer is illustrated in Table 1. A parameter estimation procedure was conducted in order to determine the value of the parameters which are specific to the system into consideration (Table 2). Both the estimation and the validation of the models were performed against experimental data derived from the system operation.

3.5.

Comparison of simulated and experimental data

A typical current profile at the accumulator is given in Fig. 8 for a two-day operation. This profile arises from the power production of the photovoltaic system in the respective period, as depicted in Fig. 9. As can be seen, the photovoltaics provide enough power to charge the accumulator, while the current profile follows almost identically the power production pattern. After a significant period (sunset), the photovoltaics reduce the power production (zero production) and the

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250

Table 1 e Parameters from manufacturer data. Value

Photovoltaic array GT,ref Tc,ref Ns IL,ref Voc,ref mΙ,sc mV,oc Imp,ref Vmp,ref PEM electrolyzer Aelec ncells

1000 W/m2 25 oC 36 4.7 A 21.4 V 2.06e3 A/K 0.077 V/K 4.25 A 16.5 V

200

Current, A

Parameter

150 100 50 0 -50

0.3 m2 14

0

500

1000

1500

2000

2500

3000

Time, min Fig. 8 e Accumulator operation current for two days. accumulator is forced to discharge in order to meet system electrical needs (power conditioning units, utilities etc.). Based on this current profile a comparison of the simulated results with the recorded experimental data on the accumulator voltage is shown in Fig. 10. As expected, the voltage increases during charging and decreases in the discharging mode, with the overall pattern to follow the current variations. The respective comparison on the SOC of the accumulator is shown in Fig. 11. During the charging mode, the SOC increases, up to 90.5% of nominal capacity, while is lowered to 46.8% during discharging. In both figures, the mathematical model response is in good agreement with the experimental data and the average absolute error is calculated at 0.8% for the voltage calculations and 0.32% for the SOC. Thus, the mathematical model of the leadeacid accumulator, as developed in [4] and further utilized in previous studies [10e13], is considered as an excellent choice for any further theoretical studies regarding optimization analysis and control issues. The electrolyzer operates in a constant maximum power mode, which derives from the constant current and voltage that are given to the power supply as set values (230 A, 29 V). However, due to auxiliary losses, the actual current that the electrolyzer consumes is nearly 220 A as shown in Fig. 12. Since the manufacturer specifications set this current limit as the preferable one for an effective operation, only small variations to this current value were taken into account so as to be able to validate the resulting VeI curve. The simulated curve fits in an acceptable manner the experimental data at an

operation temperature range of 30e35  C. Based on the operation of the electrolyzer, a significant hydrogen amount is being produced and stored under pressure in cylinders. Fig. 13 shows the hydrogen pressure in the tanks. The 50 lt buffer storage unit (one of the two bottle tanks) has an upper bound pressure at 8 bar. When the buffer pressure reaches that limit, the compressor operates so as to deliver the stored hydrogen at the final storage tanks. The final storage tank upper pressure is set at 200 bar. According to the Faraday’s Law, the ratio of the actual produced hydrogen to the theoretical expected (Faraday’s law) is shown as a function of time in Fig. 14. In the same figure, the respective values for the hydrogen production are also shown. The actual produced hydrogen is calculated based on the measurement of buffer pressure as it increases with the electrolyzer operation, according to Eq. (13). The theoretical produced hydrogen is calculated based on the Faraday’s Law and the current draw from the electrolyzer [4,11]. As can be inferred, the efficiency varies from 40% to 80% depending probably on the variation of temperature, which is internally controlled, and the higher efficiency is be recorded at I ¼ 222.1 A and V ¼ 28.6 V. Obviously, the electrolyzer unit

8000 7000

Table 2 e Estimated parameters by experimental data. Parameter

Value

Leadeacid accumulator k 2.55 c 0.1067 0.9 e3 U/cell Ro PEM electrolyzer 0.0067 V m2/A r1 0 V m2/A K r2 0.153 m2/A t1 0 m2 K/A t2

Power, W

6000 5000 4000 3000 2000 1000 t3 s1 s2 s3

0 m2 K2/A 1.36 V 0 V/K 0 V/K2

0 0

500

1000

1500

2000

2500

Time, min Fig. 9 e PV Power production for two days.

3000

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30

100 Exp. Sim.

Exp. Sim.

90

28

80

SOC, %

Voltage, Volt

29

27 26

70 60

25

50 24

40 0

500

1000

1500

2000

2500

3000

0

500

Time, min

1000

1500

2000

2500

3000

Time, min

Fig. 10 e Accumulator operation voltage for two days.

Fig. 11 e Accumulator SOC for two days.

needs to operate for a longer period and for different power levels, so as to create a reliable overall power-efficiency curve. This curve will be used as a reference during the full operation of the solar-hydrogen autonomous system.

hydrogen production, the accumulator utilizes the available power for charging. - SOCmax < SOC < SOCmax_charge: Electrolyzer and accumulator operate under the conditions: a) Ppv < Pmin,elec: the differential power, Ppv e Pmin,elec, is provided by the accumulator to the electrolyzer for an operation at its minimum power level. b) Ppv > Pmax,ele.c: The electrolyzer utilizes power equal to Pmax,elec and the extra power Ppv e Pmax,elec is used to charge the accumulator without exceeding the upper limit of SOCmax_charge, c) Pmin,elec  P  Pmax,elec: PV fully support the operation of the electrolyzer. - SOC  SOCmax_charge: The available power is supplied to the electrolysis unit provided that is within the permissible operating limits. If the output power by the PV panels is greater than Pmax,elec then the electrolysis unit utilizes its maximum availability and the excess is discarded. If the available power is lower than Pmin elec, then the accumulator is discharged by providing the necessary power for operating at a minimum threshold. The aforementioned algorithm provides the basis in conjunction with the

Operation algorithm

The operation algorithm of the integrated solar-hydrogen system includes a set of decisions in various levels. There are two main operation modes that pose certain actions to be performed according to the power produced by the PV panels, Ppv. Ppv > maximum operation power of electrolysis1 Pmax,elec - Ppv < minimum operation power of electrolysis, Pmin, elec -

All operational decisions are taken on the basis of one decision variable/operation mode that is the state-of-charge (SOC) of the accumulator. This variable can be estimated (indirectly measured) through voltage measurements in combination with the accumulator’s capacity, health condition and the inputeoutput current of the accumulator. Fig. 15 shows all the decisions implemented in the system, followed by their justification. As can be seen, specific values(levels) of the accumulator SOC (namely SOCelec, SOCmax, SOCmax_charge) are indicated, according to which all operational decisions of the hydrogen production system are taken. SOC < SOCelec: PV fully charges the accumulator and no electrolyzer operation is allowed. - SOCelec  SOC  SOCmax: If in the previous time step hydrogen was in production, then the electrolyzer does not shut down and operates until the SOCelec limit, enhanced by the accumulator when necessary (see next concept for electrolyzer-accumulator operation patterns). In case of no -

1

The maximum and minimum power of the electrolyzer refers to the allowable operation limits by the manufacturers. Usually the maximum is equal to the nominal operation power and the minimum as a percentage (w25e30%) of it.

30.0

29.7

Voltage, Volt

4.

Sim. Exp.

29.4

29.1

28.8

28.5 217

218

219

220

221

Current, A Fig. 12 e Electrolyzer’s voltageecurrent curve.

222

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7

Table 3 e Average values for solar radiation and temperature for each time period during one year [15].

Tank Pressure, bar

6 2

G (W/m ) T ( C)

5

4

3

0

2

4

6

8

10

12

14

16

Time, min Fig. 13 e Hydrogen pressure at the buffer storage tank.

1.0

40 Actual Hydrogen Theoretical Hydrogen Efficiency

35

0.8

30

0.6

20 0.4

15 10

Efficiency

25

0.2

5 0

0.0 0

2

4

6

8

10

12

14

JaneApr

MayeAug

SepeDec

136.8 13.7

275.6 29.6

112.7 22.2

parameters on the hydrogen production and the overall operation of the system. For this reason, regional weather data necessary for simulation are considered for a one year period of time. The size of the time intervals is considered to be 1 h and is suitable for the simulated representation of the solar energy variations. The average values for the solar radiation intensity (G, W/m2) and air temperature (T,  C)) for time period of four months are presented in Table 3 for the city of Thessaloniki [15]. The values of various parameters and initial values used in the sensitivity analysis are shown in Table 4. The first case study refers to the analysis of the system sensitivity to the variations of SOCelec limit. As similarly presented in previous studies [10,11], the minimum SOC limit is a key parameter for the operation of the accumulator because it defines its lifetime expectancy. The Depth of Discharge (DOD) is defined as the difference between the maximum and the minimum recorded operation limit of the SOC of the accumulator. The operation parameter that determines the accumulator utilization and relies on the DOD value is the number of operation cycles (Ncycles) that it undergoes. The operation cycle is defined as the process where a discharging/ charging mode is followed by a charging (or discharging) mode. Ncycles was found to conform to the following empirical relation where lower values for this operation parameter indicate higher potential lifetime for the accumulator and thus the lifetime depends on the minimum SOC limit [10,11,16]:

2

Hydrogen Production, lt/min

16599

16

Time, min Fig. 14 e Expected and actual hydrogen produced and respective efficiency.

Ncycles ¼ a$lnðDODÞ þ b

where a and b are experimental estimated parameters adjusted for each system (a ¼ 1202, b ¼ 13384). Fig. 16 presents the total energy involved at each subsystem as a function of SOC. As shown, the total energy provided for hydrogen production is slightly reduced when SOC is lowered, and for this reason the compressor total operation energy remains practically steady for the three cases. Further on, the accumulator charging and discharging energy is increased during the variation of the SOCelec limit, while the dump energy is favoured by reducing the

mathematical models for the simulation of the system behaviour during the development of the optimization framework.

5.

Parametric sensitivity analysis

A parametric sensitivity analysis has been performed to investigate the effect of important design and operation SOCelec ELECTROLYZER Status

OFF

LEAD-ACID CHARGE ACCUMULATOR Status

(15)

SOCmax_charge

SOCmax ON

ON

CHARGE/DISCHARGE (Based on the Power Excess/ Shortage )

CHARGE/DISCHARGE (Based on the Power Excess/ Shortage )

HYSTERESIS BAND EL/ZER OPERATION

HYSTERESIS BAND ACCUMULATOR USE

ON

NO FURTHER CHARGE ALLOWED

Fig. 15 e Operation algorithm of the solar-hydrogen system (- - - : operating based on the algorithm: always valid).

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accumulator operation limit and is nearly around 1e2% of the total energy production from the photovoltaic system. Table 5 presents the operation cycles (Op. Cycles %) and the total hydrogen stored at the end of the simulation time. It is apparent that as the SOCelec limit is decreased, the accumulator is forced to an operating pattern that is adverse to its lifetime due to increased operation cycles resulting from higher DOD. Particularly for a 20% decrease in the SOCelec limit, the operation cycles face a 97% increase, which should be taken into account during the operation design of the autonomous system, since such high values can reduce the expected lifetime of the accumulator. As far as the hydrogen production is of concern, the stored hydrogen keeps a reduced, but almost steady value (as a consequence of the energy consumed in the electrolyzer), which is equivalent to about 80e85 tanks with nominal capacity of 50 l/200 bar. Thus, from the overall analysis it is concluded that lower SOCelec limits force the accumulator to increased operation that will ultimately lead to its early replacement yielding increased operation and maintenance cost. Moreover, the hydrogen production is not favoured by the lower SOCelec limits and therefore it seems that operating the autonomous system at very low SOC could only cause worse operating patterns for the sensitive subsystems. The second case study refers to the analysis of the system sensitivity to variations of the minimum electrolyzer power limit, Pelec,min. The analysis, shown in Fig. 17, presents the operation energy for each involved subsystem for four different values of Pelec,min limit. As the Pelec, min limit is increased, the accumulator charging and discharging energy are increased, as it was expected by the continuous increase in the default energy demands of the electrolyzer. The electrolyzer input energy remains practically stable, despite the different operating pattern of the accumulator, while only a small increase is detected when the electrolyzer operates at its nominal power supply. The compressor electrical needs follow the general pattern observed for the electrolyzer, but the energy losses show a decreasing trend. The most important table however to conclude to safe results, is Table 6. As can be seen, as the Pelec, min limit increases, the accumulator is forced to an operating pattern

Table 4 e Parameters and initial values used in the simulation case studies (default values). Parameter SOCmax (%) Initial accumulator capacity (Ah) Minimum operation power for electrolyzer, Pmin, elec (kW) Maximum operation power for electrolyzer, Pmax, elec (kW) Initial hydrogen inventory (Nm3) Minimum storage pressure (barg) Maximum storage pressure (barg) SOCnom (the accumulator SOC when fully charged) SOCmax_charge SOCelec_min

Value 90 870 (SOC ¼ 87%) 1.75

SOCelec=70%

SOCelec=60%

SOCelec=50%

12000 10000

Energy (kWh)

16600

8000 6000 4000 2000 0 Accumulator Accumulator Electrolyzer Compressor Discharge Charge

Dump

Fig. 16 e Subsystem operation energy during the reduction of SOCelec limit.

that is also adverse to its lifetime due to increased operation cycles and operating the electrolyzer at its nominal operation level can cause serious reduction at the accumulator lifetime. The stored hydrogen keeps a steady value, which is equivalent to about 85 tanks. Thus, it is concluded that higher values for the minimum Pelec, min stresses the operation of the accumulator with no significant improvement in the hydrogen production. The above analysis provides valuable insights regarding the interaction of the subsystems and the selection of the main parameters to be optimized, which are taken into consideration during the development of the optimal operation framework.

6.

Development of the optimal framework

The system was initially designed to operate based on heuristics regarding numerous technical and operational criteria and specifications available at that time. Therefore, despite the capability of the developed stand-alone unit to produce the necessary hydrogen, its operation is not considered optimal. The proposed optimization framework aims to enhance its performance and more important will dictate the operation of the subsystems in terms of reliability and increase the subsystems’ life expectancy. The utilization pattern for the accumulator and the electrolyzer determines the frequency of equipment replacement and maintenance requirements and therefore the operating costs, over the lifespan of the overall system. To this end, the optimization problem focuses on these operation parameters, which are considered as decision variables for the optimization problem.

6.9 0.2 7 200 (at tanks)/7 (at buffer) 100% SOCmax þ 6% SOCnom SOCmax  30% SOCnom

Table 5 e Case1: decrease of SOCelec: accumulator operation cycles and hydrogen production.

SOCelec ¼ 70% SOCelec ¼ 60% SOCelec ¼ 50%

Op. cycles, %

H2, produced, Nm3

11.01 13.89 21.67

1705.9 1688.4 1670.7

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Pmin=25%

Pmin=50%

Pmin=75%

16601

Pmin=100%

12000

Energy (kWh)

10000 8000 6000 4000 2000 0 Accumulator Accumulator Electrolyzer Compressor Charge Discharge

Dump

Fig. 17 e Subsystem operation energy during the reduction of Pelec, min n limit.

Fig. 18 provides the details of the proposed systematic approach divided into 5 consecutive steps. The employed framework is mainly based on engineering experience, concrete mathematical background and a rigorous optimization methodology, which results in a feasible and functional operation that can enhance the performance of the system while in operation.

6.1.

Optimization problem formulation

The consistency and effectiveness of the optimization framework strongly depends on the ability to predict the effect that the operation decisions have on the achieved performance of the stand-alone hydrogen production system. Therefore, proper selection of optimization decision variables (parameters) and optimization output variables (cost functions) is considered essential for the reliability of the optimization-based procedure.

6.1.1.

Objective and optimization parameters

Two important factors of the optimization procedure are the selection of the optimization parameters as well as the determination of the domains of their values, which will be used to explore the behaviour of the system. The main purpose of the stand-alone system is to produce hydrogen and hence, maximum production is one of the major optimization concerns. Also, the infrastructure of the system should be protected, and more specifically that of the accumulators, since their lifetime is strongly affected by misuse. Each system objective is associated with an optimization

Table 6 e Case2: decrease of Pelec, min: accumulator operation cycles and hydrogen production.

Pmin, Pmin, Pmin, Pmin,

¼ ¼ elec ¼ elec ¼ elec elec

25% of Pnominal 50% of Pnominal 75% of Pnominal 100% of Pnominal

Fig. 18 e Systematic approach towards optimal operation of RES-integrated systems.

parameter. As indicated by the sensitivity analysis, the production of hydrogen slightly depends on the minimum power of the electrolyzer (Pelec,min), therefore it is selected as a decision variable. The SOC is used as the key variable for the overall system ability to maintain the satisfaction of the power demand specification. More specifically the minimum level of SOC where the electrolyzer operation stops (SOCelec) is used as the second decision variable for the optimization problem. The objectives and the variables are summarized in Table 7.

6.1.2.

Optimization method

Based on the above requirements and specifications, a bounded medium-scaled optimization problem is formulated, utilizing non-linear system models. The proposed framework utilizes a deterministic optimization method because the subsystems are described by deterministic, relatively simple, non-linear, well-defined and experimentally validated models. The feasible area that the optimizer will explore is determined by the variables’ bounds and the operational constraints of each subsystem. The problem initialization was easily handled due to the nature of the subsystems. It is straightforward and the initial values are provided by the actual system currently operating. The search space is defined by the specification of each subsystem, as provided by the device manufacturers and the physical boundaries of the system. The generic problem formulation is:

Table 7 e Optimization objectives and decision variables.

Op. Cycles, %

H2, produced, Nm3

Objective

Decision variables

Output variable

11.01 22.43 36.84 61.72

1705.9 1702.1 1705.3 1718.7

1. Maximize hydrogen production 2. Protect accumulators lifetime

Pelec, min (W)

Produced hydrogen (lt) Operation cycles (%)

SOCelec (%)

16602

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Table 8 e Optimization results. Objective

Pelec,

Maximize H2 and protect accumulators lifetime

minf ðx; d; pÞ

January July

min

3125 2091

(16)

Subject to : hðx; dÞ ¼ 0

(16a)

gðx; dÞ  0

(16b)

xL  x  xU ;

dL  d  dU

(16c)

where f is the optimization function, x are the continuous variables, d are the discrete variables, p are the parameters, h is the set of equality constraints and g is the set of inequality constraints. Finally Eq. (16c) denotes the constrained nature of the variables.

6.2.

Optimization results

The optimization was performed separately for two months (January and July, 2011), which represented the worst and the best-case scenarios regarding the weather conditions. In this multivariable case, each term in the objective function is normalized and has equal weight contribution to the objective. The results of each case are presented in Table 8 and it is evident that the system operation parameters should be adjusted for each month, since there is a large variability of weather conditions. The next step at the optimization framework is to implement the optimization findings to the system and analyze the results based on the experimental behaviour of the unit.

7.

Conclusion

An autonomous power system that exploits solar energy for the production and storage of hydrogen incorporating a PEM electrolyzer was assessed in this study. The automation system ensures a safe and efficient system operation, while also allows for the monitoring of the individual subsystem operation. A rigorous mathematical model was used and validated via available experimental data. For all subsystems, a quite satisfactory deviation (less than 1%) was recorded between actual and predicted values, ensuring the validity of the model for the subsequent optimization and design studies. A power management strategy was developed and proposed according to the accumulator SOC and available power from the PV-array. All this progress was utilized in an optimal framework, which assessed the system ability to follow specific design targets by a combination of maximum hydrogen production with minimal accumulator utilization. As it was found, the integrated system is sensitive to parameters such as the minimum operation limits of the electrolyzer and accumulator. These parameters were taken into account as decision variables in off-line optimization studies prior to the real operation of the system in order to identify that

(W)

H2 (lt)

SOCelec (%)

Cycles (%)

55,807 232,480

62 49

1.0 2.86

system operating limits should be adjusted continuously in order to ensure prolonged system lifetime. Future development aims to the utilization of a dynamic mathematical model during the continuous system operation that will be used in advanced model-based control regarding the real time system operation. Furthermore, as the proposed technology allows the production of renewable hydrogen in w200 bar, the overall system can be integrated with the catalytic hydrotreatment process for biofuels production, rendering the overall technology more sustainable and more energy efficient.

Acknowledgements This work is co-financed by the European LIFEþ Program (LIFE08 ENV/GR/000569). For more information please visit the web site www.biofuels2g.gr.

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