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Development of low concentrated solar photovoltaic system with lead acid battery as storage device
Journal Pre-proof Development of low concentrated solar photovoltaic system with lead acid battery as storage device Salah Ud-Din Khan, Zeyad Ammar Almutairi, Shahab Ud-Din Khan, Omer Salah AlZaid PII:
S1567-1739(20)30030-4
DOI:
https://doi.org/10.1016/j.cap.2020.02.005
Reference:
CAP 5150
To appear in:
Current Applied Physics
Received Date: 30 October 2019 Revised Date:
30 December 2019
Accepted Date: 4 February 2020
Please cite this article as: S. Ud-Din Khan, Z.A. Almutairi, S. Ud-Din Khan, O.S. Al-Zaid, Development of low concentrated solar photovoltaic system with lead acid battery as storage device, Current Applied Physics (2020), doi: https://doi.org/10.1016/j.cap.2020.02.005. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier B.V. on behalf of Korean Physical Society.
Development of low concentrated solar photovoltaic system with lead acid battery as storage device Salah Ud-Din Khana*, Zeyad Ammar Almutairia,b,c, Shahab Ud-Din Khand, Omer Salah Al-Zaida a
b
Sustainable Energy Technologies Center, College of Engineering, King Saud University, PO-Box 800, Riyadh 11421, Saudi Arabia
Mechanical Engineering Department, College of Engineering, King Saud University, PO-Box 800, Riyadh 11421, Saudi Arabia c
d
King Abdullah Institute of Nanotechnology, King Saud University, Riyadh, Saudi Arabia
National Tokamak Fusion Program, Pakistan Atomic Energy Commission, Islamabad, Pakistan
*Corresponding author’s email: [email protected] Abstract: Energy storage system powered by renewable energies is a viable option to meet energy requirement without addition of carbon footprints to the environment. This study involves development of theoretical and computational models for a solar photovoltaic (PV) system coupled with a lead acid battery. The study commenced with selection of most appropriate lead acid battery and PV system for installation in a representative location in Riyadh, Kingdom of Saudi Arabia. Various technical and economic parameters were assessed and calculated by computational approach. The optimized lead acid battery was integrated with low concentration solar PV panels (CPV) followed by a feasibility study. Theoretical model was developed for the integrated system to calculate various parameters of the CPV and lead acid battery. Technical and economic assessment of this coupled unit was calculated using a theoretical approach. The developed model was then subjected to computational approach for verification and validation analysis of the integrated system. The detailed assessment of batteries and integrated system show the applicability of this system in Riyadh region. The research will be extended to develop energy storage systems for remote areas using lead acid batteries. Key words: Energy storage; Concentrated photovoltaic; Feasibility study; Technical and economic assessment; Theoretical and simulation; Integrated system. Nomenclature: CPV CSP VRLA AGM F Z
GF Pb
Concentrated photovoltaic Concentrated solar power Valve regulated lead acid batteries Absorbent glass matt Faraday’s constant Number of charges in a reaction Gibbs free energy Lead (symbol)
MTSE E CFD DC/AC SAM h Cp KWh
Isc
Maximum theoretical specific energy Average cell voltage Computational fluid dynamics Direct current/Alternative current System advisor model heat transfer coefficient Specific heat transfer Kilowatt hours Short circuit current
Copt
Optical concentration
Voc
Output voltage
Pmax
Maximum power output
MPPT
Maximum peak power tracking
Introduction: Lead acid battery is the oldest and most inexpensive storage device among all rechargeable batteries. This type of battery is normally used when other batteries cannot provide higher energy density. Due to its ability to supply high surge current, lead acid battery has low energy to weight ratio, energy to volume ratio, and power to weight ratio. These impressive features make this type of battery a viable option for motor vehicles, which need higher current for motor starter. Larger batteries are mostly used for storing energy as backup power systems in cell phone towers, heavy power systems, and hospitals. Designs of these batteries were modified to improve storage time and reduce maintenance. These batteries are collectively called as valve regulated lead acid batteries (VRLA). In these designs, significant improvements have been made in the development of sealed cells. Lead acid cells can be used for two types of applications with different input requirements. Cells in the first type are fully charged to maintain constant voltage profile. They are called flooded or stationary, which have long life, good cycling efficiency, high reliability, and low lost ratio under overcharging. This type is mostly used in signaling systems, telecommunications, and heavy stand-by systems [1]. The second type is used in applications that may require deeper discharging such as in traction applications or in load leveling systems. Periodic trends in charging, rather than continuous, are more commonly supported by low cost and cyclic efficiency. The circuit voltage of lead acid battery can be determined from the Gibbs free energy formula as shown in Eq. 1.
∆GF (PbO2 ) = −zEF
(1)
where F is Faraday’s constant and z is number of charges in a reaction. Also, the maximum theoretical specific energy for lead acid batteries can be expressed as shown in Eq. 2.
MTSE = 26805(
xE ) w
(2)
where ‘x’ is number of elementary charges, ‘E’ is average cell voltage, and ‘W’ is sum of atomic weights. The two main types of VRLA batteries discussed above are named as gel cells and absorbed glass mat (AGM). Some features of these batteries are given in Table 1. Table 1. Main parameters of various types of lead acid batteries Parameter Energy density (Wh/L) Specific energy (Wh/kg) Energy consumer price ( Wh/$) Specific power (W/kg) Cycle durability (typically 500 cycles) Self-discharge rate (%/month) Cell voltage (nominal) (V) Cutoff voltage per cell (for gel) (V) Cutoff voltage per cell (for flooded) (V) Cutoff voltage per cell (for AGM) (V) Cutoff voltage per cell (Loaded) (V/cell) Charging temperature (oC)
Value 60 - 75 30 - 40 7 - 18 180 ~ 800 3-20 2.1 2.23 2.32 2.25 1.75 -40 - 49
The effective way to increase the output from PV system is to use mirrors or lenses (optical light collectors). In this process, photovoltaic (PV) systems supply concentrated light onto the PV cells; hence, these systems are called concentrated photovoltaic (CPV). The main function of this system is to commute the light from a larger surface area to the small area of a solar cell. This CPV system works only with direct beam radiation but not with diffuse radiation. This system is applicable in areas where there is high direct normal irradiance. To obtain higher concentration for higher cell performance, sun tracking can be used. There are three types of CPV system based on the material used in cells, namely, low, medium, and higher concentration (Table 2). Table 2. Types of CPV [2] Ratio of concentration Material used Sun tracking Required cooling
High >100 - 400 Cells with multifunction x-y, y-z, z-x (2D) Active
Medium 10 - 100 Si, Cd, Te x, y, z (1D) Passive
Low 2 - 10 Si No need No need
It should be noted that approximately 30% of field efficiency can be achieved using multi-junction cells [3]; however, they require more sophisticated tracking and cooling systems, which result in higher energy cost. Using small size solar cells to concentrate light results in lower material cost. In addition, mirrors (optics) used in CPV systems are made of glass, which are normally less expensive. The system significantly increases the temperature on the PV surface; however, this energy needs to be homogenously distributed to avoid hot spotting on the surface, which could lead to material damage. Moreover, the thermodynamic efficiency of CPV system at elevated temperatures is low, thus, there is a need to use passive cooling as listed in Table 2. The lowest levelized cost of electricity and the highest
energy density that matches the peak load demand even best in hot climate regions such as in MiddleEast region make CPV technology as best candidate for renewable energy generation and storage. According to the Kingdom of Saudi Arabia’s metrological parameters and despites of many limitations for installing PV system with batteries, it is very important to conduct a feasibility studies of the solar PV system integrated with batteries. In most part of the Kingdom, the climate conditions are somehow hard thus affecting the performance of the batteries. In this way, theoretical-computational model development can provides the pre-assessment of the system. In the current research, the main objective is develop such coupling mode between solar PV system (low power) and lead acid battery as workable solution for energy storage. An extensive review on the various mitigating factors for the use of lead acid batteries in hybrid energy systems was carried out by Patrick T. et al [4]. A comprehensive assessment of PV battery systems based on household load with comparable annual demand was considered as the best estimate for one-month test period [5]. Thermal energy storage and battery storage capacities are theoretically explained based on the economic assessment of hybrid concentrated solar power (CSP) and PV plant from a case study in Chile, which found the capacity factor to be above 90% with levelized costs between 110 to 113 U$/MWh [6]. The suitability of batteries coupled with PV systems for ordinary residence has been tested under various configurations and operational methods [7]. A computational fluid dynamics (CFD) model of lead acid batteries proves that numerical procedures are more efficient and easier to implement compared with conventional models for predicting battery dynamics and other factors [8]. A theoretical approach for PV coupled batteries, which are commercially viable due to self-consumption benefits and which need to be optimally sized to have large peak shaving potential [9]. A theoretical consideration in combining load following strategy with cyclic charging strategy for hybrid PV battery energy systems can give accurate assessment on both the operation and energy management optimization of hybrid systems [10]. Mathematical and experimental investigations on charging and discharging of lead acid batteries at various currents and temperatures for larger range of operating conditions allow to set values for each parameter [11]. The prime objective of study is to investigate energy storage system powered by CPV system. The study is based on developing model of integrated lead acid battery and CPV system most suitable for the Riyadh region. Among number of studies already conducted for lead acid batteries and CPV system but no study defines theoretically proven stand-alone integrated CPV system. In this study, theoretical approach of developing such system was considered. Technical specifications of all types lead acid batteries were simulated and compared in terms of various parameters. The most optimized battery was selected for the integrated system. Different types of CPV systems were reviewed for the most optimal candidate for energy storage system. Based on the optimized lead acid battery and feasibility study of CPV system, a technical and economical assessment model was developed. The results obtained pointed towards the development of working prototype in Riyadh. Research Methodology: The three main objectives of the research are as follows. 1. Computational assessment of various types of lead acid battery; 2. Feasibility studies of low concentration solar PV; 3. Computational assessment of low CPV with lead acid batteries. 1. Computational assessment of various types of lead acid battery
The types of batteries explained above have been computationally analyzed for both technical and economic as presented in Table 3. The main objective was to obtain the most optimized battery applicable for any renewable energy powered configuration. The technical assessment of the batteries showed that cost increases with the number of features loaded in a lead acid battery. The analyses of levelized electric cost, performance ratio, net savings plan, energy yield, energy production, and debt ratio showed that a customized type of lead acid battery has to be considered. This also confirms that the most common lead acid battery can be the optimized solution for energy storage in some applications. However, for advanced and higher energy applications, there are many other factors involved, where other types of lead acid battery could be the appropriate solution. Simulation results showed that flooded type battery can be a good alternative to valve regulated batteries (VRLA), particularly in terms of levelized energy cost and performance ratio. Table 3. Comparison of various types of lead acid batteries Battery parameters Metric Annual energy (year 1) (kWh) Capacity factor (year 1) (%) Energy yield (year 1) kWh/kW Performance ratio (year 1) Battery efficiency (incl. converter + ancillary) (%) Levelized COE (nominal) ¢/kWh Levelized COE (real) ¢/kWh Electricity bill without system (year 1) ($) Electricity bill with system (year 1) ($) Net savings with system (year 1) ($) Net present value ($) Simple payback period Discounted payback period Net capital cost ($) Equity ($) Debt ($)
Customized
Flooded
VRLA(AGM) Value 36,672 8.40 734 0.35
VRLA Gel
36,672 11.00 966 0.46
36,672 14.10 1,238 0.59
100.00
100.00
100.00
100.00
716431.50 414705.50
81152504.00 63829612.00
81152568.00 63829668.00
81106600.00 63793508.00
3,242
3,468
3,468
3,468
5,231
6,739
6,739
6,743
5,272 313,987,661,824 806,130,024,448 0 604,110,023,742
3,272 313,989,038,080 806,130,024,448 0 806,130,024,448
3,272 313,989,070,848 806,130,089,984 0 806,130,089,984
3,275 313,989,070,848 806,130,089,984 0 806,130,089,984
36,693 8.40 734 0.35
The battery was configured based on the desired bank size including its capacity and power fields, which were calculated prior to conversion and other detrimental losses. In this case, the DC/AC ratio conversion efficiency was used to manage and scale the battery size. In the proposed battery configuration, the desired bank capacity was 10 kWh with actual bank capacity of 5 kWh. The number of cells in series were three, while the maximum rates of charge and discharge were taken as constant at
0.5 per hour. System Advisor Module (SAM) measured various bank properties used for simulation purposes. The nominal value for bank voltage was the product of number of cells in series and nominal voltage, while the nominal voltage was the product of bank voltage and capacity of cells. The c-rate denotes the charge or discharge capacity of a battery per hour. The maximum value for power was calculated from the maximum discharge c-rate. Some of the properties of lead acid batteries are presented in Table 4. Table 4. Main parameters of customized lead acid batteries Parameters Nominal bank voltage Nominal bank capacity Maximum charge power Maximum discharge power Time for maximum power Total number of cells Number of cells in series Maximum charge current Maximum discharge current Maximum charge c-rate Maximum discharge c-rate
Values 500.4 V (DC) 10.1331 kWh (DC) 5.06655 kW (DC) 5.06655 kW (DC) 2.0 h 1251 139 10.125 A 10.125 A 0.5 /h 0.5 /h
An important feature of SAM is that if the input is higher compared with the desired grid power, it charges the battery if electric load is less than the target and discharges if electric load is greater than the target. When connecting the battery to any photovoltaic panel (PV or CPV), it should be noted that the battery must be connected to the DC side of PV or CPV system; however, AC to DC conversion efficiency is adjustable. In addition, initial and minimum/maximum values of state-of-charge, as well as minimum rate of charge state, was adjusted according to the requirement. The cycle degradation and battery lifetime are listed in Table 5. Table 5. Battery cycle degradation of lead acid battery Cycles elapsed 0 5000 10000 0 1000 2000
Depth-of-discharged 20 20 20 80 80 80
Capacity (%) 100 80 60 100 80 60
Moreover, the efficiency of batteries is dependent on the number of cycle and their age. It was observed that the age of a battery increases with decreasing capacity factor. The internal battery configuration was calculated by considering the desired voltage using the cell nominal voltage. In addition, the resistance of each cell was used to compute the temperature and voltage of the battery. There were other factors that caused additional hourly losses, which were not included in the power conversion losses. These losses were due to the battery system such as pumps, heaters, and other equipment. It is
important to note that for AC-connected batteries, the losses were on the AC side, while for DCconnected batteries, the losses were on the DC side. For thermal behavior of the batteries, heat transfer coefficient (h) is directly proportional to the heat transfer (Cp), i.e., Cp, α, and h. However, if a battery requires more operations related to heating or cooling, then the associated electricity can be regarded as “additional system losses”. Some physical properties of the battery are listed in Table 6.
Table 6. Physical properties of customized lead acid battery Parameters Specific energy per mass Specific energy per volume Battery mass Battery volume
Calculated 197.33 Wh/kg 501.25 Wh/L 51.351 kg 0.0202157 m3
Values Reference [12] 196.52 Wh/Kg 499.58 Wh/L 52.258 kg 0.00254 m3
Figure 1. Trends of battery capacity
It can be observed in Fig. 1 that the lifetime battery capacity was approximately 98%, which means that the battery was efficient up to 2000 min. The charging of the battery and to keep it charged was approximately 50%, which depicted the charging and discharging of the battery in same manner.
Figure 2. Battery charging and discharging trend. For a 1000 KWh of battery power, the battery charged in 2 h and then got steady for up to 25 h. During this time duration, the discharging of the battery remained constant at zero as shown in Fig. 2. The battery performance, including operational charging and cyclic efficiency are listed in Table 7. Table 7. Trend of battery cyclic and conversion operation Battery average cycle conversion efficiency (%) Battery average roundtrip efficiency (%) Battery bank installed capacity (kWh)
100 100 1.00E+09
2. Feasibility studies of low concentration solar PV For this case, it is important to calculate the concentration of light that affects the I-V characteristics of solar cells, which is a function of the number of photons hitting the PV surface( I LαQ.A ) [13]. An easy to understand schematic of CPV is shown in Fig. 3. The concentration of photons increases with the optical concentration ratio of each cell, thus, producing a short circuit current, which can be defined as −
I sc = Copt . I sc . This shows that short circuit current is the highest current in solar cell, whereas, the dark current contains the lowest values. The relation between cell voltage and light concentration ratio is given in Eq. 3.
Voc = Voc + kTq ln Copt
(3)
To approximate the effect of concentration on maximum power output, the governing equation is shown in Eq. 4.
Pmax = Pmax .Copt (1 + kTq ln CVoc )
(4)
Fig. 3. Concentration solar PV Based on the technical and economic assessment of CPV system, the annual energy ratio and the capacity factor were estimated to be 2325947 kWh and 23.6%, respectively. The corresponding levelized cost of energy was 26.36 ¢/kWh. Some of the calculated parameters are listed in Table 8. Table 8. Main parameters of CPV system Parameters Input radiation Levelized cost of energy Module efficiency Tracker nameplate First year Annual tracker power loss Annual stowing power loss Annual Energy Annual DC nominal
Value 1.11E+07 kWh 0.263603 $/kWh 29.8058 % 56332.9 W 2064.47 kWh/kW 107145 kWh 0 kWh 2.33E+06 kWh 3.32E+06 kWh
For the feasibility study of low CPV systems, 20 CPV cells, each with cell area of 1 cm2 and with overall module area of 14 m2, were considered. The estimated efficiency of full module was 29.8%, concentration ratio of 700, and alignment loss factor of 0.93. The other associated parameters are listed in Table 9.
Table 9. AC/DC parameters with consumption Parameters AC Power (maximum) AC voltage (nominal) Power consumption (at night) Power consumption (operation) DC power (maximum) DC voltage (maximum) DC current (maximum) MPPT DC voltage (minimum) DC voltage (nominal)
Value 333000 WAC 480 VAC 60 WAC 1139.96 WDC 342726 WDC 600 VDC 500 ADC 330 VDC 368.096 VDC
It should be noted that the maximum power was dependent on the reference beam normal irradiance (DNI) and the efficiency values specified in Table 10, assuming an ambient temperature of 20 oC, wind speed of 4 m/s, and air mass of 1.5. Table 10 shows the various concentrations (suns) with respect to irradiance and cell efficiency. It can be observed that increasing the concentration (suns) also increased the irradiance values, which in turn increased the cell efficiency. It can be observed also that radiation level increased monotonically, but not necessarily in even increment. The reference selection determined the maximum power values shown in Table 10. Table 10. CPV system under various irradiance and concentrations Concentrations (suns) 140 280 420 630 700
Irradiance (W/m2) 200 400 600 900 1000
Cell efficiency (%) 30 34 36 37 37
The number of trackers was assumed to be 20 and the cell modules in each tracker was calculated to be 150. Further, four inverters were connected to the CPV system. The capacity of the system and the tracker capacity were 1126.66 kWDC and 56.3329 kWDC, respectively. The other operational parameters are given in Table 11. Table 11. CPV system with tracking Parameters Single tracker power during operation Single tracker power (fraction of DC name plate) Inverter AC capacity General tracking error Tracker elevation angle limits(deg) Tracker azimuth angle limits(deg)
Values 1126.66 W 0.02 1332 kWAC 0.98 150min-850max 500min-3100max
3. Computational assessment of CPV with lead acid batteries In this section, low concentrated CPV module was integrated to the batteries and it was assumed that no diffusion was captured by the module, and so the system output beyond when a tracker limit had reached zero. Also, in the southern hemisphere, the tracker azimuth limit angles are interpreted to include the 0 (360) degree arc of the circle (minimum = -300, maximum = 60 for a 120-degree range). A generalized schematic diagram of CPV integrated with a battery unit is shown in Fig. 4.
Fig. 4. Integrated CPV with lead acid batteries
In this case, it was important to approximate the linear function of the energy transfer across the coupled unit. Many factors were involved in the degradation process; they include higher values of crate, the effect of temperature profile for both low and higher temperatures, and the depth of discharge, which is related to higher cyclic ratio. (
)
= (
). (
).
!" " # "#$"! # !" " #
(5)
The expression for PV cell temperature is expressed in Eq. 6. (
%%)
&
=
# &"#
"#' $ !"
& " #
(6)
The total revenue of the project was based on the annual cost, interest rate, and project lifetime, along with capital cost recovery factor. The relationship between total cost and recovery factor is given in Eq. 7. (
%
( ))* = ($
(+## "
), $ .( $ - .)/ ,.
). (%
)
(7)
Fig. 5. Rated output power with efficiency The output efficiency increased with efficiency and it was observed that the maximum power packing factor for the integrated system attained an efficiency of around 98%, which is highly acceptable. The system’s overall conversion efficiency was close to 25% with a packing factor of 6.0 as presented in Table 12. Table 12. Integrated system with soiling and derates Parameters Calculated DC wiring loss factor DC module mismatched loss factor Diodes and connections loss factor AC wiring loss factor Estimated overall system conversion efficiency Packing factor Total area
0.97 0.97
Values References[14] 0.95 0.95
0.99
1.0
0.99 24.8634 %
1.0 25.358 %
6 6.22692 acres
6 6.622692 acres
Fig. 6. Trends of energy production for one year
It can be observed in Fig. 6 that the maximum trend was achieved in the month of May while the lowest trend appeared in July. The average trends were observed in August to December, which indicates that during this period the solar intensities were not as high as those during March to June. The overall system capacity and capital cost of the integrated system are given in Table 13. Table 13. Capital and operating characteristics of integrated system System capacity Capital cost Annual fixed operating cost
1126.66 kW $5400 per kW $15 per kW
The research is expected to be carried out for 20 years with a capital cost of 60% and assumed inflation rate of 2.5%. Various calculated parameters along with the reference values are given in Table 14. The calculations investigate the relationship between CPV technology and financial parameters involves for a specific period of time. This shows that the theoretical approach predicts even less cost under the same land occupation and inflation rates which means that the volume percentages of the system need to be higher than the aggregates values of the system followed by the log(PV) function of time. Table 14. Financial parameters of integrated system Parameters Fixed charged rate Inflate rate Analysis period Project term debt Nominal debt interest rate Internal rate of return (nominal) Effective tax rate
Calculated 0.098 2.5 % 20 years 60 % of capital cost 8 % per year 13 % per year 40 % per year
Values References[15,16] 0.09925 2.5 % 20 years 55 % of capital cost 7.5 % per year 12 % per year 45 % per year
For modeling and simulation of any solar PV system, it is very important to estimate the daily global solar radiation for a particular location. As discussed above, input radiation increases to cover the energy requirement throughout the year. As shown in Fig.7, the most important parameters required includes the ambient environmental temperature and solar radiations which in turn equipped with sensors for measurement. In the current research, it was estimated that the net beam irradiance of the system corresponds to the monthly average energy of the integrated system.
Fig. 7. Trends of irradiance for one year
It can be observed in Fig. 8 that the trends for lead acid battery was at maximum when the trends for radiation was falling on the surface of CPV, while the energy production of the lead acid battery had the same trends. In this way, it may provide maximum performance for both cycling and floating duration applications. The installed battery can easily maintain for longer life cycle. As presented in Fig.8, the analysis was performed for the entire year for the purpose to estimate the expected performance of integrated system.
Fig. 8. Integrated system with and without batteries Conclusion In the study, various types of lead acid batteries were simulated in a CPV system, whose aim was to store energy for various applications. The results obtained from the technical analyses of lead acid batteries and CPV panels provide an ideal situation for energy storage and utilization. Technical and economic analyses of batteries with CPV systems show the ideal performance of an integrated system. The energy production values of integrated systems is in between 160-240kWh which is 120 % more than single CPV unit. Also, the fixed charge rates of the developed integrated system achieves the values of 0.098 which is 20% less than the stand alone CPV unit. Hence, it was concluded that lead acid batteries with CPV system are the best options for energy storage for the climate in Riyadh region. The study suggests a development of a computationally designed energy storage system powered by a renewable energy. Acknowledgment: The authors would like to thank the Dean of Scientific Research at King Saud University for funding this research through the Research Group Project no RGP-255. References: [1]. G. J. May, A. Davidson, B. Monahov, Lead batteries for utility energy storage: A review, Journal of Energy Storage 15 (2018) 145–157. [2]. E. Anderson, M. Antkowiak, R. Butt, J. Davis, J. Dean, M. Hillesheim, E. Hotchkiss, R. Hunsberger, A. Kandt, J. Lund, K. Massey, R. Robichaud, B. Stafford, and C. Visser, Broad overview of energy efficiency and renewable energy opportunities for department of defense installations, National Renewable Energy Laboratory, Technical Report NREL/TP-7A20-50172, August 2011. [3]. S. Kurtz, K. Whitfield, G.T. Mani, M.Koehl, D. Miller, J. Joyce, J. Wohlgemuth, N. Bosco, M. Kempe, T. Zgonena, Evaluation of high-temperature exposure of photovoltaic modules, Progress in Photovoltaics 19 (2011) 954–965. [4]. P.T. Moseley, D. A. J. Rand, B. Monahov, Designing lead acid batteries to meet energy and power requirements of future automobiles, Journal of Power Sources 219 (2012) 75–79.
[5]. S. Schopfera, V. Tiefenbecka, T. Staakeb, Economic assessment of photovoltaic battery systems based on household load profiles, Applied Energy 223 (2018) 229–248. [6]. A. Zurita, C. Mata-Torres, C. Valenzuela, J.M. Cardemil, R.A. Escobar, Techno-Economic Analysis of a Hybrid CSP+PV Plant Integrated with TES and BESS in Northern Chile, AIP Conference Proceedings 2033, 180013 (2018); https://doi.org/10.1063/1.5067185 Published Online: 08 November 2018. [7]. M. Yamaguchi, A. Iga, K. Ishihara, D. Wada, K. Yoshii, O. Sueda, A Study of the merits of a battery combined photovoltaic generation system for a residential house, Electrical Engineering in Japan 147 (2004) (Translated from Denki Gakkai Ronbunshi, Vol. 123-B, No. 3, March 2003, pp. 402–411). [8]. V. Esfahanian, F. Torabi, A. Mosahebi, An innovative computational algorithm for simulation of leadacid batteries, Journal of Power Sources 176 (2008) 373–380. [9]. W.L. Schram, I. Lampropoulos, W.G.J.H.M. van Sark, Photovoltaic systems coupled with batteries that are optimally sized for household self-consumption: Assessment of peak shaving potential, Appl. Energy 223 (2018) 69–81. [10]. A.S. Aziz, M.F.N. Tajuddin, M.R. Adzman, M.A.M. Ramli, S. Mekhilef, Energy management and optimization of a PV/diesel/battery hybrid energy system using a combined dispatch strategy, Sustainability 11 (2019) 683. [11]. J.B. Copetti, F. Chenlo, Lead/acid batteries for photovoltaic applications. Test results and modelling, J. Power Sources 47 (1994) 109–118. B.Bogno,J.P.Sawicki,T.Salame,M.Aillerie,F.Eve,B.Tibi, Improvement of safety, [12]. longevity and performance of lead acid battery in off-grid PV systems, International journal of hydrogen energy,Vol.42,no.2,pp.3466-3478,2017 [13]. F.M.Monika, Photovoltaic modeling handbook, Hoboken, NJ: Wiley-Scrivener, ISBN 9781119363521,Pg.4657, 2018. [14]. D.Sato, N.Yamada, Design and testing of highly transparent concentrator photovoltaic modules for efficient dual-land-use applications, Energy Sci Eng. 2019;00:1–10. [15]. Feldman D, Barbose G, Margolis R, Darghouth N, James T, Weaver S, Goodrich A, Wiser R. Photovoltaic system pricing trends: historical, recent, and near-term projections 2013 edition, US Department of Energy SunShot report PR-6A20-60207, July 16, 2013. CSP Today. CSP vs PV in South Africa – assessing the current situation. CSP Today, [16]. November 2013.
Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☒The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
All the authors would like to sincerely appreciate the Deanship of Scientific Research at King Saud University for its funding of this research through the Research Group Project no RGP-255.
S1567-1739(20)30030-4
DOI:
https://doi.org/10.1016/j.cap.2020.02.005
Reference:
CAP 5150
To appear in:
Current Applied Physics
Received Date: 30 October 2019 Revised Date:
30 December 2019
Accepted Date: 4 February 2020
Please cite this article as: S. Ud-Din Khan, Z.A. Almutairi, S. Ud-Din Khan, O.S. Al-Zaid, Development of low concentrated solar photovoltaic system with lead acid battery as storage device, Current Applied Physics (2020), doi: https://doi.org/10.1016/j.cap.2020.02.005. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier B.V. on behalf of Korean Physical Society.
Development of low concentrated solar photovoltaic system with lead acid battery as storage device Salah Ud-Din Khana*, Zeyad Ammar Almutairia,b,c, Shahab Ud-Din Khand, Omer Salah Al-Zaida a
b
Sustainable Energy Technologies Center, College of Engineering, King Saud University, PO-Box 800, Riyadh 11421, Saudi Arabia
Mechanical Engineering Department, College of Engineering, King Saud University, PO-Box 800, Riyadh 11421, Saudi Arabia c
d
King Abdullah Institute of Nanotechnology, King Saud University, Riyadh, Saudi Arabia
National Tokamak Fusion Program, Pakistan Atomic Energy Commission, Islamabad, Pakistan
*Corresponding author’s email: [email protected] Abstract: Energy storage system powered by renewable energies is a viable option to meet energy requirement without addition of carbon footprints to the environment. This study involves development of theoretical and computational models for a solar photovoltaic (PV) system coupled with a lead acid battery. The study commenced with selection of most appropriate lead acid battery and PV system for installation in a representative location in Riyadh, Kingdom of Saudi Arabia. Various technical and economic parameters were assessed and calculated by computational approach. The optimized lead acid battery was integrated with low concentration solar PV panels (CPV) followed by a feasibility study. Theoretical model was developed for the integrated system to calculate various parameters of the CPV and lead acid battery. Technical and economic assessment of this coupled unit was calculated using a theoretical approach. The developed model was then subjected to computational approach for verification and validation analysis of the integrated system. The detailed assessment of batteries and integrated system show the applicability of this system in Riyadh region. The research will be extended to develop energy storage systems for remote areas using lead acid batteries. Key words: Energy storage; Concentrated photovoltaic; Feasibility study; Technical and economic assessment; Theoretical and simulation; Integrated system. Nomenclature: CPV CSP VRLA AGM F Z
GF Pb
Concentrated photovoltaic Concentrated solar power Valve regulated lead acid batteries Absorbent glass matt Faraday’s constant Number of charges in a reaction Gibbs free energy Lead (symbol)
MTSE E CFD DC/AC SAM h Cp KWh
Isc
Maximum theoretical specific energy Average cell voltage Computational fluid dynamics Direct current/Alternative current System advisor model heat transfer coefficient Specific heat transfer Kilowatt hours Short circuit current
Copt
Optical concentration
Voc
Output voltage
Pmax
Maximum power output
MPPT
Maximum peak power tracking
Introduction: Lead acid battery is the oldest and most inexpensive storage device among all rechargeable batteries. This type of battery is normally used when other batteries cannot provide higher energy density. Due to its ability to supply high surge current, lead acid battery has low energy to weight ratio, energy to volume ratio, and power to weight ratio. These impressive features make this type of battery a viable option for motor vehicles, which need higher current for motor starter. Larger batteries are mostly used for storing energy as backup power systems in cell phone towers, heavy power systems, and hospitals. Designs of these batteries were modified to improve storage time and reduce maintenance. These batteries are collectively called as valve regulated lead acid batteries (VRLA). In these designs, significant improvements have been made in the development of sealed cells. Lead acid cells can be used for two types of applications with different input requirements. Cells in the first type are fully charged to maintain constant voltage profile. They are called flooded or stationary, which have long life, good cycling efficiency, high reliability, and low lost ratio under overcharging. This type is mostly used in signaling systems, telecommunications, and heavy stand-by systems [1]. The second type is used in applications that may require deeper discharging such as in traction applications or in load leveling systems. Periodic trends in charging, rather than continuous, are more commonly supported by low cost and cyclic efficiency. The circuit voltage of lead acid battery can be determined from the Gibbs free energy formula as shown in Eq. 1.
∆GF (PbO2 ) = −zEF
(1)
where F is Faraday’s constant and z is number of charges in a reaction. Also, the maximum theoretical specific energy for lead acid batteries can be expressed as shown in Eq. 2.
MTSE = 26805(
xE ) w
(2)
where ‘x’ is number of elementary charges, ‘E’ is average cell voltage, and ‘W’ is sum of atomic weights. The two main types of VRLA batteries discussed above are named as gel cells and absorbed glass mat (AGM). Some features of these batteries are given in Table 1. Table 1. Main parameters of various types of lead acid batteries Parameter Energy density (Wh/L) Specific energy (Wh/kg) Energy consumer price ( Wh/$) Specific power (W/kg) Cycle durability (typically 500 cycles) Self-discharge rate (%/month) Cell voltage (nominal) (V) Cutoff voltage per cell (for gel) (V) Cutoff voltage per cell (for flooded) (V) Cutoff voltage per cell (for AGM) (V) Cutoff voltage per cell (Loaded) (V/cell) Charging temperature (oC)
Value 60 - 75 30 - 40 7 - 18 180 ~ 800 3-20 2.1 2.23 2.32 2.25 1.75 -40 - 49
The effective way to increase the output from PV system is to use mirrors or lenses (optical light collectors). In this process, photovoltaic (PV) systems supply concentrated light onto the PV cells; hence, these systems are called concentrated photovoltaic (CPV). The main function of this system is to commute the light from a larger surface area to the small area of a solar cell. This CPV system works only with direct beam radiation but not with diffuse radiation. This system is applicable in areas where there is high direct normal irradiance. To obtain higher concentration for higher cell performance, sun tracking can be used. There are three types of CPV system based on the material used in cells, namely, low, medium, and higher concentration (Table 2). Table 2. Types of CPV [2] Ratio of concentration Material used Sun tracking Required cooling
High >100 - 400 Cells with multifunction x-y, y-z, z-x (2D) Active
Medium 10 - 100 Si, Cd, Te x, y, z (1D) Passive
Low 2 - 10 Si No need No need
It should be noted that approximately 30% of field efficiency can be achieved using multi-junction cells [3]; however, they require more sophisticated tracking and cooling systems, which result in higher energy cost. Using small size solar cells to concentrate light results in lower material cost. In addition, mirrors (optics) used in CPV systems are made of glass, which are normally less expensive. The system significantly increases the temperature on the PV surface; however, this energy needs to be homogenously distributed to avoid hot spotting on the surface, which could lead to material damage. Moreover, the thermodynamic efficiency of CPV system at elevated temperatures is low, thus, there is a need to use passive cooling as listed in Table 2. The lowest levelized cost of electricity and the highest
energy density that matches the peak load demand even best in hot climate regions such as in MiddleEast region make CPV technology as best candidate for renewable energy generation and storage. According to the Kingdom of Saudi Arabia’s metrological parameters and despites of many limitations for installing PV system with batteries, it is very important to conduct a feasibility studies of the solar PV system integrated with batteries. In most part of the Kingdom, the climate conditions are somehow hard thus affecting the performance of the batteries. In this way, theoretical-computational model development can provides the pre-assessment of the system. In the current research, the main objective is develop such coupling mode between solar PV system (low power) and lead acid battery as workable solution for energy storage. An extensive review on the various mitigating factors for the use of lead acid batteries in hybrid energy systems was carried out by Patrick T. et al [4]. A comprehensive assessment of PV battery systems based on household load with comparable annual demand was considered as the best estimate for one-month test period [5]. Thermal energy storage and battery storage capacities are theoretically explained based on the economic assessment of hybrid concentrated solar power (CSP) and PV plant from a case study in Chile, which found the capacity factor to be above 90% with levelized costs between 110 to 113 U$/MWh [6]. The suitability of batteries coupled with PV systems for ordinary residence has been tested under various configurations and operational methods [7]. A computational fluid dynamics (CFD) model of lead acid batteries proves that numerical procedures are more efficient and easier to implement compared with conventional models for predicting battery dynamics and other factors [8]. A theoretical approach for PV coupled batteries, which are commercially viable due to self-consumption benefits and which need to be optimally sized to have large peak shaving potential [9]. A theoretical consideration in combining load following strategy with cyclic charging strategy for hybrid PV battery energy systems can give accurate assessment on both the operation and energy management optimization of hybrid systems [10]. Mathematical and experimental investigations on charging and discharging of lead acid batteries at various currents and temperatures for larger range of operating conditions allow to set values for each parameter [11]. The prime objective of study is to investigate energy storage system powered by CPV system. The study is based on developing model of integrated lead acid battery and CPV system most suitable for the Riyadh region. Among number of studies already conducted for lead acid batteries and CPV system but no study defines theoretically proven stand-alone integrated CPV system. In this study, theoretical approach of developing such system was considered. Technical specifications of all types lead acid batteries were simulated and compared in terms of various parameters. The most optimized battery was selected for the integrated system. Different types of CPV systems were reviewed for the most optimal candidate for energy storage system. Based on the optimized lead acid battery and feasibility study of CPV system, a technical and economical assessment model was developed. The results obtained pointed towards the development of working prototype in Riyadh. Research Methodology: The three main objectives of the research are as follows. 1. Computational assessment of various types of lead acid battery; 2. Feasibility studies of low concentration solar PV; 3. Computational assessment of low CPV with lead acid batteries. 1. Computational assessment of various types of lead acid battery
The types of batteries explained above have been computationally analyzed for both technical and economic as presented in Table 3. The main objective was to obtain the most optimized battery applicable for any renewable energy powered configuration. The technical assessment of the batteries showed that cost increases with the number of features loaded in a lead acid battery. The analyses of levelized electric cost, performance ratio, net savings plan, energy yield, energy production, and debt ratio showed that a customized type of lead acid battery has to be considered. This also confirms that the most common lead acid battery can be the optimized solution for energy storage in some applications. However, for advanced and higher energy applications, there are many other factors involved, where other types of lead acid battery could be the appropriate solution. Simulation results showed that flooded type battery can be a good alternative to valve regulated batteries (VRLA), particularly in terms of levelized energy cost and performance ratio. Table 3. Comparison of various types of lead acid batteries Battery parameters Metric Annual energy (year 1) (kWh) Capacity factor (year 1) (%) Energy yield (year 1) kWh/kW Performance ratio (year 1) Battery efficiency (incl. converter + ancillary) (%) Levelized COE (nominal) ¢/kWh Levelized COE (real) ¢/kWh Electricity bill without system (year 1) ($) Electricity bill with system (year 1) ($) Net savings with system (year 1) ($) Net present value ($) Simple payback period Discounted payback period Net capital cost ($) Equity ($) Debt ($)
Customized
Flooded
VRLA(AGM) Value 36,672 8.40 734 0.35
VRLA Gel
36,672 11.00 966 0.46
36,672 14.10 1,238 0.59
100.00
100.00
100.00
100.00
716431.50 414705.50
81152504.00 63829612.00
81152568.00 63829668.00
81106600.00 63793508.00
3,242
3,468
3,468
3,468
5,231
6,739
6,739
6,743
5,272 313,987,661,824 806,130,024,448 0 604,110,023,742
3,272 313,989,038,080 806,130,024,448 0 806,130,024,448
3,272 313,989,070,848 806,130,089,984 0 806,130,089,984
3,275 313,989,070,848 806,130,089,984 0 806,130,089,984
36,693 8.40 734 0.35
The battery was configured based on the desired bank size including its capacity and power fields, which were calculated prior to conversion and other detrimental losses. In this case, the DC/AC ratio conversion efficiency was used to manage and scale the battery size. In the proposed battery configuration, the desired bank capacity was 10 kWh with actual bank capacity of 5 kWh. The number of cells in series were three, while the maximum rates of charge and discharge were taken as constant at
0.5 per hour. System Advisor Module (SAM) measured various bank properties used for simulation purposes. The nominal value for bank voltage was the product of number of cells in series and nominal voltage, while the nominal voltage was the product of bank voltage and capacity of cells. The c-rate denotes the charge or discharge capacity of a battery per hour. The maximum value for power was calculated from the maximum discharge c-rate. Some of the properties of lead acid batteries are presented in Table 4. Table 4. Main parameters of customized lead acid batteries Parameters Nominal bank voltage Nominal bank capacity Maximum charge power Maximum discharge power Time for maximum power Total number of cells Number of cells in series Maximum charge current Maximum discharge current Maximum charge c-rate Maximum discharge c-rate
Values 500.4 V (DC) 10.1331 kWh (DC) 5.06655 kW (DC) 5.06655 kW (DC) 2.0 h 1251 139 10.125 A 10.125 A 0.5 /h 0.5 /h
An important feature of SAM is that if the input is higher compared with the desired grid power, it charges the battery if electric load is less than the target and discharges if electric load is greater than the target. When connecting the battery to any photovoltaic panel (PV or CPV), it should be noted that the battery must be connected to the DC side of PV or CPV system; however, AC to DC conversion efficiency is adjustable. In addition, initial and minimum/maximum values of state-of-charge, as well as minimum rate of charge state, was adjusted according to the requirement. The cycle degradation and battery lifetime are listed in Table 5. Table 5. Battery cycle degradation of lead acid battery Cycles elapsed 0 5000 10000 0 1000 2000
Depth-of-discharged 20 20 20 80 80 80
Capacity (%) 100 80 60 100 80 60
Moreover, the efficiency of batteries is dependent on the number of cycle and their age. It was observed that the age of a battery increases with decreasing capacity factor. The internal battery configuration was calculated by considering the desired voltage using the cell nominal voltage. In addition, the resistance of each cell was used to compute the temperature and voltage of the battery. There were other factors that caused additional hourly losses, which were not included in the power conversion losses. These losses were due to the battery system such as pumps, heaters, and other equipment. It is
important to note that for AC-connected batteries, the losses were on the AC side, while for DCconnected batteries, the losses were on the DC side. For thermal behavior of the batteries, heat transfer coefficient (h) is directly proportional to the heat transfer (Cp), i.e., Cp, α, and h. However, if a battery requires more operations related to heating or cooling, then the associated electricity can be regarded as “additional system losses”. Some physical properties of the battery are listed in Table 6.
Table 6. Physical properties of customized lead acid battery Parameters Specific energy per mass Specific energy per volume Battery mass Battery volume
Calculated 197.33 Wh/kg 501.25 Wh/L 51.351 kg 0.0202157 m3
Values Reference [12] 196.52 Wh/Kg 499.58 Wh/L 52.258 kg 0.00254 m3
Figure 1. Trends of battery capacity
It can be observed in Fig. 1 that the lifetime battery capacity was approximately 98%, which means that the battery was efficient up to 2000 min. The charging of the battery and to keep it charged was approximately 50%, which depicted the charging and discharging of the battery in same manner.
Figure 2. Battery charging and discharging trend. For a 1000 KWh of battery power, the battery charged in 2 h and then got steady for up to 25 h. During this time duration, the discharging of the battery remained constant at zero as shown in Fig. 2. The battery performance, including operational charging and cyclic efficiency are listed in Table 7. Table 7. Trend of battery cyclic and conversion operation Battery average cycle conversion efficiency (%) Battery average roundtrip efficiency (%) Battery bank installed capacity (kWh)
100 100 1.00E+09
2. Feasibility studies of low concentration solar PV For this case, it is important to calculate the concentration of light that affects the I-V characteristics of solar cells, which is a function of the number of photons hitting the PV surface( I LαQ.A ) [13]. An easy to understand schematic of CPV is shown in Fig. 3. The concentration of photons increases with the optical concentration ratio of each cell, thus, producing a short circuit current, which can be defined as −
I sc = Copt . I sc . This shows that short circuit current is the highest current in solar cell, whereas, the dark current contains the lowest values. The relation between cell voltage and light concentration ratio is given in Eq. 3.
Voc = Voc + kTq ln Copt
(3)
To approximate the effect of concentration on maximum power output, the governing equation is shown in Eq. 4.
Pmax = Pmax .Copt (1 + kTq ln CVoc )
(4)
Fig. 3. Concentration solar PV Based on the technical and economic assessment of CPV system, the annual energy ratio and the capacity factor were estimated to be 2325947 kWh and 23.6%, respectively. The corresponding levelized cost of energy was 26.36 ¢/kWh. Some of the calculated parameters are listed in Table 8. Table 8. Main parameters of CPV system Parameters Input radiation Levelized cost of energy Module efficiency Tracker nameplate First year Annual tracker power loss Annual stowing power loss Annual Energy Annual DC nominal
Value 1.11E+07 kWh 0.263603 $/kWh 29.8058 % 56332.9 W 2064.47 kWh/kW 107145 kWh 0 kWh 2.33E+06 kWh 3.32E+06 kWh
For the feasibility study of low CPV systems, 20 CPV cells, each with cell area of 1 cm2 and with overall module area of 14 m2, were considered. The estimated efficiency of full module was 29.8%, concentration ratio of 700, and alignment loss factor of 0.93. The other associated parameters are listed in Table 9.
Table 9. AC/DC parameters with consumption Parameters AC Power (maximum) AC voltage (nominal) Power consumption (at night) Power consumption (operation) DC power (maximum) DC voltage (maximum) DC current (maximum) MPPT DC voltage (minimum) DC voltage (nominal)
Value 333000 WAC 480 VAC 60 WAC 1139.96 WDC 342726 WDC 600 VDC 500 ADC 330 VDC 368.096 VDC
It should be noted that the maximum power was dependent on the reference beam normal irradiance (DNI) and the efficiency values specified in Table 10, assuming an ambient temperature of 20 oC, wind speed of 4 m/s, and air mass of 1.5. Table 10 shows the various concentrations (suns) with respect to irradiance and cell efficiency. It can be observed that increasing the concentration (suns) also increased the irradiance values, which in turn increased the cell efficiency. It can be observed also that radiation level increased monotonically, but not necessarily in even increment. The reference selection determined the maximum power values shown in Table 10. Table 10. CPV system under various irradiance and concentrations Concentrations (suns) 140 280 420 630 700
Irradiance (W/m2) 200 400 600 900 1000
Cell efficiency (%) 30 34 36 37 37
The number of trackers was assumed to be 20 and the cell modules in each tracker was calculated to be 150. Further, four inverters were connected to the CPV system. The capacity of the system and the tracker capacity were 1126.66 kWDC and 56.3329 kWDC, respectively. The other operational parameters are given in Table 11. Table 11. CPV system with tracking Parameters Single tracker power during operation Single tracker power (fraction of DC name plate) Inverter AC capacity General tracking error Tracker elevation angle limits(deg) Tracker azimuth angle limits(deg)
Values 1126.66 W 0.02 1332 kWAC 0.98 150min-850max 500min-3100max
3. Computational assessment of CPV with lead acid batteries In this section, low concentrated CPV module was integrated to the batteries and it was assumed that no diffusion was captured by the module, and so the system output beyond when a tracker limit had reached zero. Also, in the southern hemisphere, the tracker azimuth limit angles are interpreted to include the 0 (360) degree arc of the circle (minimum = -300, maximum = 60 for a 120-degree range). A generalized schematic diagram of CPV integrated with a battery unit is shown in Fig. 4.
Fig. 4. Integrated CPV with lead acid batteries
In this case, it was important to approximate the linear function of the energy transfer across the coupled unit. Many factors were involved in the degradation process; they include higher values of crate, the effect of temperature profile for both low and higher temperatures, and the depth of discharge, which is related to higher cyclic ratio. (
)
= (
). (
).
!" " # "#$"! # !" " #
(5)
The expression for PV cell temperature is expressed in Eq. 6. (
%%)
&
=
# &"#
"#' $ !"
& " #
(6)
The total revenue of the project was based on the annual cost, interest rate, and project lifetime, along with capital cost recovery factor. The relationship between total cost and recovery factor is given in Eq. 7. (
%
( ))* = ($
(+## "
), $ .( $ - .)/ ,.
). (%
)
(7)
Fig. 5. Rated output power with efficiency The output efficiency increased with efficiency and it was observed that the maximum power packing factor for the integrated system attained an efficiency of around 98%, which is highly acceptable. The system’s overall conversion efficiency was close to 25% with a packing factor of 6.0 as presented in Table 12. Table 12. Integrated system with soiling and derates Parameters Calculated DC wiring loss factor DC module mismatched loss factor Diodes and connections loss factor AC wiring loss factor Estimated overall system conversion efficiency Packing factor Total area
0.97 0.97
Values References[14] 0.95 0.95
0.99
1.0
0.99 24.8634 %
1.0 25.358 %
6 6.22692 acres
6 6.622692 acres
Fig. 6. Trends of energy production for one year
It can be observed in Fig. 6 that the maximum trend was achieved in the month of May while the lowest trend appeared in July. The average trends were observed in August to December, which indicates that during this period the solar intensities were not as high as those during March to June. The overall system capacity and capital cost of the integrated system are given in Table 13. Table 13. Capital and operating characteristics of integrated system System capacity Capital cost Annual fixed operating cost
1126.66 kW $5400 per kW $15 per kW
The research is expected to be carried out for 20 years with a capital cost of 60% and assumed inflation rate of 2.5%. Various calculated parameters along with the reference values are given in Table 14. The calculations investigate the relationship between CPV technology and financial parameters involves for a specific period of time. This shows that the theoretical approach predicts even less cost under the same land occupation and inflation rates which means that the volume percentages of the system need to be higher than the aggregates values of the system followed by the log(PV) function of time. Table 14. Financial parameters of integrated system Parameters Fixed charged rate Inflate rate Analysis period Project term debt Nominal debt interest rate Internal rate of return (nominal) Effective tax rate
Calculated 0.098 2.5 % 20 years 60 % of capital cost 8 % per year 13 % per year 40 % per year
Values References[15,16] 0.09925 2.5 % 20 years 55 % of capital cost 7.5 % per year 12 % per year 45 % per year
For modeling and simulation of any solar PV system, it is very important to estimate the daily global solar radiation for a particular location. As discussed above, input radiation increases to cover the energy requirement throughout the year. As shown in Fig.7, the most important parameters required includes the ambient environmental temperature and solar radiations which in turn equipped with sensors for measurement. In the current research, it was estimated that the net beam irradiance of the system corresponds to the monthly average energy of the integrated system.
Fig. 7. Trends of irradiance for one year
It can be observed in Fig. 8 that the trends for lead acid battery was at maximum when the trends for radiation was falling on the surface of CPV, while the energy production of the lead acid battery had the same trends. In this way, it may provide maximum performance for both cycling and floating duration applications. The installed battery can easily maintain for longer life cycle. As presented in Fig.8, the analysis was performed for the entire year for the purpose to estimate the expected performance of integrated system.
Fig. 8. Integrated system with and without batteries Conclusion In the study, various types of lead acid batteries were simulated in a CPV system, whose aim was to store energy for various applications. The results obtained from the technical analyses of lead acid batteries and CPV panels provide an ideal situation for energy storage and utilization. Technical and economic analyses of batteries with CPV systems show the ideal performance of an integrated system. The energy production values of integrated systems is in between 160-240kWh which is 120 % more than single CPV unit. Also, the fixed charge rates of the developed integrated system achieves the values of 0.098 which is 20% less than the stand alone CPV unit. Hence, it was concluded that lead acid batteries with CPV system are the best options for energy storage for the climate in Riyadh region. The study suggests a development of a computationally designed energy storage system powered by a renewable energy. Acknowledgment: The authors would like to thank the Dean of Scientific Research at King Saud University for funding this research through the Research Group Project no RGP-255. References: [1]. G. J. May, A. Davidson, B. Monahov, Lead batteries for utility energy storage: A review, Journal of Energy Storage 15 (2018) 145–157. [2]. E. Anderson, M. Antkowiak, R. Butt, J. Davis, J. Dean, M. Hillesheim, E. Hotchkiss, R. Hunsberger, A. Kandt, J. Lund, K. Massey, R. Robichaud, B. Stafford, and C. Visser, Broad overview of energy efficiency and renewable energy opportunities for department of defense installations, National Renewable Energy Laboratory, Technical Report NREL/TP-7A20-50172, August 2011. [3]. S. Kurtz, K. Whitfield, G.T. Mani, M.Koehl, D. Miller, J. Joyce, J. Wohlgemuth, N. Bosco, M. Kempe, T. Zgonena, Evaluation of high-temperature exposure of photovoltaic modules, Progress in Photovoltaics 19 (2011) 954–965. [4]. P.T. Moseley, D. A. J. Rand, B. Monahov, Designing lead acid batteries to meet energy and power requirements of future automobiles, Journal of Power Sources 219 (2012) 75–79.
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Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☒The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
All the authors would like to sincerely appreciate the Deanship of Scientific Research at King Saud University for its funding of this research through the Research Group Project no RGP-255.