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  • Procedia Technology 11 ( 2013 ) 1048 1053

    2212-0173 2013 The Authors. Published by Elsevier Ltd. Open access under CC BY-NC-ND license.Selection and peer-review under responsibility of the Faculty of Information Science & Technology, Universiti Kebangsaan Malaysia.doi: 10.1016/j.protcy.2013.12.293

    The 4th International Conference on Electrical Engineering and Informatics (ICEEI 2013)

    Study of Renewable Energy Sources Capacity and Loading UsingData Logger for Sizing of Solar-Wind Hybrid Power System

    M. Ikhsan*, Agus Purwadi, Nanang Hariyanto, Nana Heryana, Yanuarsyah Haroen

    Electrical Power Engineering, School of Electrical Engineering and Informatics, Bandung Institute of TechnologyJl. Ganesha 10, Bandung 40132, Indonesia

    Abstract

    If the sizing of renewable energy power plant is planned with a less reliable data of its energy sources, usually in the future, thepower system will have poor performance. Such cases often occur in various regions, including Indonesia. This problem can be solved through observation and measurement in the power systems using a data logger. Data logger is the electronic equipmentwhich can record the formation of data accurately. In this paper, the current and voltage formation of solar-wind hybrid power system which is recorded by the data logger will be analyzed. The results then will be used for resizing strategy of the plant,especially which was established but still showed indications of inefficiency. Furthermore, by using the Levelized Cost of Electricity (LCOE), the system energy cost before and after resizing will be compared. The data logger is also used to see the capacity of the energy source by applying the indirect method.

    2013 The Authors. Published by Elsevier B.V.Selection and peer-review under responsibility of the Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia

    Keywords: Data logger; system sizing; renewable energy; indirect method, LCOE

    1. Introduction

    Various research about the sizing strategy of hybrid power system have been carried out, some of them is based on the potential data of energy [1], the technical characteristics of the plant [2,3], to the levels of CO2 emissions [4]. A large amount of research indicated that the sizing strategy of hybrid power system still require an improvement [5]. In the sizing plan of hybrid power system, a detail, accurate, and reliable data about energy potential in a specific location is the most important thing to have [2,4,5]. But in fact, it is often not available. If the system sizingis based only with a less reliable data, when it completes, the hybrid power systems is not producing the capacity as expected.

    * Corresponding author. Tel.: +62-813-65490814.

    E-mail address: [email protected]

    Available online at www.sciencedirect.com

    2013 The Authors. Published by Elsevier Ltd. Open access under CC BY-NC-ND license.Selection and peer-review under responsibility of the Faculty of Information Science & Technology, Universiti Kebangsaan Malaysia.

    ScienceDirect

  • 1049 M. Ikhsan et al. / Procedia Technology 11 ( 2013 ) 1048 1053

    The solar-wind hybrid power system that operates out of its specification rate can lead to lowering the system efficiency and loss in financial [6].The system load and the capacity of available energy sources is important to highlight so that the hybrid power plants work more efficiently.

    This research uses the data logger, the electronic instrument with an electronic sensor instrument capable ofrecording the data in a specified period [7]. The data from the data logger, in the form of current and voltageformation, obtained from each renewable energy sources of hybrid power system will be analyzed. The results will then be used as the basis for sizing of hybrid power systems. Afterwards, the energy cost before and after the sizingprocess can be calculated and compared using Levelized Cost of Electricity (LCOE).

    2. Mathematical equations of the wind turbines

    The amount of kinetic energy which can be converted by the wind turbines into mechanical energy is mathematically written as Eq. (1),

    2 31

    2PP C r VS (1)

    Where is the power coefficient, is the air density (at sea level: 1.22 Kg/m3), is the radius of wind turbine blades (m), and is the wind speed (m/s). express the wind kinetic energy that can be extracted by the blades. In some references such as [8-11], is a function of the pitch angle , the turbine blade radius , the angular velocity, and the wind speed. approach as described above will greatly depend on the design of the turbine, making it difficult to use in other types of turbine. In order to avoid this difficulty, the mathematical equations used in thispaper will refer to [12] which use a polynomial method and wind turbine power curve.

    3. Photovoltaic mathematical models

    The photovoltaic mathematical model in this study was referred to [13]. The generated current I is proportional to Eq. (2) as follow,

    , s spv n I T on t p

    V R I V RGI I K I exp

    G V a R

    (2)

    t s

    kTV N

    q (3)

    , ,p s

    pv n sc np

    R RI I

    R

    (4)

    where,

    : Diode saturation current, typical 10-6 to10-15 A [14] NS : Number of cells connected in series

    'LRGHFRQVWDQWD K : Boltzman constant, 1.3806 x 10-23 J/KRS : Series resistance Q : Electron charge, 1.6021 x 10

    -19 CRP : Parallel resistance Gn : 1000 W/m2Vt : Thermal resistance

    KI is a current constant that is generally found in the product datasheet, T is the actual temperature in Kelvinreduced by the nominal temperature. The nominal light-generated current value IPV,n,, is equal to the Eq. (4). Isc, is the short circuit current of solar panels. The current coefficient KI and KV will affect Io which is very dependent ontemperature. The other unknown parameters is Rs and Rp. The value of Rs and Rp can be found if Pmax,m=Pmax,e, thus,

    , 0 ,1mp s mp mp s mp

    max m mp Pv max es p

    V R I V R IqP V I I exp P

    kT aN R

    (5)

  • 1050 M. Ikhsan et al. / Procedia Technology 11 ( 2013 ) 1048 1053

    (6)

    Eq.(5) and Eq. (6) has a meaning that for each value of Rs there is only be one value of Rp. To obtain this value it is necessary to do iterations process until Pmax,m equal to Pmax,e.

    Eq. (2) to Eq. (6) will be used to measure the solar irradiation indirectly. In fact, references [15] have first done the indirect measurements for solar irradiation. However, because it uses only the photovoltaic output voltage, the method is considered not accurate enough to used.

    4. Cost of energy

    Electricity generation cost can be calculated by using the Levelized Cost of Electricity (LCOE) as written in Eq.(7)[16], some references use the term Levelized Energy Cost (LEC).

    (7)

    is the system lifetime, , , , is the maintenance cost, operating cost, and fuel cost. Discount is the annual percentage rate at which the value of a unit is assumed to fall over time.

    5. System resizing procedure

    The resizing flowchart is shown in Fig 1. First, the recorded data was used to calculate the amount of energyreceived by the power system, this can be done using Eq. (8) where is the power (W) recorded by the data logger with sampling time 10 s (10/3600 hour), n = 1 and M =8640. 1 is the data taken at 00.00 AM while 8640 is the data taken at 23.59 PM.

    (8)

    Fig.1. The hybrid power system resizing flowchart

    The total of energy received by the system is used to determining the batteries capacity (based on energy balance).Some options that can be done is increasing or decreasing the capacity of the batteries and make adjustments to theload. In this paper, no option for adding the capacity of energy sources, because these components are relativelyexpensive compared to the other.

    6. PV, Wind turbine, and batteries performance based on field experiment

    The data logger used was appropriate with [7], and the installation schematic is shown as Fig 2.

  • 1051 M. Ikhsan et al. / Procedia Technology 11 ( 2013 ) 1048 1053

    Fig.2. The installation schematic of data Logger

    The hybrid solar-wind power system at ITB-Jatinangor is used for data collection. It uses 2 x 130 WP SharpND130T1J solar panels, 500 Watt Hummer HPW-500 wind turbine, and 2 x 70 Ah batteries. The system is off-grid and used only as a source of lighting with a load capacity installed 3 x 10 Watt. Using Eq. (8), the average energyper day, charge and discharge of the battery, the energy produced, and the duration of all events can be calculated as shown in table 1. For the wind energy calculation, an indirect method as referred in [12] was used.

    Table 1.System average energy per dayProcess Time Duration Watt-hour

    Battery charging 6 am 5 pm 9h 43m 774.25Battery discharging (load) 5 pm 6 pm 14h 57m 451.87Photovoltaic generation 6 am 5 pm 9h 43m 1320.14Wind generation 0 am 0 pm 24 h 636.71

    6.1. Wind Turbine

    Using the indirect methods as shown in [12], the average working point of the wind turbines can be mapped as shown in Fig 3a. It appears that the wind turbines are rarely generating power at its rating (500watts at wind speeds of 7 m/s). Fig 3b shows the wind speed at the location for 24 hour, the red lines express the cut-in speed of the windturbine (3m/s). Only wind above 3m/s which the energy can be extracted.

    Fig.3.Wind turbines performance over 24 hours (a) working point (b) average daily wind speed

    6.2. Photovoltaic

    By using the current, voltage, and temperature data into Eq.2 to Eq.6, the solar insolation at the site for 24 hours can be determined, as shown in the Fig 4a. While the current, voltage, and temperature of the solar panel in 24 hour is mapped as shown in Fig 4b.

  • 1052 M. Ikhsan et al. / Procedia Technology 11 ( 2013 ) 1048 1053

    Fig.4. Photovoltaic performance for 24hour (a) solar insolation (b) current, voltage, and temperature working point

    6.3. Batteries

    The battery used at the field is G-Force 70 Ah 12 V flooded shallow cycle type batteries. Parameters of the existing battery can be seen according to the table 2.

    Table 2. System batteries parameter

    No Parameter Existing BatteryAfter resizing(with wind turbine)

    After resizing(without wind turbine)

    Unit

    1 Nominal voltage 2 x 12 2 x 12 2 x 12 V2 Ampere.hour 2 x 70 2 x 260 2 x 175 A.h3 Maximum capacity 2 x 840 2 x 3120 2 x 2100 W.h4 Efficiency 58 58 58 %5 Depth of discharge 27 27 27 %6 Average charging energy 774.25 1956.85 1320.14 W.h7 Average discharging energy 451.87 1134.97 765.68 W.h

    7. Result

    The new battery capacity will be adjusted by the amount of energy that flows into the system. The total input of system energy each day is 1956.85 Wh. If the depth of discharge (DOD) and the efficiency of the new battery isassumed equal with the existing battery, the new battery capacity is,

    1956.85

    0.58 0.27 24521

    nom

    totalWhBattery capacity Ah

    efficiency DOD V

    u u

    u u (9)

    Table 3.System energy cost before and after resizing procedure

    System Status LCOE Component Unit Wind Turbine PV BatteryBefore Resizing Total Life cycle Cost Rp 18,239,723.03 25,000,000.00 8,845,733.27Ein : 1956.85 Wh Total Lifetime Energy Wh 2,981,725.36 8,771,648.61 3,233,334.42Eout : 451.87 Wh Energy Cost Rp/kWh 6,117.17 2,850.09 2,735.79

    Average energy Cost Rp/kWh 3,901.02

    After Resizing Total Life cycle Cost Rp 18,239,723.03 25,000,000.00 63,386,957.012 x 520 Ah Battery Total Lifetime Energy Wh 2,981,725.36 8,771,648.61 8,121,224.17Eout : 1134.97 Wh Energy Cost Rp/kWh 6,117.17 2,850.09 7,805.10

    Average energy Cost Rp/kWh5,590.79

    After Resizing Total Life cycle Cost Rp 0.00 25,000,000.00 21,453,702.46without wind turbine Total Lifetime Energy Wh 0.00 8,771,648.61 5,478,787.032 x 350 Ah Battery Energy Cost Rp/kWh 0.00 2,850.09 3,915.78Eout : 765.68 Wh Average energy Cost Rp/kWh 3,382.93

    2426

    2830

    32

    0

    5

    1020

    25

    30

    35

    40

    Current (A) Voltage (V)

    Tem

    pera

    ture

    (Cel

    sius

    )

    Sol

    ar Ir

    radi

    atio

    n (W

    /m2 )

    0 1000 2000 3000 4000 5000 6000 7000 8000 90000

    100

    200

    300

    400

    500

    600

    700

    800

    Data number

    a b

  • 1053 M. Ikhsan et al. / Procedia Technology 11 ( 2013 ) 1048 1053

    So, the system is requiring 2 x 520 Ah 12 V batteries. Due to efficiency index, only 58% of 1956.85 Wh energy that can discharged, which is Wh 1134.97. Then, If the lighting load must be served for 15 hours, the load can be resize to 75 W or equivalent to 1125 Wh.

    The generation cost was calculated using LCOE. Table 3 compares the system component cost before and afterthe resizing. Based on the result, there are several factors that lead to the high LCOE, which is the use of low efficiency shallow cycle batteries, and low energy generated by wind turbines. If the wind turbines not included in the calculation of energy flows and cost, the LCOE value of the system can decrease. This means that the powersystem will be more efficient and better without the presence of wind turbine components.

    8. Conclusion

    In this study, data logger is used to resize an inefficient generating system. The recorded data was analyzed and used in the resizing process. Power plant components such as the battery and the load will be adjusted according tothe total amount of energy that flows into the system. The same data is also used for the indirect measurementmethod to see the performance of solar panels and the wind turbine. Through this performance monitoring, it can be known whether the energy source is effective to use or not. The resizing strategy is also seen from the cost of energywhich calculated using the LCOE. With this value, the costs of each system component before and after the resizing can be calculated and compared.

    Acknowledgements

    This research was funded by Director General of Higher Education Republic of Indonesia through DIKTI decentralization program year 2013.

    References

    [1] Borowy, B.S., Salameh, Z.M., Methodology for optimally sizing the combination of a battery bank and PV array in a Wind/PV hybrid system. IEEE Transactions on Energy Conversion, 1996. 11(2):367-373.

    [2] A. Musse. M., S. Marizan, Design and Proper Sizing of Solar Energy Schemes for Electricity Production in Malaysia, National Power and Energy Conference (PECon) Proceedings, 2003. p.268 271

    [3] S. Diaf, D. Diaf, M. Belhamel, M. Haddadi , A. Louche A : Methodology For Optimal Sizing Of Autonomous Hibrid Pv/Wind System[4] R.Bazyar, Kh.Valipoor, M.R.Javadi, M.Valizade and H.Kord: Optimal Design and Energy Management of stand-alone

    Wind/PV/Diesel/Battery Using Bacterial Foraging Algorithm.2010.[5] Orhan Ekren dan Banu Yetkin Ekren: Fundamental and Advanced Topics in Wind Power: Size Optimization of a Solar-wind Hibrid

    Energy System Using Two Simulation Based Optimization Techniques, Turkey. 2011.[6] H. Belmili, N. Matidji, O. Badaoui, S. Attoui, N.Hanini, O. Nedjmi: Sizing A (Photovoltaic/ Wind) Hibrid System.[7] Purwadi, A., Haroen, Y., Farianza Yahya Ali, Heryana, N., Nurafiat, D. and Assegaf, A., 2011. Prototype development of a Low Cost

    data logger for PV based LED Street Lighting System, Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011 2011.

    [8] Roln, A., Luna, ., Vzquez, G., Aguilar, D. AND Azevedo, G., 2009. Modeling of a variable speed wind turbine with a permanent magnet synchronous generator, IEEE International Symposium on Industrial Electronics 2009. p. 734-739.

    [9] Yokoyama, H., Tatsuta, F. and Nishikata, S., 2011. Tip speed ratio control of wind turbine generating system connected in series, 2011 International Conference on Electrical Machines and Systems, ICEMS 2011 2011.

    [10] Liu, W., Chen, L., Ou, J. and Cheng, S., 2011. Simulation of PMSG wind turbine system with sensor-less control technology based on model reference adaptive system, 2011 International Conference on Electrical Machines and Systems, ICEMS 2011 2011.

    [11] Chen, J. and Jiang, D., 2009. Study on modeling and simulation of non-grid-connected wind turbine, WNWEC 2009 - 2009 World Non-Grid-Connected Wind Power and Energy Conference 2009. p. 292-296.

    [12] Purwadi, A., Ikhsan, M., Nanang, H., Heryana, N., Haroen, Y., Wind Speed Calculation by Using Electrical Output and Wind Turbine Power Curve. 2013.

    [13] Villalva, M.G., Gazoli, J.R. and Filho, E.R., Comprehensive approach to modeling and simulation of photovoltaic arrays. IEEE Transactions on Power Electronics, 2009. 24(5), pp. 1198-1208.

    [14] Rashid, M., Power Electronics: Circuit, Device, and Application 3hd ed. Pearson Education. 2004.[15] Husain, N.S., Zainal, N.A., Mahinder Singh, B.S., Mohamed, N.M. and Mohd Nor, N., 2011. Integrated PV based solar insolation

    measurement and performance monitoring system, 2011 IEEE Colloquium on Humanities, Science and Engineering, CHUSER 2011 2011.p. 710-715.

    [16] Woodhouse, M., James, T., Margolis, R., Feldman, D., Merkel, T. and Goodrich, A., 2011. An economic analysis of photovoltaicsversus traditional energy sources: Where are we now and where might we be in the near future? Conference Record of the IEEE Photovoltaic Specialists Conference 2011. p. 2481-2483.