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Sustainable Cities and Society 28 (2017) 358–366

Contents lists available at ScienceDirect

Sustainable Cities and Society

journa l homepage: www.e lsev ier .com/ locate /scs

erformance evaluation of a stand-alone PV-wind-diesel-batteryybrid system feasible for a large resort center in South China Sea,alaysia

onowar Hossain a,∗, Saad Mekhilef a,∗, Lanre Olatomiwa a,b,∗

Power Electronics and Renewable Energy Research Laboratory (PEARL), Department of Electrical Engineering, Faculty of Engineering, University ofalaya, 50603 Kuala Lumpur, MalaysiaDepartment of Electrical and Electronic Engineering, Federal University of Technology, PMB 65, Minna, Nigeria

r t i c l e i n f o

rticle history:eceived 19 August 2016eceived in revised form 17 October 2016ccepted 18 October 2016vailable online 21 October 2016

eywords:tand-aloneRES

a b s t r a c t

The tourist sectors in South China Sea, Malaysia (SCSM) completely depend on diesel generators for 24 hpower supply. The emissions from diesel based power plants are environmentally risky for tourist spots.In this research article, a multi-optimal combination of stand-alone hybrid renewable energy system(HRES) for a large resort center located in SCSM has been proposed with detailed operational performanceanalysis. Hybrid Optimization Model for Electric Renewable (HOMER) software is used for economicand technical analysis of the system. The estimated peak and average load per day for the resort are1185 kW and 13,048 kW respectively. The best optimized stand-alone hybrid energy system comprisesof PV, wind, diesel generator, converter and battery. The optimized system resulted in net present cost

co-tourismOMERalaysia

(NPC) of $17.15 million, cost of energy (COE) of $0.279/kWh, renewable fraction (RF) of 41.6%, and CO2

of 2,571,131 kg/year. Whereas, the diesel only system takes NPC of $21.09 million, COE of $0.343/kWhand CO2 of 5,432,244 kg/year. The diesel only system has higher NPC, COE and CO2 emission than opti-mized HRES. The designed and analyzed HRES model might be applicable to any tourist locations anddecentralized places in SCSM and around the world having similar climate conditions.

© 2016 Elsevier Ltd. All rights reserved.

. Introduction

Renewable global status report shows renewable energyources contribute 22.8% of global electricity, whereas, the remain-ng 77.2% comes from fossil fuels and nuclear power plantRenewables Global Status Report, 2015). According to this report,bout 1.1 billion of the world population do not have access to thelectricity. However, the people of remote islands in South Chinaea, Malaysia where no accessibility to the national electrical gridue to high construction cost of the transmission line, relies onhe diesel generators for electricity (Basir Khan, Jidin, Pasupuleti,

Shaaya, 2015). Besides, the tourist sectors in the these islandsompletely depend on diesel generators for 24 h power supply

Shezan et al., 2015). But the volatile market price of diesel fuel, CO2mission and high operation and maintenance cost of diesel plantakes the system environmentally risky and costly (Ashourian

∗ Corresponding authors.E-mail addresses: saad@um.edu.my (S. Mekhilef), apumonowar@gmail.com

M. Hossain), olatomiwa.l@futminna.edu.ng (L. Olatomiwa).

ttp://dx.doi.org/10.1016/j.scs.2016.10.008210-6707/© 2016 Elsevier Ltd. All rights reserved.

et al., 2013; Fadaeenejad, Radzi, AbKadir, & Hizam, 2014). In addi-tion, the diesel price is almost double in the Malaysian islands thanon the mainland (Anwari, Rashid, Muhyiddin, & Ali, 2012). There-fore, standalone hybrid renewable energy system (HRES) can playthe most important role to supply reliable electricity to the touristsectors in these islands.

The islands located in South China Sea are full of renew-able energy resources. In 2004–2005, eight solar hybrid system(SHS) was established by TNB in five different islands situated inSouth China Sea. These SHS are operated in Pulau (Pulau meansIsland in local language) Besar (45 kW), Pulau Pemanggil (50 kW),Pulau Sibu (100 kW), Pulau Aur (50 kW) and Pulau Tinggi (50 kW)(Borhanazad, Mekhilef, Saidur, & Boroumandjazi, 2013). In 2007,Malaysian government in collaboration with TNB implementeda hybrid renewable energy system (HRES) in Perhentian Island,which comprised of 100 kW PV array, two 100 kW wind turbine,one 100 kW diesel generator and a battery bank of 480 kWh, 240 V(DC) (Darus et al., 2009).

Techno-economic viability of off-grid HRES for remote villagesand islands has been reported by many authors (Ajayi, Ohijeagbon,Mercy, & Ameh, 2016; Charfi, Atieh, & Chaabene, 2016; Demiroren

ities a

&B&&&tLD2tl

e4tcadaujaH1Roo

Cgaaba

M. Hossain et al. / Sustainable C

Yilmaz, 2010; Diaf, Belhamel, Haddadi, & Louche, 2008; Himri,oudghene Stambouli, Draoui, & Himri, 2008; Ismail, Moghavvemi,

Mahlia, 2013; Nandi & Ghosh, 2010; Rahman, Khan, Ullah, Zhang, Kumar, 2016; Shaahid & El-Amin, 2009; Shaahid, Al-Hadhrami, Rahman, 2014), whereas, the study on HRES for hotels and

ourist locations are limited (Aagreh & Al-Ghzawi, 2013; Dalton,ockington, & Baldock, 2008; Dalton, Lockington, & Baldock, 2009a;alton, Lockington, & Baldock, 2009b; Güler, Akdag, & Dinc soy,013; Shezan et al., 2015).Thus, more research can be conductedo understand feasibility and performance of HRES for the touristocations.

The Tioman Island is located in the South China Sea along theast coast of Peninsular Malaysia with the geographic location of 2◦

7′ 47′′ N, 104◦ 10′ 24′′ E (Muda et al., 2011). The island consists ofhirteen villages, private and government offices, schools, commer-ial buildings, mosques, police stations, resort and hotels, hospitalsnd an airport. TNB supplies electricity to this island with 8.9 MWiesel power plant at Tekek village and a 500 kW mini hydro plantt Juara village through 11 KV distribution line. However, TNB isnable to supply electricity to very big resort and hotels like Ber-

aya Tioman Resort (Basir Khan et al., 2015).Therefore, this studyim to investigate the feasibility and performance of an off-gridRES for a large resort located in Tioman Island for load demand of3,048 kWh/day. HOMER (Hybrid Optimization Model for Electricenewable), developed by the U.S. National Renewable Energy Lab-ratory (NREL), is employed for design and performance evaluationf the HRES (Lambert, Gilman, & Lilienthal, 2006).

Moreover, there are more than twelve small islands in Southhina Sea surrounding Pulau Tioman named; Pulau Aur, Pemang-il, Sibu, Babi Besar, Tinggi, Rawa, Harimau, Dayang, Tengah, Tulai

nd Pulau Seri Buat that have more or less same climate conditionss Tioman Island (NASA surface meteorology and solar energy dataase, 2016). The monthly average solar radiation (kWh/m2/day)nd wind speed (m/s) for these islands are presented in Table A1

Fig. 1. Location of BTR in Tioman Island [So

nd Society 28 (2017) 358–366 359

while, monthly average ambient temperature (◦C) and clearnessindex are presented in Table A2. Thus, this analysis can repre-sent any of the off-grid hybrid RE system for large resort centerlocated in these islands. In the first part of this paper, site descrip-tion with load estimation and resource assessment are conductedwhereas, in the second part, model design and its techno-economicparameters is described. Third, the performance of proposed modelis analyzed. Finally, uncertainty of the model is investigated withHOMER software.

2. Methodology

2.1. Site description and load estimation

The Berjaya Tioman Resort (BTR), a big chalet style hotel andresort located in Tioman Island, was selected in this study. Geo-graphical location of this resort is 2◦ 48′ 30′′ N, 104◦ 8′ 29′′ E, asshown in Fig. 1.

The resort is powered by diesel generators installed at the resort,but hourly load data was not available at the resort. Thus, hourlyload profile of one year for the resort was estimated. The list ofelectrical appliances employed and their power rating is shownin Table 1, while the estimated load profile for one year is shownin Fig. 2 for different seasons. All the electrical appliances and itsnumbers are computed based on the available information fromthe resort (Berjaya Tioman Resort, 2015). The electrical load inthis resort varies seasonally due to variation in tourist presence.The duration of northeast monsoon (NEM), the first inter-monsoon(FIM), the southwest monsoon (SWM), and the second inter-monsoon (SIM) are November-February, March-April, May-August,

and September-October respectively that control the climate andweather in Tioman Island (Basir Khan et al., 2015). In NEM season,high wind speed and heavy rainfall affects the tourism businesssignificantly. Consequently, the load profile of this resort is high

urce: Google map and then edited].

360 M. Hossain et al. / Sustainable Cities and Society 28 (2017) 358–366

Table 1Load estimation of the resort based on the available information from the resort (Berjaya Tioman Resort, 2015).

Load description Power rating (Watt) Quantity(no.) Running hour/day Total power (kW)

Category Appliances

All 268 Rooms in chalets Air conditioning 1500 268 10 4020.0Cable TV 150 268 5 201.0Tube light 25 804 12 241.2Table light 18 536 5 48.24Ceiling fan 80 536 4 171.52Fridge 400 268 18 1929.6Hair dryer 2500 268 2 1340.0Coffee maker 1500 268 2 804.0

Conference room that accommodate400 guests

Air conditioning 1500 10 3 45.0Tube light 25 50 3 3.75CFL bulb 40 10 3 1.2Overhead projector 300 1 2 0.60Slide projector 200 1 2 0.40Microphone 10 5 1 0.05Desktop computer 120 1 3 0.36Laptop computer 80 1 2 0.16

4 meeting rooms Air conditioning 1500 8 4 48.0Tube light 25 15 4 1.5CFL bulb 40 15 4 2.4Slide projector 200 1 2 0.4Microphone 10 4 1 0.04Laptop computer 80 1 2 0.16

2 Bars & 2 Restaurants Air conditioning 1500 12 8 144Cable TV 150 4 5 3.0Refrigerator 400 8 12 38.4Tube light 25 30 8 6.0CFL bulb 40 20 8 6.4Ceiling fan 80 8 6 3.84Freezer 400 4 12 19.2

Resort Office Air conditioning 1500 2 10 30.0Tube light 25 5 5 0.625CFL bulb 40 5 5 1.0Desktop computer 120 1 10 1.2Refrigerator 400 2 8 6.4Printer 100 2 4 0.80Scanner 10 2 2 0.04

daa

Oven 3000

Total

uring SWM, FIM, and SIM seasons and slightly low in NEM seasons shown in Fig. 2. Estimated hourly load data of one year serveds input in HOMER taking day to day load variance of 10% and time

Fig. 2. Hourly average load profile of a typica

1 2 6.0

9126.49

step of 15%. Estimated peak load and average load per day were1185 kW and 13,048 kW respectively.

l day in different seasons for the resort.

M. Hossain et al. / Sustainable Cities and Society 28 (2017) 358–366 361

F

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ig. 3. Monthly average daily solar radiation and clearness index at Tioman Island.

.2. Solar and wind resource assessment

Solar radiation data analysis is essential in developing an effec-ive solar energy conversion system as well as calculation of itsroduction capacity (Ayodele & Ogunjuyigbe, 2015; Güler et al.,013; Olatomiwa, Mekhilef, Huda, & Sanusi, 2015; Olatomiwa,ekhilef, Huda, & Ohunakin, 2015). For this aim, monthly aver-

ge global solar radiation for the period July 1983–June 2005as collected from NASA surface meteorology and solar energy

atabase for Tioman Island as there is no meteorological stationn this Island. Annual average solar radiation in Tioman Island is.21kWh/m2/day. Fig. 3 shows monthly average solar radiation andlearness index of the Island.

Fig. 3 indicates that a significant amount of solar power can bearvested from any solar panel in the Tioman Island. The electricalnergy generation as an output of a photovoltaic system can bestimated as follows (Shezan et al., 2016):

= A × r × H × PR (1)

here, E = Energy (kWh), A = Total Solar panel area (m2), r = Solaranel yield (%), H = Annual average solar radiation on tilted panelsshadings not included) and PR = Performance ratio, coefficient forosses.

The maximum power from a solar panel can be calculated asShezan et al., 2016):

mp = �PV × Gˇ × A (2)

here, A is the surface area of a PV module. Pmp is the maximumower from a solar panel, �PV is the efficiency of silicon based PVell and Gˇ is the global horizontal solar irradiance.

Wind energy is abundant renewable energy with comparativelyow GHG emissions than fossil fuel based power plants. To build a

ind farm in a specific location, analysis of wind speed data of thispecific location is needed (Ammari, Al-Rwashdeh, & Al-Najideen,015; Boudia, Benmansour, & Tabet Hellal, 2016). For this research,onthly average wind speed for the period July 1983-June 1993as collected from NASA surface meteorology and solar energy

atabase for Tioman Island as there is no meteorological station inhis Island. Annual average wind speed in Tioman Island is 3.73 m/s.verage monthly daily wind speed at 50 m height for the site ishown in Fig. 4.

. Economic model assessment criteria

Economic analysis is very essential to suggest an optimal combi-ation of components in the HRES. The HOMER software has beensed to accomplish this aim, and this is based on net present cost

NPC) given as (Sinha and Chandel, 2015b):

npc,tot = Cann,tot

CRF(i, Rproj

) (3)

Fig. 4. Monthly average wind speed at Tioman Island.

Where,Cann,tot is the total annualized cost ($/year), i is the annualreal interest rate (%), Rproj is the project lifetime (year), CRF repre-sent capital recovery factor. CRF is given by the following equation(Olatomiwa, Mekhilef, Huda, & Sanusi, 2015; Olatomiwa, Mekhilef,Huda, & Ohunakin, 2015):

CRF (i, N) = i(1 + i)N

(1 + i)N − 1(4)

Where, N and i are number of years and annual real interest raterespectively. HOMER do not use nominal interest rate in the com-putations but real interest rate is computed from real interest rateusing following equation (Ramli, Hiendro, & Twaha, 2015):

i = i’ − f1 + f

(5)

In Eq. (5), f is the annual inflation rate and i’ is the nominal interestrate. The lifetime of the project was considered 25 years with 1percent annual capacity shortage. The discount and inflation ratefor this study were considered 8% and 2% respectively. The systemcost in HOMER was simulated in US dollars ($USD).

The price of per kWh of electricity is called cost of energy (COE)(Park & Kwon, 2016). The COE is computed by dividing total annu-alized cost by annual electricity served to load which is defined asfollows (Sinha & Chandel, 2015a):

COE = Cann,totLann.load

(6)

To calculate the CO2 emissions from the hybrid energy systemthe following supporting equations has been introduced (Shezanet al., 2016):

tCO2 = 3.667 × mf × HVf × CEFf × Xc (7)

Where, tCO2=Amount of CO2 emissions, mf = Fuel quantity (Liter),HVf = Fuel heating value (MJ/L), CEFf = Carbon emission factor (toncarbon/TJ) andXc = Oxidized carbon fraction. Another factor mustbe taken into account that in 3.667 g of CO2 contains 1 g of carbon.

4. HOMER model design

The HRES was primarily designed with solar, wind, battery, con-verter, diesel generator and ac load profile shown in Fig. 5. Theinput of HOMER model was estimated hourly load data of one year,monthly average wind speed, ambient temperature and solar irra-diance data. The diesel fuel price was considered $0.80/L whichis 1.25 times over the diesel price forecasted in August 2014 by

Malaysian government for the mainland. The price of diesel forMalaysian Island is considered higher than the mainland pricebecause of additional transportation and labor cost (Anwari et al.,2012).

362 M. Hossain et al. / Sustainable Cities and Society 28 (2017) 358–366

mgar

aaT

5

wsotishvo(oa1aeop2Ne

Ttt6tnws

Fig. 6. Monthly electrical power generation in the best HRES by each system com-ponents.

Fig. 5. HOMER model of proposed hybrid renewable energy system.

The control parameters of the hybrid system were economicinimization, cycle charging, diesel off operation allow, multiple

enerator operation and system with two types of wind turbinellow. The outputs of solar and wind were considered 30 and 50%espectively.

The carbon dioxide penalty cost was taken as zero. The financialnd technical parameters for PV, wind turbine, converter, batterynd diesel generator considered in this HRES design are shown inable 2.

. Result and discussion

An optimal combination of HRES has been modelled in HOMERith large number of hourly simulations. HOMER simulates all pos-

ible combinations according to the input parameters and sorts theptimization result from lowest NPC to highest NPC. The optimiza-ion results in the categorized form are shown in Table 3, whichncludes the optimal system configurations, NPC, RF, COE, dispatchtrategy and excess electricity per year. Table 4 shows the runningours of diesel generators, percentage of contributions by indi-idual system components whereas, Table 5 shows the amountf harmful emissions including CO2, CO, unburned hydrocarbonUHC), particulate matter (PM), sulfur dioxide (SO2) and nitrogenxide (NO) in each optimized system. The maximum allowablennual capacity shortage for all simulations was considered to be

percent. Moreover, the obtained yearly unmet electric load inll feasible system were negligible and cycle charging is consid-red to be the best dispatch strategy for all hybrid system. It can bebserved from Tables 3 and 4 that the best optimized HRES com-rised of 700 kW PV module, 5 wind turbine, 3 diesel generators and40 units of battery. This system resulted in the COE of $0.279/kWh,PC of $17.15millions, renewable fraction (RF) of 41.6%, and excesslectricity (EE) of 16.2% of total electrical production.

The third best optimized system is wind-diesel-battery system.his configuration has RF of 37.6% and NPC near the best optimiza-ion system, with seven wind turbine contributing about 51.65% ofotal electrical production. It can be noticed that the system in rank

has EE of 3.4% of total production per year with $0.028 higher COE

han rank 1. However, the size of PV in this rank is 1000 kW whicheeded almost double the land area than rank 1, also this systemill increase the dependency on diesel generators. And notably, this

ystem has RF of 21.7% which is almost half that of rank 1. Addition-

Fig. 7. Summary of nominal cash flow for the best optimized HRES.

ally, this system will increase carbon footprint. The system in ranks11–13 are made up of diesel-battery system configuration that hasmuch more harmful emissions as listed in Table 5 than of the bestoptimized hybrid system. Not only this, but also the COE and NPCare much higher compare to the best case. Therefore, the system inrank 1 is considered as best off grid system configuration to supplyreliable electricity to the resort.

Fig. 6 shows monthly electrical power generated by each systemcomponents in best optimized standalone PV-wind-diesel- batteryhybrid system. It can be found that the wind turbines contributehighest in the NEM season. On the other hand, the contribution ofdiesel generators is higher than other system components in theSWM, FIM and SIM seasons due comparatively low wind speed.The selected PV panel can harvest about 81 MW per month all overthe year. Therefore, diesel-RE hybrid system is considered a morereliable power supply option to the resort than 100% RE system.

Fig. 7 shows the nominal cash flow of 25 years’ project life timewhich is NPC by cost type. It can be seen from Fig. 7 that the initialcapital cost of PV, wind turbine, batteries, converter and generatorsis around $4.2 million. Notably, $10.1 million of total NPC ($17.15million) is gulped up by fuel cost of diesel generators only.

Figs. 8 and 9 respectively shows the monthly excess energy andthe battery state of charge in different months of the year in thebest optimized HRES. It can be noted from Fig. 8 that the amount ofexcess electricity is much higher in the months of January, February

and December than other months of the year due to high windspeed usually experienced in this months. Consequently, the redcolor of Fig. 9 reveals that the batteries are over charging dur-ing the months of January, February, March, August, November

M. Hossain et al. / Sustainable Cities and Society 28 (2017) 358–366 363

Table 2Technical and economical parameters for the design of HRES.

PV Wind TurbineFactors Value Factors ValueSize (step size 100 kW) 0–1000 kW Model EWT DW 52/54Capital cost $2000/kW Size considered 0−10Replacement cost $2000/kW Nominal output 250 kWO & M cost $10/kW/year Capital cost (CC) $375,000/unitTemperature co-efficient −0.5/ ◦C Replacement cost $262,500/unit (70% of CC)Derating factor 80% O & M cost $7500/year (2% of CC)Operation temperature 47 ◦C Cut in wind speed 2.5 m/sBattery Rated wind speed 7.5/8 m/sFactors Value Cut out wind speed 25 m/sModel IND 17 Life time 20 yearsNominal voltage 6 V Hub height 50mCapacity 1,231Ah Diesel GeneratorCapital cost $1200/unit Factors ValueReplacement cost $1170/unit Brand name CumminsO & M cost $10/year. Size (step size 100 kW) 0–1000 kW (Generator 1)String size (40/string) 0−10 Size (step size 100 kW) 0–500 kW (Generator 2 & 3)Life throughput per battery 9300kWh Capital cost $220/kWBidirectional Converter Replacement cost $200/kWFactors Value O & M cost $0.03/h.Size (step size 100 kW) 0–1000 kW Minimum running hours 15,000hr.Capital cost $890/kW Minimum load ratio 25%Replacement cost $800/kW – –O & M cost $10/kW/year – –Lifetime 15 years – –Efficiency 95% – –

Table 3Optimized hybrid renewable system sorted by NPC.

Rank PV (kW) Wind GEN 1(kW) GEN 2(kW) GEN 3(kW) Batt Conv (kW) Disp COE ($/kWh) NPC (million $) RF (%) EE (%)

1 700 5 400 200 100 240 600 CC 0.279 17.1552 41.6 16.22 700 5 500 0 200 280 700 CC 0.286 17.5750 41.4 15.93 0 7 400 200 100 320 700 CC 0.287 17.6771 37.6 17.94 0 7 500 200 0 320 700 CC 0.296 18.2007 36.2 18.25 800 5 600 0 0 320 700 CC 0.306 18.7898 41.4 17.86 1000 0 500 200 100 160 500 CC 0.307 18.8516 21.7 3.47 1000 0 600 0 200 240 500 CC 0.316 19.4232 21.0 3.38 0 7 600 0 0 320 800 CC 0.316 19.4288 35.7 18.89 1000 0 0 200 500 240 500 CC 0.317 19.4726 20.5 3.310 1000 0 700 0 0 320 700 CC 0.336 20.6613 20.4 2.511 0 0 400 200 300 160 400 CC 0.343 21.0944 0 012 0 0 500 300 0 160 400 CC 0.354 21.7353 0 013 0 0 700 0 0 240 400 CC 0.385 23.6165 0 0

Table 4Performance of system components in different optimization result.

Rank Running hours of DG in a year Percentage of individual contribution

GEN 1 GEN 2 GEN 3 PV Wind GEN 1 GEN 2 GEN 3

1 4127 4057 4070 16.20 37.65 27.12 12.96 6.072 4407 0 3703 16.16 37.56 35.01 0 11.263 4685 3837 3856 0 51.65 30.37 12.17 5.814 4857 3698 0 0 51.08 37.86 11.06 05 5828 0 0 18.08 36.37 45.06 0 06 5178 3804 4230 27.21 0 50.39 14.58 7.827 5378 0 3536 27.02 0 60.18 0 12.808 6380 0 0 0 50.92 49.08 0 09 0 5311 5617 26.94 0 0 19.54 53.5210 6112 0 0 26.86 0 73.14 0 0

amtsrec

11 5955 4661 5121 012 7077 4964 0 013 8756 0 0 0

nd December because of excess electricity experienced in theseonths. The batteries can be damaged or lifetime shortened due

o overcharging. The high contributions of wind turbines in NEM

eason is responsible for this surplus electricity and consequentlyesponsible for over charging of batteries. Therefore, this excesslectricity can be dumped for the safe operation of the systemomponents.

0 49.25 19.24 31.520 71.76 28.24 00 100 0 0

To understand the uncertainty of the model in terms of COE,sensitivity analysis was carried out. The sensitivity variables were;wind speed, solar radiation and diesel price. The variables were

varied ± 30% from their scaled average value. The scaled yearlyaverage solar irradiance and wind speed for Tioman Island were5.21 kWh/m2/day and 3.73 m/s respectively, whereas, base casediesel price was $0.8/L. Fig. 10 shows the sensitivity result. It can be

364 M. Hossain et al. / Sustainable Cities and Society 28 (2017) 358–366

Table 5Harmful emissions from different system type.

Rank Emissions (kg/year)

CO2 CO UHC PM SO2 NO

1 2,571,131 6346.5 702.99 478.43 5163.3 56,6302 2,600,885 6419.9 711.13 483.96 5223.0 57,2853 2,736,751 6755.3 748.28 509.24 5495.9 60,2784 2,820,157 6961.2 771.08 524.76 5663.4 62,1155 2,755,103 6800.6 753.30 512.66 5532.7 60,6826 3,426,989 8459.0 937.0 637.68 6882.0 75,4817 3,495,995 8629.4 955.87 650.52 7020.6 77,0018 3,020,503 7455.7 825.86 562.04 6065.7 66,5289 3,487,326 8608.0 953.5 648.91 7003.2 76,81010 3,612,237 8916.3 987.65 672.15 7254.0 79,56111 5,432,244 10,940.0 1211.9 824.74 8900.7 97,62212 5,518,589 11,153.0 1235.0 840.80 9074.1 99,52313 5,829,111 11,920.0 1320.4 898.58 9697.7 1,06,363

Fig. 8. Monthly excess electricity production by the best optimized system.

Fig. 9. SOC of battery in diff

Fig. 10. Sensitivity result of the system with variable wind speed and solar radiation.

erent month of a year.

observed from the figure that solar radiation variation doesn’t affectthe COE significantly. On the other hand, COE is greatly affectedwith the variation of diesel price and wind speed. The COE variesfrom $0.229/kWh to $0.329/kWh when diesel price shifts from$0.56/L to $1.04/L. The second dominant factor wind speed wasvaried from 2.611 m/s to 4.849 m/s that resulted in COE decliningfrom $0.324 kWh to $0.241/kWh.

6. Conclusion

In Malaysia, electricity generation alone emitted 36,925,190 ton

of CO2 in 2002 and it is projected that it would be 107,318,707 tonin 2020 (Mahlia, 2002). This amount of CO2 emission from powersector alone is a great threat for a developing country like Malaysia.Therefore, stand-alone hybrid renewable energy system (HRES) can

ities a

belMwrhooTGOcwio

would like to thank Shezan Arefin for his help and suggestions.

TM

TMb

M. Hossain et al. / Sustainable C

e cost effective and environmental friendly solution in providinglectricity access. In this paper, a HRES has been designed and ana-yzed the performance for a large resort center in South China Sea,

alaysia. The best optimized system is made up of a 700 kW PV, 5ind turbine, 240 batteries, 3 units of diesel generator and a bidi-

ectional converter of 600 kW. The selected wind turbine for the siteas low cut in wind speed of 2–3 m/s. The turbine gives rated powerf 250 kW at the wind speed of 7–8 m/s. The COE, NPC and RF of theptimized system were $0.279/kWh, $17.15m, 41.6% respectively.he proposed HRES ensures significant reduction in CO2 and otherHG emissions and economical viability in terms of COE and NPC.n the other hand, the other islands surrounding the Tioman hasomparatively smaller area. Thus, the HRES in rank 3 constructed

ith wind-diesel-battery might be more suitable for these small

slands while, 700 kW PV in rank 1 need large area to install. More-ver, the analyzed hybrid energy system might be applicable for

able A1onthly average solar radiation and wind speed of the islands in South China Sea surroun

Months Solar radiation (kWh/m2/day)

Pulau Aur, Pemanggil, Sibu,Tinggi, Tioman, Dayang andPulau Tulai.

Pulau Babi Besar, Harimau,Tengah, Seri Buat and PulauSembilang.

January 4.78 4.12

February 5.76 4.97

March 5.99 5.02

April 6.10 5.08

May 5.55 4.84

June 5.22 4.66

July 5.14 4.61

August 5.30 4.70

September 5.36 4.83

October 5.05 4.70

November 4.23 4.05

December 4.04 3.62

able A2onthly average ambient temperature and clearness index of the islands in South China

ase, 2016).

Months Ambient temperature (◦C)

Pulau Aur, Pemanggil, Sibu,Tinggi, Tioman, Dayang andPulau Tulai.

Pulau Babi Besar, Harimau,Tengah, Seri Buat, and PulaSembilang.

January 25.83 25.49

February 25.84 25.70

March 26.24 26.11

April 26.84 26.51

May 27.18 26.61

June 27.13 26.34

July 26.90 26.03

August 26.83 26.04

September 26.75 26.07

October 26.75 26.29

November 26.52 26.12

December 26.15 25.74

nd Society 28 (2017) 358–366 365

the different areas of the world where the climate conditions aresame as the analyzed area. In future, we will conduct feasibility andperformance analysis of grid connected and stand-alone hybrid REsystem for other areas of the world.

Acknowledgements

The authors would like to thank the Ministry of Higher Educa-tion, Malaysia, and University of Malaya, Malaysia, for providing theenabling environment and the financial support under Postgradu-ate Research Grant (PPP) project no. PG192-2015B. The authors also

Appendix.

ding Tioman Island (NASA surface meteorology and solar energy data base, 2016).

Wind speed (m/s) at 50m

Pulau Aur, Pemanggil, Sibu,Tinggi, Tioman, Dayang andPulau Tulai.

Pulau Babi Besar, Harimau,Tengah, Seri Buat and PulauSembilang.

5.36 4.354.55 3.733.44 2.972.38 1.942.60 2.123.82 3.233.85 3.244.32 3.553.38 2.722.74 2.183.32 2.875.04 4.19

Sea surrounding Tioman Island (NASA surface meteorology and solar energy data

Clearness index

uPulau Aur, Pemanggil, Sibu,Tinggi, Tioman, Dayang andPulau Tulai

Pulau Babi Besar, Harimau,Tengah, Seri Buat and PulauSembilang.

0.49 0.420.56 0.490.57 0.480.59 0.490.56 0.490.55 0.490.53 0.480.53 0.470.52 0.470.49 0.460.43 0.410.42 0.38

3 ities a

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A

A

A

A

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B

B

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D

D

D

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D

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org/10.1016/j.enconman.2015.08.078Sinha, S., & Chandel, S. S. (2015b). Review of recent trends in optimization

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