Electrical Power and Energy Systems 43 (2012) 1393–1400

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Electrical Power and Energy Systems

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Investigation into economical desalination using optimized hybrid renewableenergy system

A. Hossam-Eldin ⇑, A.M. El-Nashar, A. IsmaielElectrical Engineering Department, Alexandria University, Alexandria, Egypt

a r t i c l e i n f o

Article history:Received 9 May 2009Received in revised form 11 May 2012Accepted 14 May 2012Available online 2 August 2012

Keywords:OptimizationHybridRenewableEnergyDesalinationEconomics

0142-0615/$ - see front matter � 2012 Elsevier Ltd. Ahttp://dx.doi.org/10.1016/j.ijepes.2012.05.019

⇑ Corresponding author.E-mail address: hossamudn@hotmail.com (A. Hoss

a b s t r a c t

This paper investigates the use of hybrid renewable energy systems (HRESs) in Reverse Osmosis (RO)desalination. Mathematical model aided with a newly developed computer program for sizing (HRES)components. The study evaluates the individual and total expenses needed as well as the amount ofexcess renewable energy production. An optimization program was developed to select the best (HRES)combination that can produce desalinated water in a relatively economic cost. It demonstrates an inves-tigated optimization approach based on minimization of the excess energy. It presents the impact of theconsidered optimization technique on the unit cost of energy and consequently unit cost of desalinatedwater. Unit production costs of both energy and desalinated water for two existing small and medium(RO) plants powered with conventional electricity grid are compared with the generated electricity fromoptimized (HRES). Cost sensitivity evaluation for (HRES) components to estimate the most economicalprice of (HRES) for desalination is presented.

� 2012 Elsevier Ltd. All rights reserved.

1. Introduction However while the results of these optimization processes

Hybrid Renewable Energy Sources (HRESs) is defined as a com-bination of one or more resources of renewable energy. It repre-sents one of the promising options for the considerable energyneeds of desalination processes especially in remote and aridregions, where the use of conventional energy (fossil fuels, electric-ity) is costly or not available. HRES powered desalination plantsmay be an attractive alternative option. In most cases, fresh waterscarcity co-exists with abundant renewable energy (RE) resources.RO desalination processes have been the technology of choice as aresult of recent technological developments in the process engi-neering. The average costs of product water have decreasedsignificantly [1,2].

The main desirable features for renewable energy systems(RESs) are low cost. The selection of the optimum combination ofRES and desalination technologies for a specific location is basedon resource availability and the technical compatibility [2].Numerous RES–RO combinations have been identified by severalresearches [3–8]. Economic aspects of these technologies are suffi-ciently promising to include them in developing power generationcapacity for developing countries and there is still need for moreeffort to be done so that the system can be optimized.

Several studies have been done demonstrating the ability tooptimize hybrid configurations of renewable energy systems in or-der to maximize performance while minimizing cost [9–14].

ll rights reserved.

am-Eldin).

show the optimum sizing and appropriate combination of compo-nents for the system, but the problem of maximum componentcapacity must be taken in consideration in order to overcomingthe existence of high excess energy [9].

This paper discusses the importance of reducing excess energyin minimizing the cost of energy (CE) for RES which defined asthe ratio of total annualized cost and annual load served bythe renewable energy hybrid system. Suggestion of maximizingload reserve for two case studies is considered to meet this opti-mization objective, consequently produce both energy and waterat reasonable cost.

2. Methodology

The proposed (HRES) as shown in Fig. 1 it is consisted of a windturbine (WT) and solar photovoltaic panels (PV). Diesel generator(G), battery (Batt) and inverter (Inv) are added as part of back-upand storage system. Scheme of the RO plant (load demand) isshown in Fig. 2.

3. Mathematical modeling

The developed computer program algorithms are based on thefollowing equations:

3.1. Energy consumption (load demand)

The pump delivered power:

Nomenclature

Notation ExplanationAnn annualAs swept area (m2)CA.Cap ann. capital cost ($/y)CA.Fu ann. fuel cost ($/y)CA.O ann. O&M cost ($/y)CA.rep ann. replacement cost ($/y)CA.Tot ann. total cost ($/y)Ccap capital cost ($)CE cost of energy ($/kW h)CFu fuel cost ($/L)CO/Batt ann. battery O&M cost ($/y)CO/G ann. generator O&M cost ($/y)CO/Inv ann. inverter O&M cost ($/y)CO/WT ann. WT O&M cost ($/y)COBatt total battery O&M cost ($/y)COG total generator O&M cost ($/y)COInv total inverter O&M cost ($/y)COPV total PV O&M cost ($/y)COWT total WT O&M cost ($/y)Cp coefficient of performanceCrep replacement cost ($)Cw cost of product water ($/m3)EA.Exc ann. excess energy (kW h/y)EA.RO ann. consump. energy (kW h/y)EA.Tot ann. total energy (kW h/y)EG ann. generator energy (kW h/y)EPV ann. PV energy (kW h/y)ERO ann. RO plant energy (kW h/y)EWT ann. wind energy (kW h/y)FUSur specific Fu. usage rate (L/kW h)GOh ann. generator operating hours (h)GP generator rated power (kW)i interest rateIBatt total battery capacity (Ah)ID total daily load (Ah)n number of yearsNBatt required number of batteryNcomp number of componentsNG required number of generatorsNInv required number of invertersNpan required number of PV panels

Nrep number of replacementsNSBatt number of battery in seriesNURO number of RO unitsNWT number of WT’sP pressure (bar), power (kW)PankW PV panel power (kW)PDP dosing chemicals pump power (kW)PFP feed water pump power kW)PG generator power (kW)PHPP high pressure pump power (kW)Ppumb pump delivered power (kW)PPV PV power (kW)PRO RO plant power (kW)PWT wind power (kW)Q flow rate (m3/h)Qp product water flow rate (m3/h)RET ann. total RE (kW h/y)SERO specific energy consumption (kW h/m3)UIBatt unit battery capacity (Ah)VAC AC voltage (volt)Vbatt battery DC voltage (volt)Vw average wind velocity (m/s)qa air density (kg/m3)gg WT generator efficiencygPan PV panel efficiencygPumb pump efficiencygWT WT efficiencyBatt batterycosu power factorCRF capital recovery factorDF design factorDOA days of autonomyU daily operating hours (h)G generatorHRES hybrid renewable energy systemInv inverterNo. numberPV photovoltaicRES renewable energy systemRO reverse osmosisROUND round to nearest integerWT wind turbine

1394 A. Hossam-Eldin et al. / Electrical Power and Energy Systems 43 (2012) 1393–1400

PPumb ¼ 0:02278 � P � Q=%gPumb ð1Þ

The RO plant power:

PRO ¼ ½PFP þ PHPP þX

PDP� � NURO ð2Þ

The RO plant energy:

ERO ¼ PRO � time ð3Þ

Specific energy consumption:

SERO ¼ ERO=Q P ð4Þ

Annual consumption energy:

EA:RO ¼ PRO �U � 365 ð5Þ

Fig. 1. HRES configuration.

3.2. HRES production

The wind power:

PWT ¼ NWT �gW � gg � qa � Cp � AS � V3

W

2 � 1000

" #ð6Þ

Annual wind energy:

Fig. 2. Scheme of RO process.

A. Hossam-Eldin et al. / Electrical Power and Energy Systems 43 (2012) 1393–1400 1395

EWT ¼ PWT � 8760 ð7Þ

The PV power:

PPV ¼ NPan �gPan � PankW

1000

� �ð8Þ

Annual PV energy

EPV ¼ PPV � 12 � 365 ð9Þ

Annual total renewable energy:

RET ¼ EWT þ EPV ð10Þ

Annual diesel generator energy

EG ¼ ERO � RET ð11Þ

Annual diesel generator operating hours

Goh ¼ EG=GPr ð12Þ

Annual total energy production:

EA:Tot ¼ RET þ EG ð13Þ

Annual excess energy:

EA:exc ¼ EA:Tot � EA:RO ð14Þ

3.3. Sizing battery system and inverter

Total daily load:

ID ¼PRO �U � 1000ffiffiffi

3p� VAC � cos u � gInv

ð15Þ

Total battery capacity

IBatt ¼ ID � DOA � DF ð16Þ

Required number of batteries:

NBatt: ¼ ROUNDIBatt

UIBatt

� �ð17Þ

Number of series batteries:

NSBatt: ¼ ROUNDVAC

VBatt

� �ð18Þ

3.4. Cost estimation

Annual capital cost:

CA:cap ¼ Ccap � CRF ð19Þ

where

CRF ¼ i � ð1þ iÞn

ð1þ iÞn � 1ð20Þ

Annual replacement cost:

CA:rep ¼Crep � Nrep

nð21Þ

where

Nrep ¼n� Ncomp:

Ncomp:ð22Þ

Annual O&M cost:Annual Operation and Maintenance (O&M) cost, (CA.O) is defined

as the yearly summation of individual (HRES) Components (O&M)cost, which is defined as the following:

COWT ¼ NWT � CO=WT ;

COPV ¼ 0;COG ¼ CO=G � GOh;

COBatt ¼ NBatt � CO=Batt; andCOInv ¼ NInv � CO=Inv

ð23Þ

Annual fuel cost:

CA:Fu ¼ FuSur � EG � CFu ð24Þ

Annual total cost:

CA:Tot ¼ CA:cap þ CA:rep: þ CA:O þ CA:Fu ð25Þ

Cost of energy:

CE ¼ CA:Tot=EA:Tot ð26Þ

Cost of water

CW ¼ SERO � CE ð27Þ

Flow chart of the used algorithms is shown in Fig. 3. The first stepinvolves the definition of input data (load specifications, cite mete-orological and HRES specifications, i.e. components, sizes andEconomics).

In the next step the main algorithms is performed which in-clude annual load demands, annual (renewable – diesel generator)energy production, sizing battery system and inverter and eco-nomic (financial) parameters. The amount of (HRES) componentsis compared to the annual load demands to insure that, the powergenerated by the system is able to meet the load demands. Finalstep shows the output data which includes the system Energyand Cost Shares in details.

4. Case studies

The software has been used to identify and design most appro-priate (RES) powered (RO) desalination plant for two case studiesof existing small and medium size plants powered with conven-tional grid supply. Four alternative combinations of (RES) havebeen evaluated for each case. The optimum combination that givesminimum unit cost of energy is optimized in order to minimize itsexcess energy. Necessary cost comparing and sensitivity isperformed.

The plants located on the west cost of Egypt (latitude: 31, lon-gitude: 027). Egypt demand of electricity projected to reach120 GW in 2050 [15]. Renewable energy is the favorable alterna-tive to fossil fuels especially because of Egypt’s the only place inthe world where both solar and wind potentials are available at ahigh quality [12]. Accordingly a renewable energy share of over50% till 2050 is considered as a realistic option [12,15,16]. Consid-ered RO plants and (HRES) data are as follow:

4.1. RO plants data

Plants specifications are shown in Table 1.

Cite Meteorological

Load Demand

Sizing (HRES) components and its

Economics

meeting load

Resizing Components and Specify Economics

Evaluate (Energy, Water and Component) costs and (Renewable, Total and Excess) Energy Production

No

Yes

Select the Optimum (Economical) system Sizing

Optimize (minimize) the system Excess Energy Check

Reserve Energy

No

Define Inputs Data Start

Display Outputs Data End Yes

Fig. 3. Flowchart of used algorithm.

1396 A. Hossam-Eldin et al. / Electrical Power and Energy Systems 43 (2012) 1393–1400

4.2. HRES data

4.2.1. Wind turbineThe used wind turbine kind is FL 250, has a Rotor diameter

96.8 m [14]. The average wind speed for the plants location is5.3 m/s [17]. Air density, coefficient of performance, generator effi-ciency and gearbox-bearings efficiency are about 1.2 kg/m3, 0.25,50%, 50% respectively. Turbine capital cost is $100,000 and itsreplacement at $100,000. Annual operation and maintenance costis 2% of capital cost. The turbine life time is 20 years.

4.2.2. Solar PV panelsThe PV panel power is 1 kW, panel efficiency is about 90%. The

monthly average daily solar radiation is 5 kW h/m2/day [17]. Themonthly average daily sunshine duration is assumed 12 h. Panelefficiency is 90%.

Panel capital cost is $4000 and its replacement at $4000. Annualoperation and maintenance cost is neglected. The turbine life timeis 20 years.

4.2.3. Diesel generatorThe AC generator capital cost is 400 $/kW and its replacement

cost is 400 $/kW. The operation and maintenance is 0.005 $/kWof operating hour. The lifetime of the generator is estimated at15,000 operating hours. Its efficiency is about 75%. Diesel is pricedat 0.3 $ per liter.

4.2.4. Battery systemThe valve regulated lead acid battery is rated at 12 V and has a

capacity 305 Ah. Battery capital cost is $450. The replacement costis $450. The annually operation and maintenance cost about 1% ofcapital cost. Design factor, days of autonomy is 1.25 and 5 respec-tively. The battery life time is 4 years.

4.2.5. InverterThe Inverter capital cost is 750 $/kW and its replacement cost is

700 $/kW. The operation and maintenance is 20% of capital cost. Itsefficiency is about 80%. Lifetime is 20 years.

4.2.6. Economics and constraintsThe calculations take into account the annual interest rate at 6%

and the (HRES) lifetime is 20 years. The operating reserve is set at10%, 15%and 25% of the load demands, out put wind and solarpower respectively.

5. Results and discussion

Based on effectiveness cost, several program runs were made todetermine the optimum (HRES) component sizing and its detailedcost analysis for each case study which derived the results shownin Tables 2 and 3. Graphical representations of optimum (HRES)cost analysis are shown in Fig. 4.

For case I, the optimum combination consists of four wind tur-bines, 125 kW diesel generator, 160 batteries and 125 kW inverterhave the lowest costs of energy, water at 0.1023 $/kW h and1.787 $/m3 which are about 62% and 19% higher if compared withthe current electricity tariff set by Egypt government at 0.0631 $/kW h [16], and unit cost of desalinated water at 1.6 $/m3. This com-bination can provide annually 1.207 � 106 kW h of energy withamount of excess energy about 33% of product energy.

For case II, the optimum combination consists of eight wind tur-bines, 125 PV Panels, 300 kW diesel generator, 416 batteries and300 kW inverter have the lowest costs of energy, water at0.1135 $/kW h and 1.403 $/m3 which are about 40% and 12% higherif compared with the current electricity tariff at 0.0807 $/kW h andunit cost of desalinated water at 1.25 $/m3. This combination canprovide annually 2.963 � 106 kW h of energy with amount ofexcess energy about 30% of produced energy.

Table 1Case studies RO plants techno-economic and meteorological specifications.

Title Case I (small scale plant) Case II (medium scale plant)

RO plant units number 2 4

RO unit dataUnit capacity (m3/d) 150 300Feed concentration (ppm) 33,000 34,000Pressure vessels number 4 5Membrane number per vessel 3 4Membrane type Filmtec Sw30-380 HR, 8’’

Pumps and energy recovery No. Q (m3/h)

P bar g% Power(kW)

No. Q (m3/h)

P bar g% Power(kW)

Feed pump 1 25 3 80 2.60 1 50 3 80 5.2High pressure pump 1 25 55.39 80 48.08 1 50 35 80 60.76Energy recovery 0 0 0 0 0 1 35 55 90 0Dosing pump 5 0.015 5 80 0.013 10 0.015 5 80 0.026Product water pump 1 15 3 80 1.562 2 30 3 80 3.12Auxiliary loads (kW) 2.73 0.871Total RO unit load (kW) 55 70Product water flow (m3/h) 7.5 15Product TDS (ppm) 350:450Daily operating hours (h) 20Electric energy supply 380–220 V, �AC, Grid Electricity

ConnectionPower consumption (kW) 110 280Daily energy consumption (kW h/d) 2200 5600Specific energy consump.(kW h/m3) 7.3 4.6

CostsUnit cost of product water ($/m3) 1.6 1.25Unit cost of energy ($/kW h) 0.0631 0.0807Specific unit cost of energy ($/m3) 0.463 0.376Energy to product water cost ratio

(%)30.87 30.12

Table 2Optimum (HRES) component sizing.

PV (kW) WT G (kW) Batt Inv (kW) EA.Tot � 106 (kW h/y) EExces% EA.Tot CE ($/kW h) CW ($/m3)

Case I0 4 125 160 125 1.207 33.48 0.1023 1.7871 4 125 160 125 1.211 33.702 0.1028 1.7900 0 125 0 0 1.606 16.66 0.18 2.356

110 0 125 160 125 1.081 25.77 0.208 2.562

Case II125 8 300 416 300 2.963 30.994 0.1135 1.403

0 8 300 416 300 2.871 27.431 0.1163 1.4160 0 300 0 0 2.452 16.66 0.1727 1.679

280 0 300 416 300 2.753 25.776 0.203 1.820

Table 3Optimum (HRES) cost analysis.

Component Capital Annual cost

Cost ($) Capital ($/y) Replacement ($/y) O&M ($/y) Fuel ($/y) Total ($/y)

Case IWT 400,000 34,873.82 0.00 400.00 0.00 35,273.82G 50,000 4,359.23 894.12 636.40 10,137.82 16,027.57Batt 72,000 6,277.29 14,400.00 800.00 0.00 21,477.29Inv 93,750 8173.55 0.00 1250.00 0.00 9423.55Totals 615,750 53,683.89 15,294.12 3086.40 10,137.82 82,202.23

Case IIWT 800,000 69,747.65 0.00 800.00 0.00 70,547.65PV 500,000 43,592.28 0.00 0.00 0.00 43,592.28G 120,000 10,462.15 2,743.76 1639.46 24,567.24 39,412.60Batt 187,200 16,320.95 37,440.00 2080.00 0.00 55,840.95Inv 225,000 19,616.53 0.00 3000.00 0.00 22,616.53Totals 1,832,200 159,739.55 40,183.76 7519.46 24,567.24 232,010.00

A. Hossam-Eldin et al. / Electrical Power and Energy Systems 43 (2012) 1393–1400 1397

Fig. 4. Cost analysis.

Table 4Optimization results for load demand increasing.

Lad/LRO (%) Eexc (%) CE ($/kW h) CE/UCE (%) CW ($/m3) CW/UCW (%)

Case I0 33.4800 0.1023 62.1236 1.7870 19.13535 30.1550 0.0975 54.5166 1.7518 16.788710 26.8280 0.0930 47.3851 1.7188 14.588815 23.5000 0.0890 41.0460 1.6895 12.633320 20.1770 0.0850 34.7068 1.6602 10.677925 16.5480 0.0815 29.1601 1.6345 8.966830 12.9200 0.0780 23.6133 1.6088 7.255835 10.0170 0.0756 19.8098 1.5912 6.0825

Case II0 31 0.1135 40.6444 1.4030 12.24295 27.5 0.1081 33.9529 1.3778 10.227210 24.19 0.1031 27.7571 1.3545 8.360815 20.64 0.0987 22.3048 1.3340 6.718420 17.19 0.0945 17.1004 1.3144 5.150625 14.23 0.0913 13.1351 1.2995 3.956130 10.09 0.0871 7.9306 1.2799 2.3883

1398 A. Hossam-Eldin et al. / Electrical Power and Energy Systems 43 (2012) 1393–1400

Table 3 shows the annualized cost analysis for optimum (HRES).For case I components, WT, generator, battery and inverter costscontribute about 44%, 19%, 26% and 11% respectively of the totalannual cost of $82,202.23. while case II components, WT, PV, gen-erator, battery and inverter costs contribute about 30%, 19%,17%,24% and 10% respectively of the total annual cost of $232,010.00as shown in Fig. 4.

The costs of WT and Battery play important part in determiningcost of energy due to the fact that WT is the main power compo-nent in the system which directly affects the capital cost. Batteriesneed to be replaced after predetermined time which mean thatcertain number of batteries has to be replaced which directly af-fects the replacement cost.

Table 4 shows the optimization approach by increasing the loaddemand in order to minimize the amount of excess energy; foreach case optimum (HRES) subjected to the excess energy mustnot less than 10% of total energy production (which represents areserve) to insure that there is no unmeet load in system.

Fig. 5 shows that if case I and case II have a load demand in-crease 35% and 30% respectively, the excess energy will decreaseby about 23% and 21% respectively of the total energy production)and the cost of energy is reduced to be $0.0756 and $0.0871 whichindicate an improvement of 42.3% and 32.7% respectively. Conse-quently the cost of water is reduced to $1.591and $1.279 whichillustrate a cost reduction of 13% and 9.9% of the initial cost.

It is clear that case II is more economical than case I. This is dueto the fact that case II is medium scale RO plant while case I is a

small one. The Sensitivity of the components costs for case II isshown in Fig. 6.

If (due to the effort of researches) a reduction of 10% in the costof WT and, keeping the cost of other components constants thecost of energy unit will be $0.083 while a more reduction of 20%will result in the cost of energy unit will be $0.0803 which muchbetter than initial unit cost of energy. A more reduction in WT costof 30% will cause more reduction to reach $0.0769/kW h.

Repeating the same procedures for PV, Battery, WT plus PV, it isclear that a general reduction will occur but it still not economical.On the contrary the (HRES) with WT, PV and Battery will prove tobe economical.

If the calculation were repeated for a reduction of 20% it indi-cates that WT, PV plus WT and (HRES) of (PV + Batt + WT) will bemuch more economical. The more reduction in cost of 30% willshow that all systems are economical with the HRES(PV + Batt + WT) is effectively economical than all other cases.

If presumably, the cost of electricity has been increased by 10%over its current prices, as it may be forecasted now days, the devel-oped hybrid renewable energy systems will show superiority in itscost and less pollution which reflects on a drastic improve in envi-ronmental conditions. For this reason we are urging the scientiststo work hard on the research to reduce the cost of RES.

6. Conclusions

Main extracted conclusions from this work are:

Fig. 5. Optimization approach effect on product energy and water costs.

Fig. 6. Sensitivity of components cost for case 2.

A. Hossam-Eldin et al. / Electrical Power and Energy Systems 43 (2012) 1393–1400 1399

� From a techno economic point of view, sizing (RES) power sup-ply options in combination with RO depends on several condi-tions such as renewable energy sources, available componentssizes and prices on market as well as the design constraintsconsiderations.� From that point of view, it is hardly define straightforward way

to select the favorable system design for general application andan iterative approach is most probable to be followed, involvingcareful assessment of available options in meeting waterdemand and the economic viability of the selected solution.

� Wind energy is a best choice in the present days however, solarenergy is essential for the future.� The costs of WT and Battery play important part in determining

cost of energy due to WT is the main power component in thesystem which directly effect in capital cost and battery needsto be replaced certain number which directly effect in replace-ment cost.� Optimization of (HRES) is essential issue for reasonable cost of

energy produced and in most cases dependent on the locationof renewable resources and the specific system design.

1400 A. Hossam-Eldin et al. / Electrical Power and Energy Systems 43 (2012) 1393–1400

� In order to reduce the cost of energy production using (HRES) it isimportant to minimize the amount of excess energy the systemproduce. As a result depicts, reduction of 20% excess energy wouldhave about one and half effect on the cost of produced energy.� The use of (HRES) is more appropriate for medium scale RO

Plants than small scale ones in countries with same Egyptianconditions.� For medium scale (RO) plants, elementary reduction of 20%, 25%

and 30% or gathering one with 10% of WT, Battery and PV panelprices would give (RES) the economic priority in comparisonwith conventional grid supply. The same result can be achievedwith 10% increase of electrical energy tariff.� Due to advances in renewable energy technologies to drive the

prices down, subsequent rise in prices of petroleum productsand depleting reserves. Economic aspects of these technologiesare sufficiently promising to include them in power and waterproduction for developing countries.� In spite of cost and technological development of (HRES) in

recent years has been encouraging, they remain an expensivesource of power and mainly used for remote area power appli-cations and are now a days cost-effective where extension ofgrid supply is expensive.

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