Investigation into economical desalination using optimized hybrid renewable energy system

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

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Investigation into economical desalination using optimized hybrid renewable energy system A. Hossam-Eldin ⇑, A.M. El-Nashar, A. Ismaiel Electrical Engineering Department, Alexandria University, Alexandria, Egypt

a r t i c l e

i n f o

Article history: Received 9 May 2009 Received in revised form 11 May 2012 Accepted 14 May 2012 Available online 2 August 2012 Keywords: Optimization Hybrid Renewable Energy Desalination Economics

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 of excess 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 investigated optimization approach based on minimization of the excess energy. It presents the impact of the considered optimization technique on the unit cost of energy and consequently unit cost of desalinated water. 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 from optimized (HRES). Cost sensitivity evaluation for (HRES) components to estimate the most economical price of (HRES) for desalination is presented. Ó 2012 Elsevier Ltd. All rights reserved.

1. Introduction Hybrid Renewable Energy Sources (HRESs) is defined as a combination of one or more resources of renewable energy. It represents one of the promising options for the considerable energy needs of desalination processes especially in remote and arid regions, where the use of conventional energy (fossil fuels, electricity) is costly or not available. HRES powered desalination plants may be an attractive alternative option. In most cases, fresh water scarcity co-exists with abundant renewable energy (RE) resources. RO desalination processes have been the technology of choice as a result of recent technological developments in the process engineering. The average costs of product water have decreased significantly [1,2]. The main desirable features for renewable energy systems (RESs) are low cost. The selection of the optimum combination of RES and desalination technologies for a specific location is based on resource availability and the technical compatibility [2]. Numerous RES–RO combinations have been identified by several researches [3–8]. Economic aspects of these technologies are sufficiently promising to include them in developing power generation capacity for developing countries and there is still need for more effort to be done so that the system can be optimized. Several studies have been done demonstrating the ability to optimize hybrid configurations of renewable energy systems in order to maximize performance while minimizing cost [9–14].

However while the results of these optimization processes show the optimum sizing and appropriate combination of components for the system, but the problem of maximum component capacity must be taken in consideration in order to overcoming the existence of high excess energy [9]. This paper discusses the importance of reducing excess energy in minimizing the cost of energy (CE) for RES which defined as the ratio of total annualized cost and annual load served by the renewable energy hybrid system. Suggestion of maximizing load reserve for two case studies is considered to meet this optimization objective, consequently produce both energy and water at reasonable cost. 2. Methodology The proposed (HRES) as shown in Fig. 1 it is consisted of a wind turbine (WT) and solar photovoltaic panels (PV). Diesel generator (G), battery (Batt) and inverter (Inv) are added as part of back-up and storage system. Scheme of the RO plant (load demand) is shown in Fig. 2. 3. Mathematical modeling The developed computer program algorithms are based on the following equations: 3.1. Energy consumption (load demand)

⇑ Corresponding author. E-mail address: [email protected] (A. Hossam-Eldin). 0142-0615/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ijepes.2012.05.019

The pump delivered power:

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Nomenclature Notation Ann As CA.Cap CA.Fu CA.O CA.rep CA.Tot Ccap CE CFu CO/Batt CO/G CO/Inv CO/WT COBatt COG COInv COPV COWT Cp Crep Cw EA.Exc EA.RO EA.Tot EG EPV ERO EWT FUSur GOh GP i IBatt ID n NBatt Ncomp NG NInv Npan

Nrep NSBatt NURO NWT P PankW PDP PFP PG PHPP Ppumb PPV PRO PWT Q Qp RET SERO UIBatt VAC Vbatt Vw

Explanation annual swept area (m2) ann. capital cost ($/y) ann. fuel cost ($/y) ann. O&M cost ($/y) ann. replacement cost ($/y) ann. total cost ($/y) capital cost ($) cost of energy ($/kW h) fuel cost ($/L) ann. battery O&M cost ($/y) ann. generator O&M cost ($/y) ann. inverter O&M cost ($/y) ann. WT O&M cost ($/y) total battery O&M cost ($/y) total generator O&M cost ($/y) total inverter O&M cost ($/y) total PV O&M cost ($/y) total WT O&M cost ($/y) coefficient of performance replacement cost ($) cost of product water ($/m3) ann. excess energy (kW h/y) ann. consump. energy (kW h/y) ann. total energy (kW h/y) ann. generator energy (kW h/y) ann. PV energy (kW h/y) ann. RO plant energy (kW h/y) ann. wind energy (kW h/y) specific Fu. usage rate (L/kW h) ann. generator operating hours (h) generator rated power (kW) interest rate total battery capacity (Ah) total daily load (Ah) number of years required number of battery number of components required number of generators required number of inverters required number of PV panels

PPumb ¼ 0:02278  P  Q =%gPumb

number of replacements number of battery in series number of RO units number of WT’s pressure (bar), power (kW) PV panel power (kW) dosing chemicals pump power (kW) feed water pump power kW) generator power (kW) high pressure pump power (kW) pump delivered power (kW) PV power (kW) RO plant power (kW) wind power (kW) flow rate (m3/h) product water flow rate (m3/h) ann. total RE (kW h/y) specific energy consumption (kW h/m3) unit battery capacity (Ah) AC voltage (volt) battery DC voltage (volt) average wind velocity (m/s) qa air density (kg/m3) gg WT generator efficiency gPan PV panel efficiency gPumb pump efficiency gWT WT efficiency Batt battery cosu power factor CRF capital recovery factor DF design factor DOA days of autonomy U daily operating hours (h) G generator HRES hybrid renewable energy system Inv inverter No. number PV photovoltaic RES renewable energy system RO reverse osmosis ROUND round to nearest integer WT wind turbine

ð1Þ

The RO plant power:

PRO ¼ ½PFP þ PHPP þ

X

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 P RO  U  365

ð5Þ Fig. 1. HRES configuration.

3.2. HRES production The wind power:

" PWT ¼ NWT 

gW  gg  qa  C p  AS  V 3W

Annual wind energy:

2  1000

# ð6Þ

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

C A:rep ¼

C rep  Nrep n

1395

ð21Þ

where

Nrep ¼

ð7Þ

The PV power:

 PPV ¼ NPan 

gPan  PankW

ð8Þ

Annual PV energy

EPV ¼ PPV  12  365

ð11Þ

Annual diesel generator operating hours

Goh ¼ EG =GPr

ð12Þ

ð13Þ

Annual excess energy:

EA:exc ¼ EA:Tot  EA:RO

ð14Þ

3.3. Sizing battery system and inverter Total daily load:

PRO  U  1000 ID ¼ pffiffiffi 3  V AC  cos u  gInv

C OInv ¼ N Inv  C O=Inv

C A:Fu ¼ FuSur  EG  C Fu

C A:Tot ¼ C A:cap þ C A:rep: þ C A:O þ C A:Fu

ð24Þ

ð15Þ

ð25Þ

Cost of energy:

C E ¼ C A:Tot =EA:Tot

ð26Þ

Cost of water

C W ¼ SERO  C E

Annual total energy production:

EA:Tot ¼ RET þ EG

ð23Þ

C OBatt ¼ NBatt  C O=Batt ; and

Annual total cost:

ð10Þ

Annual diesel generator energy

EG ¼ ERO  RET

C OPV ¼ 0;

Annual fuel cost:

ð9Þ

Annual total renewable energy:

RET ¼ EWT þ EPV

C OWT ¼ NWT  C O=WT ; C OG ¼ C O=G  GOh ;



1000

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:

Fig. 2. Scheme of RO process.

EWT ¼ PWT  8760

n  Ncomp: N comp:

ð27Þ

Flow chart of the used algorithms is shown in Fig. 3. The first step involves the definition of input data (load specifications, cite meteorological and HRES specifications, i.e. components, sizes and Economics). In the next step the main algorithms is performed which include annual load demands, annual (renewable – diesel generator) energy production, sizing battery system and inverter and economic (financial) parameters. The amount of (HRES) components is compared to the annual load demands to insure that, the power generated by the system is able to meet the load demands. Final step shows the output data which includes the system Energy and Cost Shares in details.

Total battery capacity

IBatt ¼ ID  DOA  DF

ð16Þ

Required number of batteries:

NBatt:

  IBatt ¼ ROUND UIBatt

ð17Þ

Number of series batteries:

  V AC NSBatt: ¼ ROUND V Batt

ð18Þ

3.4. Cost estimation Annual capital cost:

C A:cap ¼ C cap  CRF

ð19Þ

where

CRF ¼

i  ð1 þ iÞn ð1 þ iÞn  1

Annual replacement cost:

4. Case studies The software has been used to identify and design most appropriate (RES) powered (RO) desalination plant for two case studies of existing small and medium size plants powered with conventional grid supply. Four alternative combinations of (RES) have been evaluated for each case. The optimum combination that gives minimum unit cost of energy is optimized in order to minimize its excess energy. Necessary cost comparing and sensitivity is performed. The plants located on the west cost of Egypt (latitude: 31, longitude: 027). Egypt demand of electricity projected to reach 120 GW in 2050 [15]. Renewable energy is the favorable alternative to fossil fuels especially because of Egypt’s the only place in the world where both solar and wind potentials are available at a high quality [12]. Accordingly a renewable energy share of over 50% till 2050 is considered as a realistic option [12,15,16]. Considered RO plants and (HRES) data are as follow:

ð20Þ 4.1. RO plants data Plants specifications are shown in Table 1.

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Start

Define Inputs Data

Sizing (HRES) components and its Economics

Cite Meteorological

Yes

Load Demand

meeting load No

Resizing Components and Specify Economics

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

No Check Reserve Energy Yes

Select the Optimum (Economical) system Sizing

Optimize (minimize) the system Excess Energy

Display Outputs Data

End

Fig. 3. Flowchart of used algorithm.

4.2. HRES data 4.2.1. Wind turbine The used wind turbine kind is FL 250, has a Rotor diameter 96.8 m [14]. The average wind speed for the plants location is 5.3 m/s [17]. Air density, coefficient of performance, generator efficiency and gearbox-bearings efficiency are about 1.2 kg/m3, 0.25, 50%, 50% respectively. Turbine capital cost is $100,000 and its replacement at $100,000. Annual operation and maintenance cost is 2% of capital cost. The turbine life time is 20 years.

4.2.2. Solar PV panels The PV panel power is 1 kW, panel efficiency is about 90%. The monthly average daily solar radiation is 5 kW h/m2/day [17]. The monthly average daily sunshine duration is assumed 12 h. Panel efficiency is 90%. Panel capital cost is $4000 and its replacement at $4000. Annual operation and maintenance cost is neglected. The turbine life time is 20 years.

4.2.3. Diesel generator The AC generator capital cost is 400 $/kW and its replacement cost is 400 $/kW. The operation and maintenance is 0.005 $/kW of operating hour. The lifetime of the generator is estimated at 15,000 operating hours. Its efficiency is about 75%. Diesel is priced at 0.3 $ per liter.

4.2.4. Battery system The valve regulated lead acid battery is rated at 12 V and has a capacity 305 Ah. Battery capital cost is $450. The replacement cost is $450. The annually operation and maintenance cost about 1% of capital cost. Design factor, days of autonomy is 1.25 and 5 respectively. The battery life time is 4 years.

4.2.5. Inverter The Inverter capital cost is 750 $/kW and its replacement cost is 700 $/kW. The operation and maintenance is 20% of capital cost. Its efficiency is about 80%. Lifetime is 20 years. 4.2.6. Economics and constraints The calculations take into account the annual interest rate at 6% and the (HRES) lifetime is 20 years. The operating reserve is set at 10%, 15%and 25% of the load demands, out put wind and solar power respectively.

5. Results and discussion Based on effectiveness cost, several program runs were made to determine the optimum (HRES) component sizing and its detailed cost analysis for each case study which derived the results shown in 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 turbines, 125 kW diesel generator, 160 batteries and 125 kW inverter have the lowest costs of energy, water at 0.1023 $/kW h and 1.787 $/m3 which are about 62% and 19% higher if compared with the current electricity tariff set by Egypt government at 0.0631 $/ kW h [16], and unit cost of desalinated water at 1.6 $/m3. This combination can provide annually 1.207  106 kW h of energy with amount of excess energy about 33% of product energy. For case II, the optimum combination consists of eight wind turbines, 125 PV Panels, 300 kW diesel generator, 416 batteries and 300 kW inverter have the lowest costs of energy, water at 0.1135 $/kW h and 1.403 $/m3 which are about 40% and 12% higher if compared with the current electricity tariff at 0.0807 $/kW h and unit cost of desalinated water at 1.25 $/m3. This combination can provide annually 2.963  106 kW h of energy with amount of excess energy about 30% of produced energy.

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Case I (small scale plant)

Case II (medium scale plant)

RO plant units number

2

4

RO unit data Unit capacity (m3/d) Feed concentration (ppm) Pressure vessels number Membrane number per vessel Membrane type

150 33,000 4 3

300 34,000 5 4

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 High pressure pump Energy recovery Dosing pump Product water pump Auxiliary loads (kW) Total RO unit load (kW) Product water flow (m3/h) Product TDS (ppm) Daily operating hours (h) Electric energy supply

1 1 0 5 1

25 25 0 0.015 15

3 55.39 0 5 3 2.73 55 7.5

80 80 0 80 80

2.60 48.08 0 0.013 1.562

1 1 1 10 2

50 50 35 0.015 30

3 35 55 5 3 0.871 70 15

80 80 90 80 80

5.2 60.76 0 0.026 3.12

350:450 20 380–220 V, AC, Grid Electricity Connection

Power consumption (kW) Daily energy consumption (kW h/d) Specific energy consump.(kW h/m3)

110 2200 7.3

280 5600 4.6

Costs Unit cost of product water ($/m3) Unit cost of energy ($/kW h) Specific unit cost of energy ($/m3) Energy to product water cost ratio (%)

1.6 0.0631 0.463 30.87

1.25 0.0807 0.376 30.12

Table 2 Optimum (HRES) component sizing. WT

G (kW)

Batt

Inv (kW)

EA.Tot  106 (kW h/y)

EExces% EA.Tot

CE ($/kW h)

CW ($/m3)

Case I 0 1 0 110

4 4 0 0

125 125 125 125

160 160 0 160

125 125 0 125

1.207 1.211 1.606 1.081

33.48 33.702 16.66 25.77

0.1023 0.1028 0.18 0.208

1.787 1.790 2.356 2.562

Case II 125 0 0 280

8 8 0 0

300 300 300 300

416 416 0 416

300 300 0 300

2.963 2.871 2.452 2.753

30.994 27.431 16.66 25.776

0.1135 0.1163 0.1727 0.203

1.403 1.416 1.679 1.820

PV (kW)

Table 3 Optimum (HRES) cost analysis. Component

Capital Cost ($)

Annual cost Capital ($/y)

Replacement ($/y)

O&M ($/y)

Fuel ($/y)

Total ($/y)

Case I WT G Batt Inv Totals

400,000 50,000 72,000 93,750 615,750

34,873.82 4,359.23 6,277.29 8173.55 53,683.89

0.00 894.12 14,400.00 0.00 15,294.12

400.00 636.40 800.00 1250.00 3086.40

0.00 10,137.82 0.00 0.00 10,137.82

35,273.82 16,027.57 21,477.29 9423.55 82,202.23

Case II WT PV G Batt Inv Totals

800,000 500,000 120,000 187,200 225,000 1,832,200

69,747.65 43,592.28 10,462.15 16,320.95 19,616.53 159,739.55

0.00 0.00 2,743.76 37,440.00 0.00 40,183.76

800.00 0.00 1639.46 2080.00 3000.00 7519.46

0.00 0.00 24,567.24 0.00 0.00 24,567.24

70,547.65 43,592.28 39,412.60 55,840.95 22,616.53 232,010.00

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Fig. 4. Cost analysis.

Table 4 Optimization results for load demand increasing. Lad/LRO (%)

Eexc (%)

CE ($/kW h)

CE/UCE (%)

CW ($/m3)

CW/UCW (%)

Case I 0 5 10 15 20 25 30 35

33.4800 30.1550 26.8280 23.5000 20.1770 16.5480 12.9200 10.0170

0.1023 0.0975 0.0930 0.0890 0.0850 0.0815 0.0780 0.0756

62.1236 54.5166 47.3851 41.0460 34.7068 29.1601 23.6133 19.8098

1.7870 1.7518 1.7188 1.6895 1.6602 1.6345 1.6088 1.5912

19.1353 16.7887 14.5888 12.6333 10.6779 8.9668 7.2558 6.0825

Case II 0 5 10 15 20 25 30

31 27.5 24.19 20.64 17.19 14.23 10.09

0.1135 0.1081 0.1031 0.0987 0.0945 0.0913 0.0871

40.6444 33.9529 27.7571 22.3048 17.1004 13.1351 7.9306

1.4030 1.3778 1.3545 1.3340 1.3144 1.2995 1.2799

12.2429 10.2272 8.3608 6.7184 5.1506 3.9561 2.3883

Table 3 shows the annualized cost analysis for optimum (HRES). For case I components, WT, generator, battery and inverter costs contribute about 44%, 19%, 26% and 11% respectively of the total annual cost of $82,202.23. while case II components, WT, PV, generator, battery and inverter costs contribute about 30%, 19%,17%, 24% and 10% respectively of the total annual cost of $232,010.00 as shown in Fig. 4. The costs of WT and Battery play important part in determining cost of energy due to the fact that WT is the main power component in the system which directly affects the capital cost. Batteries need to be replaced after predetermined time which mean that certain number of batteries has to be replaced which directly affects the replacement cost. Table 4 shows the optimization approach by increasing the load demand in order to minimize the amount of excess energy; for each case optimum (HRES) subjected to the excess energy must not less than 10% of total energy production (which represents a reserve) to insure that there is no unmeet load in system. Fig. 5 shows that if case I and case II have a load demand increase 35% and 30% respectively, the excess energy will decrease by about 23% and 21% respectively of the total energy production) and the cost of energy is reduced to be $0.0756 and $0.0871 which indicate an improvement of 42.3% and 32.7% respectively. Consequently the cost of water is reduced to $1.591and $1.279 which illustrate 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 due to 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 is shown in Fig. 6. If (due to the effort of researches) a reduction of 10% in the cost of WT and, keeping the cost of other components constants the cost 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 much better than initial unit cost of energy. A more reduction in WT cost of 30% will cause more reduction to reach $0.0769/kW h. Repeating the same procedures for PV, Battery, WT plus PV, it is clear that a general reduction will occur but it still not economical. On the contrary the (HRES) with WT, PV and Battery will prove to be economical. If the calculation were repeated for a reduction of 20% it indicates that WT, PV plus WT and (HRES) of (PV + Batt + WT) will be much more economical. The more reduction in cost of 30% will show 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 developed hybrid renewable energy systems will show superiority in its cost and less pollution which reflects on a drastic improve in environmental conditions. For this reason we are urging the scientists to work hard on the research to reduce the cost of RES. 6. Conclusions Main extracted conclusions from this work are:

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

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

 From a techno economic point of view, sizing (RES) power supply options in combination with RO depends on several conditions such as renewable energy sources, available components sizes and prices on market as well as the design constraints considerations.  From that point of view, it is hardly define straightforward way to select the favorable system design for general application and an iterative approach is most probable to be followed, involving careful assessment of available options in meeting water demand and the economic viability of the selected solution.

 Wind energy is a best choice in the present days however, solar energy 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 the system which directly effect in capital cost and battery needs to be replaced certain number which directly effect in replacement cost.  Optimization of (HRES) is essential issue for reasonable cost of energy produced and in most cases dependent on the location of renewable resources and the specific system design.

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 In order to reduce the cost of energy production using (HRES) it is important to minimize the amount of excess energy the system produce. As a result depicts, reduction of 20% excess energy would have 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 Egyptian conditions.  For medium scale (RO) plants, elementary reduction of 20%, 25% and 30% or gathering one with 10% of WT, Battery and PV panel prices would give (RES) the economic priority in comparison with conventional grid supply. The same result can be achieved with 10% increase of electrical energy tariff.  Due to advances in renewable energy technologies to drive the prices down, subsequent rise in prices of petroleum products and depleting reserves. Economic aspects of these technologies are sufficiently promising to include them in power and water production for developing countries.  In spite of cost and technological development of (HRES) in recent years has been encouraging, they remain an expensive source of power and mainly used for remote area power applications and are now a days cost-effective where extension of grid supply is expensive.

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