Designing Cost-Effective Sea Water Reverse Osmosis System under ...

Proceedings of the International Conference on Renewable Energy for Developing Countries-2006

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Designing Cost-Effective Sea Water Reverse Osmosis System under Optimal Energy Options for Developing Countries

Asmerom M. Gilau and Mitchell J. Small

Carnegie Mellon University, Engineering & Public Policy/ H. John Heinz III Professor of Environmental Engineering, Carnegie Mellon University, Civil & Environmental

Engineering and Engineering & Public Policy

Abstract Today, three billion people around the world have no access to clean drinking water and about 1.76 billion people live in areas already facing a high degree of water stress. This paper analyzes the cost-effectiveness of a stand alone small-scale renewable energy powered sea water reverse-osmosis (SWRO) system for developing countries. In this paper, we have introduced a new methodology; an energy optimization model which simulates hourly power production from renewable energy sources. Using the results of the model, we have computed hourly water production for a two stage SWRO system with a capacity of 35m3/day. According to our results, specific energy consumption is about 2.33kWh/m3, which is a lower value than that achieved in most of the previous designs. Using a booster pump, energy recovery turbine and appropriate membrane, specific energy consumption could be decreased by about 70%. Furthermore, the energy recovery turbine could reduce water cost by about 41%. Still, power cost is the major component of the total investment constituting about 80% of the total cost of the SWRO system. Our results show that, wind powered system is the cheapest and a PV powered system, the most expensive, with about 0.50$/m3 and 1.00$/m3, respectively. By international standards, for example, in China, these values are considered economically feasible. Detailed simulations of RO system design, energy options, power and water costs, and life cycle analysis are discussed. _________________________________

Key Words: Reverse osmosis; Energy recovery; Optimal energy options; Energy storage;

Power Cost; Water Cost

1. Background

Today, about three billion people around the world have no acess to clean drinking water. According to the World Water Council, by 2020, the world will be about 17 percent short of the water to feed the world population. Moreover, about 1.76 billion people live in areas already facing a high degree of water stress (Vörösmarty et. al, 2001). "Water stress" is on the top of the worlds’ agenda at least as firmly as climate change (Vaknin, 2005). As a result, the need for desalination is increasing, even in regions where water supply is adequate.

As part of the most affected arid areas of the world, Eritrea has been the victim of recurrent droughts and water shortage. The problems of water supply vary from place to place and one of the most severe problems exists in the coastal areas and islands (MLWE, 1995). Thus, the case study will asses the use of renewable energy for sea water reverse osmosis for the coastal village of Beraso’el, located at the Southern Red Sea, Eritrea.


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Sea water desalination is an energy-intensive process (Gaparini, 1985; Carta, et. al 2003). Most of the available large scale desalination plants around the world are powered by fuel. Due to the concern of global warming, and increasing fuel costs alternative energy sources has been proposed for desalination purposes. For example, the International Atomic Energy Agency (IAEA) has proposed to use nuclear power for large scale desalination plants (Carta et. al. 2003), and the use of renewable energy sources for small scale desalination plants is emerging. Autonomous wind powered sea water reverse osmosis potentials has been studied (Feron 1985; Marcos et.al. 2002) and pilot projects are in progress (Carta et. al. 2003). However, the application of renewable energy for desalination has not yet reached sufficient maturity to be applied widely. Moreover, most of the proposed designs are connected to the conventional power grids (Sultan et. al. 2002, Carta et. al. 2003). Thus, the main objective of this research is to design a cost-effective reverse osmosis system, which functions under optimal energy options. This paper introduces a new methodology of an energy optimization model for the sea water reverse osmosis (SWRO) system. In the analysis, two major models are applied, namely Reverse Osmosis System Analysis (ROSA)1, a sophisticated reverse osmosis (RO) design program that predicts the performance of membranes in user specified systems, and HOMER2, an energy optimization model for hybrid as well as stand alone power systems. Life cycle analyses are performed to examine the performance of the system, determine water costs and undertake comparative analysis of different options.

2. Situation Analysis

2.1 Water Demand: Recent studies conduced by the Swiss Federal Institute for Environmental Science and Technology (2003) indicate that East African countries have renewable freshwater resources below the calculated threshold of 1500 m3 per capita year. Eritrea is already in water deficits. According to the recent studies, in the coastal parts of the country water demand is 116m3/household/year (Marie and Pedersen, 2001). Thus, for the Beraso’el village of 108 households, we are assuming an average water demand of 35m3/day and 13,000m3/year.

2.2. Energy Supply: In Eritrea, there is a strong potential for wind-powered electricity generation (Habtesion et. al. , 2001) and sunshine is abundant. The Southeast coast of Eritrea has as much as 100 - 200 kilometres of 6 and 7 wind classes. At these sites, wind turbines may operate at a 40% - 60% capacity factor (Habtetsion and Tsighe, 2002), which implies that the wind potential ranges from excellent to exceptional (African Development Bank, 2004), and could be potentially used for commercial and industrial purposes as well. The average annual wind speed and solar radiation are about 6.8m/s and 6.8kWh/m2, respectively.

3. Modeling Sea Water Reverse Osmosis Design and Optimal Energy Options

3.1 Sea Water Reverse Osmosis Design Considerations

3.1.1 Model Description: ROSA 6.0.1 software is the latest version, used in the analysis in order to determine the performance of a membrane and energy requirements for desalination. The use of the model is influenced by the need to design a technically feasible RO system. We have 1 ROSA is developed by the DOW chemical company. 2 Homer is developed by the National Renewable Energy Laboratory (NRWL).


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extended the application of the model in creating an operating window for a RO system that could operate under intermittent power supply. This is done by running the model multiple times under different water flow and pressures. The main inputs of the model include the amount of feed water and its chemical characteristics, feed water flow rate, feed water and concentrate feed pressures, temperature and pH. Then a configuration of the number of membranes, pressure vessels, and type of membrane, and feed and booster pumps is determined. After performing calculations, the model provides the amount of water produced and energy required. The energy required to produce an intended amount of drinking water with acceptable water quality is then determined by running the model multiple times. Booster pumps and an energy recovery turbine are applied.

3.1.2 RO System Design and Energy Consumption: Using ROSA, we have performed several RO design options capable of producing 35m3/day potable water. After performing several design alternatives, our preferred design is a two stage design with three membrane elements in each stage (figure 1). The reasons for choosing a two stage system is in order to increase water productivity by applying a booster pump, and recover a significant amount of energy. The type of the membrane used in the analysis is SW30HRLE-400. The membrane is designed to properly function under intermittent energy supply (Dow, 2005).

High pressurepump (Booster)

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Producttank

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Seawater Reverse Osmosis Desalination System for Beraso'ele Village, Southern Red Sea, Eritrea

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Figure 1: Schematic double stage RO system for the village of Beraso’el, Southeastern Red Sea, Eritrea.

In reverse osmosis desalination systems, energy is a major consideration. Power consumption by the system which includes power for sea water pumping, high pressure pumping, booster, and chemical treatment could be calculated using equation 1(Darwish et. al. 2002).

n

nn

EQ PrPw n

×= (1)

Where, Pwn(kW) = Power consumed by feed, low and high pressure, booster and chemical water treatment pumps,

Qn(m3/s) = Rates of feed water(Q1), fresh water production (Q5 +Q6), boosted water(Q3), Prn(kPa) = Feed pressure(Pr1), boosted pressure (Pr3), rejection pressure (Pr2 and Pr4),and

En (Net efficiency of feed pump) = Ep (pump efficiency) x En(motor efficiency) for high pressure pump (booster) and energy recovery turbine.


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According to Darwish et. al (2002) the low pressure pump consumes the highest energy(Pw1), and the rest constitutes about 20% of the LP pump. The power required for the system’s LP pump, at 10m3/h feed water flow rate, 45bar pressure, and 0.85 pump efficiency, is about 14.71kW. An additional 2.94kW will be needed for booster, feed water, chemical treatment and other pumps, which is about 20% of the LP power requirement. Thus, the total power required for the RO system design is about 17.64kW. Using ROSA software, we have obtained a similar result, which is 17.23kW. The specific work done is about 3.92kWh/m3 potable water produced. Without a booster pump, the system requires about 7.87kWh/ m3, and its water quality deteriorates from 270 ppm to 800 ppm total dissolved solids. Moreover, if the booster pump is not applied, water production in the second stage could decrease by about 33% per hour i.e. from 2.1 m3/hr to 1.4m3/hr. Thus, in terms of water production, water quality, and energy recovery, a two stage RO design is a preferable. The system design has an average conversion factor of 55% (relation between product water flow and feed water flow), producing about 4.4m3/hr of potable water.

3.1.3 Performance Prediction: In designing the SWRO system that uses intermittent energy sources, it is very important to design a RO system that could operate under broad operational windows. The main thresholds of the operational window include the maximum feed pressure (determined by the membrane mechanical resistance); maximum brine flow rate (should not be exceeded to avoid membrane deterioration); minimum brine flow rate (should be maintained to avoid precipitation and consequent membrane fouling); and maximum product concentration (if the applied pressure is less than a determined value, the permeate concentration will be too high) (Marcos et. al, 2002). Using chemical characteristics of water of the study area (Thomson et. al., 2001), and varying the values of variables of operational window thresholds, we have run the model several times. According to the results of the analysis, at 250C, the maximum allowable pressure, maximum brine flow rate, minimum feed flow rate, and minimum pressure of our design are about 50 bar, 16m3/h, 7m3/h, and 30 bar, respectively (figure 2a).

Water quality and flowrate threshholds of the SWRO design

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Figure 2. (a) Water quality and feed water flow rate, (b) power thresholds of the SWRO system.

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This design allows the production of potable water with an average water quality of about 500 ppm total dissolved solids or less, which is the World Health Organization (WHO)’s water quality acceptable standard. Likewise, in order to operate under this operational window, the energy supply or pumping power should not be less than 7 kW and not exceed 26 kW (figure 2b). This means that, without interrupting the operation of the system, it can operate at as low as 7kW and as high as 26kW power supply, which is a wide operating window. This is one of the aspects that could potentially make renewable energy resources for SWRO systems more attractive.

The average conversion factor of the system, at 250C, is about 55%. Carta et. al.(2003) indicated that for an increase of 10C, water production increases by about 4%. Thus, since the climatic condition of the study area is very hot, with average monthly temperatures varying from 26.50C to 35.50C (Buskirk, 1998), it is expected that, most of the time of the year; the conversion factor could reach as high as 70%. 3.1.4 Energy recovery: Energy recovery should be considered if brine exits the system at 300 psig or more, and if system recovery is less than 80 % (Amjad, Z. 1993). Since the brine rejection pressure of our design is well above 300 psig, such as 760 psi (52.82 bar), the potential for energy recovery is very high. For example, using an energy recovery turbine with an efficiency of 0.85, at a concentrate pressure of 52.82 bar, and concentrate water flow of 5.6m3/hr, about 6.98kW (equation 2) energy could be recovered. Thus, our design can potentially reduce power consumption by half, from 15kW to about 9kW.

` tnn EQ ××= PrPw n ( 2 )

Where, Et is turbine efficiency, Prn(kPa) is the feed pressure, and Qn(m3/s) is rates of the feed brine.

The type of energy recovery turbine under consideration is the pressure exchange (PX) turbine, PX45s (Energy Recovery Inc. Data Sheet, 2005). Using this energy recovery device, net energy consumption could be reduced from 17.23kW to 10.25kW, and specific energy consumption from 3.92kWh/m3 to 2.33kWh/m3. This is about a 40% energy recovery, and it is a substantial energy recovery opportunity for a small scale sea water reverse osmosis system. Therefore, using a boosted pump and energy recovery turbine, energy consumption has decreased from about 7.87kWh/ m3 to about 2.33kWh/m3. Darwish (2002) and a RO system in a Caribbean (Curacao) islands have reported a specific energy consumption of 4.52kWh/m3, and 3.15kWh/m3, respectively, for 5700 m3/day desalination water capacity. Compared with these results, our design results are low, though compatible. Thus, depending on the feed flow rate and pressure exerted, our operating window of the system can potentially recover anywhere between 4kW to 12kW. The RO system is expected to operate under a semi-instantaneous base load of 17kW. Thus, assuming a feed pressure of 45 bar, boosting pressure of 10 bar and feed water flow rate of 10 m3/hr, the system can recover about 4 kW to 7 kW per hour (figure 3). Moreover, increasing the feed water flow rate at low pressure could substantially increase energy recovery and water production. In this regard, within the operational window of the system, any other points of operation could be selected as an initial point of operation.


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Initial energy consumption, potential energy recovery, and net energy consumtion at 45 bar feed pressure of the RO syetm

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Figure 3. Energy recovery potential of the RO system at 45 bar feed pressure

3.2 Modeling Optimal Energy Options

The main objective of this analysis is to determine cost-effective and feasible energy options to produce the required amount of water using the SWRO design discussed in the previous section. The types of energy options under consideration include stand alone as well as hybrid energy sources of wind, PV, and diesel. In determining least cost energy options, we have used an optimization model, HOMER, developed by the National Renewable Energy Laboratory (NREL). 3.2.1 Model Description: HOMER evaluates different energy options by simulating hourly energy flows for complex hybrid as well as stand alone power systems. The model simulates the power system configurations, optimizes for lifecycle costs, and generates results. It simulates the operation of a system by making energy balance calculations for each of the 8,760 hours of the year. For each hour, the model compares the electric load in the hour to the energy that the system can supply in that hour. In the presence of energy storage devices, the model determines when to discharge and charge electricity. The model also estimates the lifecycle cost of the sys-tem based on capital, replacement, operation and maintenance, and fuel costs of each component including PV, wind turbines, batteries, generators, and inverters. After simulating the system configurations, the model displays a list of feasible systems, sorted by lifecycle cost, based on their net present values (NPVs). Then, based on the results, we have to navigate for the least cost and feasible systems. However, all the least cost systems are not necessarily feasible. Thus, reliability and other issues need to be considered in deciding the optimal energy option. 3.2.2 Determining SWRO Base Load: In reverse osmosis desalination systems, energy is a major consideration. Depending on the capacity of the RO systems, estimates of energy use ranges from 2 to 10 kWh/m3 of water produced (Hafez, 2002). The average base load for our RO system is 17 kW/h with a specific work done of about 2.33 kWh/m3. In order to make the assumptions of SWRO energy demands more realistic, 5% and 2% noise are added in the model for daily and hourly loads, respectively (figure 4). The challenge is optimizing the constant load under highly variable power supply systems.


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Figure 4: SWRO system design monthly primary average loads.

In order to solve the challenge, we have designed the SWRO system to operate anywhere between 7kW and 27kW, which are the allowed operating power thresholds. Any power below and above about 7kW and 27kW, could ultimately be considered as excess power. Thus, we are trying to optimize the regular SWRO power load under an irregular power supply, which makes the analysis complex, especially when wind energy is considered. Based on the base load power requirement, different energy models are simulated with different based load configurations, with and without energy storage devices. An energy dispatch strategy is designed to fulfill the base load of the RO system depending on the type of energy option selected.

3.2.3 Wind Power

3.2.3.1. Implications of Wind Speed Variations: The average wind speed of the study area is 6.8 m/s (GoE, 2004). Wind speed distributions can typically be described in terms of the Weibull distribution (Feron, 1985). According to Rosen (1998), the shape parameter for the Southern Red Sea area, particularly Port Assab, is 2.4. The standard Weibull shape parameter is 2.5. Using the Danish Industry Wind Association power calculator, at 250C, 10m above sea level, 101.21 kPa atmospheric pressure, 1.22 kg/m3 air density and 2.4 shape parameter, the scale parameter is 7.67. Thus, using equation 3, the probability of the wind speed distribution is computed.

( ) ⎥⎦⎤

⎢⎣⎡ −⎟

⎠⎞

⎜⎝⎛=

− kk

v cv

cv

ckvf exp)(

1

( 3 )

Where, v is wind speed in m/s

k is shape parameter(Beta), c is scale parameter(Alpha) ⎟

⎠⎞

⎜⎝⎛ +Γ

−=

kV11

V−

is average wind speed, Γ is the gamma function.


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However, wind speeds throughout the seasons are irregular, and the average wind speed has high seasonal variations. According to the U.S. Navy Climatic Study of the Red Sea report conducted in 1982 (Rosan, 1998), for 7 months (October through April), mean wind speeds are between 6 m/s and 10 m/s, and the remaining months (May through September), mean wind speeds are between 3 m/s and 5 m/s. Assuming an average wind speed of 8 m/s for season 1(October through April ) and 4 m/s for season 2 (May through September), the scale parameters are 9 and 4.5, respectively. The density and cumulative Weibull distributions of the two seasons are shown in figure 5. A very high wind speed in one season and a very low wind speed in the other season makes the modeling process more complex. That is, the irregularity of seasonal wind speed makes the system more complex to optimize without expecting very high excess electricity during the high wind speed season, and employing energy storage systems during the low wind speed season. Most studies indicate that to overcome power shortages during the low wind speed seasons a large number of wind turbines should be installed (Cavalllo,1997 and De Carlos and Keth, 2002, Denhom et.al, 2005). This is one of the issues that we need to address in this analysis. 3.2.3.2. Model Input: In this analysis, we assume that the RO system will be powered by a stand alone wind turbine. In this model, a 17kW base load is considered in order to produce potable water at about 2.33 kWh/m3. This approach allows capturing all wind energy outputs, produced whenever the wind is blowing. Taking in to consideration the above mentioned seasonal wind speed and energy variations, we have considered monthly average wind speeds, with an annual average wind speed of 6.8 m/s. The monthly average wind speeds are then simulated to hourly wind speeds for 8750 hours of a year. Using a FL3 30kW wind turbine the result of the simulation are shown in figure 6.

Seasonal variation of wind speed

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Figure 5: Seasonal variations of wind speed Weibull Figure 6. Daily wind power outputs and SWRO load distribution of the study area of FL 30kW wind turbines

According to the model, the effective annual number of hours is more than 4000 hours. Thus, the wind turbine seems to be a good candidate for the analysis. However, in doing so, excess electricity is expected, especially during the high wind speed season. Thus, using FL 30 wind turbine with a lifetime of 20 years (Manwell 2002), capital cost of $130,000, and operation and

3 Fuhrländer(FL) 30kW is a type of 30kW wind turbine developed by Fuhrländer AG. http://www.fuhrlaender.de/start.php


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maintenance costs of $3900 (2.5% of capital cost) (DuPont, 2004) and replacement cost (85% of the capital cost), an analysis has been conducted and its performance determined. 3.2.3.3. Results of the Analysis: According to the optimization results, the least cost and feasible wind energy option consist of one FL 30kW wind turbine, 10 batteries, and 10 converters. The cost of this option is about $0.17/kWh with a capacity shortage of about 51%. This means that our RO system will be functional at full capacity for about 4000 hours of the year, producing about 30,000m3/year. Since most of the high wind speeds are during the day, the system can operate for more than 8 to 12 hours per day for the whole year totaling 4000 hours of a year. In order to deal with water production increase during high temperature and wind speed seasons, there might be a need to build a water storage tank in order to store water to be used during the low-wind-speed season of the year. It should be noted that the use of a battery as an energy storage device is not solely meant to increase energy production. The primary objective is to keep the power supply at a semi-instantaneous condition (fig. 7) so that the RO system could produce water continuously. When wind power is starting to decrease, electricity is dispatched accordingly to meet the demand. This could also potentially minimize the deterioration of membranes. According to the results of the model, the lowest wind speeds occur from mid night up to early in the morning. Thus, there is a possibility of putting off the RO system or adjusting it to function with battery power during this time interval. Annual wind power output, base load, and water production scenarios are shown in figure 8.

RO Baseload, Wind Power and Water Production

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Figure 7: Base load, wind power production, battery power dispatched, and water production for the first two weeks of the month of January.


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RO Base load, wind power and water Production

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Figure 8: Annual wind power, base load, and water production scenarios.

3.2.4 Photovoltaic (PV) Power

3.2.4.1 Model Input: This analysis assumes simulating a stand alone PV power without applying a sunshine tracking system. Using a 17kW base load for about 8 hours/day of a year, daily solar radiation at Beraso’el (13030’ N and 42043’E), the performance of a PV powered system has been analyzed. In the analysis, we have assumed a 25 years lifetime of PV, $7000/kW capital cost, and $6500 replacement cost. It is important to realize that the assumptions of RO base load in the wind powered system, which is continuous for 24 hours (figure 8) is different from the PV powered RO system, which is configured to operate during the day only. The main reason is, unlike sunshine daily predictions, in wind power it is very difficult to predict when the wind will be blowing. Therefore, in the case of the wind powered configuration, we have considered a continuous power demand in order to capture all available wind energy whenever the wind is blowing. In the case of a PV powered system, the optimal load is configured to operate during the day assuming that the energy load is only required for 8 to 10 hours of the day. Thus, this configuration assumes operation during the day only, producing the intended amount of water.

3.2.4.2 Results of the Analysis: The analysis considers a PV power system with and without battery energy storage. The result of the analysis shows that unmet load is substantially decreased when battery energy storage is applied. Using energy storage, unmet capacity shortage is decreased from 36% to 17%. Thus, in terms of attaining a steady power supply for about eight hours per day, the PV with battery energy storage might be a good candidate compared with the PV model without battery. Compared with the cost of electricity using wind power (0.17$/kWh), using the PV system with battery energy storage, the cost of electricity is about 0.39 $/kWh. The RO system base load, PV power produced and battery power dispatched for the first two weeks (330 hrs) of January are shown in figure 9. According to the simulated results, most of the


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time the PV power is above or equal to the base load. Since the RO system can operate at up to 27 kW, all the electricity could be used producing as high as 8 m3/hr. Under this scenario, on average water production is about 5 m3/hr. Without battery storage, annual water production is about 11,600 m3 and with battery storage, about 14, 000 m3. Thus, PV power with battery storage increases water production by about 20%. Figure 10 shows the annual load, power and water produced. From this analysis, it can be concluded that the PV-battery configuration is an appropriate configuration option, enough to achieve daily water needs without substantially investing in a big water storage tank.

Baseload of RO, PV Power, Battey Power Storage and Dispatch and Water Production

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Figure 9: The first two weeks of the month of January’s simulated RO base load, PV power produced, battery power dispatched/stored in kW, and water produced (m3/hr).

RO Base load, PV Power Produced and Water Produced for one year

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Figure 10: Annual RO base load, PV power and water production

3.2.5 Diesel Power

3.2.5.1 Model Input: In this model a 50 kW diesel generator is considered. The estimated capital and replacement costs are $14000, and $10000, respectively, the O&M cost is $1.5/hour, and the diesel cost in Eritrea is assumed to be $0.60/liter. The lifetime of the generator is 20,000


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hours. In Eritrea, the cost of electricity produced by the gensets is at least twice as expensive as grid electricity (Habtesion and Tsighe, 2005). This is mainly due to the high diesel transporting cost to remote areas, high maintenance costs and high fuel consumption. Despite such high costs, users tend to give high value to the power generated by gensets as it is flexible to meet the energy needs of both domestic and income generating activities. 3.2.5.2 Results of the Analysis: According to the results of the analysis, a 25 kW diesel generator is enough to meet the intended water demand, operating for about 8 hours per day. The cheapest electricity cost and feasible option of diesel generator is about 0.29 $/kWh. 3.2.6 Integrated Power System This option simulates the combination of the three options such as wind, PV and diesel energy sources with battery as energy storage. In this scenario, the challenge is the assumptions of base load configurations. In the case of stand alone power systems, it is possible to configure the base load according the behavior of energy sources. In an integrated case, we will assume that the base load will be the same as the wind energy configuration 24 hours per day and 8750 hours per year. Then, based on the simulation results, in addition to least cost options, we will determine the most feasible options. In an integrated approach, whenever wind energy is available it is the cheapest option. Under this scenario, the cost of electricity of the feasible options ranges from 0.17 $/kWh to 0.22 $/kWh, for stand alone wind turbine with battery, and wind turbine and PV with diesel (W_PV_D), respectively. A 5 kW PV, 30 kW wind turbine, and 5 kW battery are one of the least cost ($0.190/kWh) choices. However, it seems that whenever wind speed is very high, PV energy is also very high. Thus, there is no point to incorporate PV power in our system. Wind energy with a battery is the cheapest option ($0.170/kWh), and it is a feasible choice with the lowest capacity shortage of about 55%. Thus, for SWRO system an integrated system might not be feasible option.

4. Discussion 4.1. Energy Options: Based on the renewable energy potential of the study area, both wind and PV energy resources are feasible. With respect to power cost, the optimization model doesn’t incorporate price uncertainty. All costs are simulated based on the net present value. According to the optimization model, at about a 6% interest rate, the PV powered RO system is more expensive than the wind powered RO system, with the costs about 0.39 $/kWh and 0.17 $/kWh, respectively (table 1). However, the results of our sensitivity analysis shows that PV could be competitive with wind energy as the interest rate increases. As a result of changes in interest rates, production, and other factors, energy prices are volatile4, especially for diesel. Thus, assuming about 25% error, we have calculated the maximum and minimum power costs (figure 11). According to the results, the minimum and maximum wind power costs are about 0.13 $/kWh and 0.22 $/kWh, respectively. Likewise, for PV power, the minimum and maximum costs are about 0.30 $/kWh and 0.50 $/kWh, respectively. In either case, wind power is cheaper 4 The research will be further extended (not discussed in this paper) to incorporate energy prices volatility using stochastic process.


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than PV power. In terms of reliability, for this particular area of study, it seems that PV is more reliable than wind. This is due to the fact that the duration of the availability of sunshine is more predictable than the wind speed. As a result, it is easier to schedule the operation of PV powered RO systems than a wind powered system. 4.2 Water cost: In the last 20 years, the water cost from SWRO has dramatically decreased from about $2.8/m3 to $1.5/m3. This has been attributed to the use of energy recovery devices, and efficient membranes that allow rejection of salt at low pressure (Meyer-Steele and Gottberg, 2005). Water costs are analyzed based on the overall life cycle of the RO system, which includes the capital costs of the initial membrane, pressure vessel, pumps, energy recovery turbine, and operational and maintenance for membrane replacement and electricity costs. The power costs (table 1) are the results of an optimization model for selected energy options. Assuming the life time of the RO system is 10 years, at 10% interest rate, water costs are computed (table 1). According to the results of the analysis, energy expenses per cubic meter water produced range from 0.520 $/m3 for wind powered, and 1.06 $/m3 for the PV powered RO system. PV powered water cost is more expensive than wind powered by about a factor of two. Recent studies indicated that today a cost of $1/m3 for seawater desalination would be feasible (Zhou and Tol , 2005). With an estimated 25% water cost error, water costs for each option are shown in figure 12.

Table 1: Summary of power and water costs for different energy options 1 2 3 4 5 6 7 8 9

Energy Options* W_B W_PV_B W_D W_D_B W_PV_D_B W_PV_D D PV_B PV

Power cost ($/kWh)** 0.170 0.190 0.197 0.200 0.217 0.222 0.286 0.394 0.402

Water cost($/m3)** 0.522 0.569 0.585 0.592 0.631 0.643 0.791 1.042 1.061

• (1). W_B= Wind and battery; (2). W_PV_B= Wind, Photovoltaic, and Battery; (3). W_D= Wind and diesel; (4) W_D_B= Wind,

diesel, and battery; (5). W_PV_D_B= Wind, Photovoltaic, Diesel, and Battery; (6). W_PV_D= Wind, Photovoltaic, and Diesel; (7).

D= Diesel Only;

(8). PV_B= Photovoltaic and Battery; and (9). PV= Photovoltaic.

Power Costs of Different Energy Options

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Figure 11: Power costs with 25% error. Figure 12: Water costs with 25% error

(Note: the number of energy options corresponds with the types of energy options shown in table 2).


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According to the results, power cost constitutes the highest proportion (fig. 13). Regardless of the type of energy option chosen, the fraction of energy expenses per cubic water produced is about 80% of the total expenses. The proportion of expense per cubic water produced for other expenses constitutes less than 20%, which includes the initial membrane and pressure vessel (3%), the capital cost of the energy recovery turbine, pumps and miscellaneous (8%), and the membrane replacement cost (3%).

4.3 Water productivity: According to the model, wind powered (option 1) and PV powered (Option 2) RO systems could produce about 30,000 m3 and 15,000 m3 per year, respectively. PV produces about the exact demand at high price and wind produces more than double of the required amount at the cheapest price. Wind powered water production for the months of November through March is about 4000 m3/month and for the months of April through October, about 1600m3/month (figure 14). The PV powered RO system produces a constant amount of water, 1,200 m3/month. Based on the potential of water productivity and water cost, the wind powered RO system is indicated to be the best option. 4.4 Implications of battery storage: We have tried to compare the implications of energy storage in wind and PV powered systems. For wind and PV systems with energy storage, the average water production increases by about 11% and 6%, respectively (figure 15). This implies that, using our model, the use of battery storage in a RO system is more pronounced or important for the PV than the wind powered RO system. Moreover, in addition to increasing water production at a minimum incremental cost of about 6% for battery in wind and 2% for battery in PV, it helps to decrease the intermittency of the electricity flow. 4.5 Implication of Energy Recovery: An attempt has also been made to compare the impact of energy cost with and without the energy recovery system (figure 16). The results show that using a pressure exchange (PX) 45s energy recovery turbine, the energy expenses for the wind powered option for example could be reduced by about 41% from 0.67 $/m3 to 0.40 $/m3. The capital cost of a PX 45s energy recovery turbine, which is about $12,000, could be paid back within a year.

Cost breakdown of the syatem under different energy options

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Figure 13: Cost breakdown for all energy options

PV and Wind Powered RO Systems' Monthly Water Production

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Percentage of water production Increase using battery storage system

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Figure 15. Water productivity and energy storage

Energy Expense with and without energy recovery(ER) the system under different energy options

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Figure 16: Energy cost with and without energy

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5. Conclusion

This paper has analyzed the performance of a renewable energy powered small scale sea water reverse osmosis system particularly in terms of water productivity and energy cost. The RO system has been designed with a wide operating system capable of operating under intermittent energy supply. Results of our analysis show that the contribution of the booster pump and energy recovery are not negligible in increasing water productivity and decreasing energy consumption per cubic meter of water produced. Our results show that using the available technologies, it is possible now to produce water at about 2.33 kWh/m3, which for a long time seemed unattainable. Using a booster pump and energy recovery turbine, energy consumption can be decreased from about 7.87 kWh/m3 to about 2.33 kWh/m3. The results indicate that wind powered water production (0.50 $/m3) seems economically feasible. According to Zhou and Tol (2004), in China, 1.0 $/m3 desalinated water is considered as economically feasible. This implies that even a PV powered system could be competitive, and most of the world could benefit. Generally, in Eritrea water production and tariff costs are about 0.30/m3 and $0.43/ m3, respectively. This implies that wind powered system might be competitive. Our energy optimization model for reverse osmosis desalination system is the first step toward facilitating its widely used application. Moreover the energy optimization model, which has not been used before for such purpose, provides a useful and easy model, particularly in simulating hourly renewable energy production and consequently synchronizing with the energy load in order to operate under the wide operating system of the RO plant. However, in order to determine the robustness of the methodology, we should point out that the model needs to be tested. We recommend that since the availability of energy recovery systems capable of operating under wide operating system are limited, for higher water productivity and continuous energy recovery, it is important to operate above 10m3/hr feed water at low pressures between 30 to 45 bars. The lower the pressure the lower the energy required to produce water.


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6. Further Research

In this paper, energy options are selected primarily based on the Net Present Values (NPVs). The research will be further extended to look at the impact of optimal investment options under uncertainties in energy prices under clean development mechanism (CDM) of the United Nations Framework Convention on Climate Change (UNFCCC). We believe that stochastic process as a result of energy price and demand volatility could give a valuable incentive in determining energy options under CDM beyond the SWRO system. References:

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