Dry cooling of concentrating solar power (CSP) plants, an economic competitive option for the desert regions of the MENA region

Available online at www.sciencedirect.com

ScienceDirect Solar Energy 103 (2014) 417–424 www.elsevier.com/locate/solener

Dry cooling of concentrating solar power (CSP) plants, an economic competitive option for the desert regions of the MENA region Ahmed Liqreina a, Louy Qoaider b,⇑ b

a REMENA Master Program at Kassel University, 2012, Kassel, Germany German Aerospace Center (DLR), Institute for Solar Research, Paseo de Almerı´a, 73, 04001 Almerı´a, Spain

Received 8 December 2013; received in revised form 16 February 2014; accepted 20 February 2014 Available online 15 March 2014 Communicated by: Associate Editor Ranga Pitchumani

Abstract The objective of this work is to investigate the competitiveness of dry cooling of steam power blocks of concentrating solar power (CSP) plants in comparison to wet cooling in the arid zone of the Middle East and North Africa (MENA). A case study is performed for this purpose in the Ma’an area in southern Jordan. In Ma’an water is scarce, expensive and restricted. Thus dry cooling of a power block is an attractive option. For the study purposes, a reference parabolic trough CSP power plant that is based on design of Andasol power plant in southern Spain is considered. The reference power plant has a nominal capacity of 50 MWel with 7.5 full load storage hours and is simulated with Greenius software (DLR, 2001) for both dry and wet cooling options. The reference power plant is simulated in its original location in order to validate the results using actual values of the plant. Thereafter, simulations were conducted for the power plant at the case study site with the help of high precision ground measured meteorological data provided by the DLR’s enerMENA meteorological (DLR, 2012) station installed on site. The technical simulation at the selected site predicted good economic results thanks to the site’s high direct normal irradiation (DNI) values and normal ambient temperatures (around 5628 h 10–30 °C). Although dry cooling increases cost versus wet cooling, dry cooled CSP plants still a competitive option in hot arid regions with excellent solar resource such as Ma’an and can even realize lower cost than equivalent wet-cooled plants located in regions with lower solar resource. In this context, from the technical and economical point of view, the dry cooling option in Ma’an showed to be feasible. The reference power plant produces at the selected site in Jordan with dry cooling option 180.54 GW he net annual energy yield, with 4108 full load operating hours. 13.1% annual mean overall efficiency, with a water consumption of 59,991 m3/a, and 0.1444 €/kW h the levelized cost of electricity (LCOE). Whereas, the wet cooled reference power plant produces 201.06 GW he net annual energy yield, with 4399 full load operating hours. 14.6% annual mean overall efficiency, water consumption of 751,618 m3/a, and 0.1258 €/kW h LCOE. By comparing the wet cooled reference Andasol power plant in Spain with the same but dry cooled power plant in Jordan, following outputs are resulted: the overall efficiency of the dry cooled plant in Ma’an is reduced by 3.1%, the water consumption reduced by 92%. The energy yield increased by 21.8%, and the LCOE reduced by 18.8%. The results of this study prove that dry cooled CSP power plants in sites with significantly high DNI values is an attractive economic and technical option to be considered in future planning of new projects. Ó 2014 Elsevier Ltd. All rights reserved.

Keywords: Dry cooling; Concentrating solar power (CSP); Middle East and North Africa (MENA); Greenius simulation software

⇑ Corresponding author. Current address: German Jordanian University, School of Natural Resources Engineering & Management, C313 Amman, Jordan. Tel.: +34 950 27 8817; fax: +34 950 26 0315. E-mail addresses: [email protected] (A. Liqreina), [email protected] (L. Qoaider).

http://dx.doi.org/10.1016/j.solener.2014.02.039 0038-092X/Ó 2014 Elsevier Ltd. All rights reserved.

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1. Introduction Solar energy technologies are the most promising renewable sources for the future world energy, photovoltaic, which is direct conversion from light energy into electricity, and concentrating solar thermal power called also concentrating solar power (CSP) are the two common technology types utilize solar energy to generate power. CSP shows attractive features to be installed in utility scale. On-grid CSP power plants with thermal storage should stabilize the grid and secure the dispatchability of power. These power plants consist mainly of the solar field and the conventional power cycle. Most matured CSP technology is parabolic trough technology, which operates in general with conventional steam Rankine cycle for power generation. Thermodynamics rules state that wet cooling of conventional steam power cycles is advantageous over dry cooling in terms of power block efficiency since with wet cooling option the exit steam from the turbine will be cooled down faster and to a lower temperature than with dry cooling. CSP parabolic trough power plants are not an exception because they operate an identical conventional steam power cycle and operate better with wet cooling system. However, unlike the conventional fossil fuel fired power plants, which are normally located in the coastal regions, where atmospheres are generally unclear, the most suitable regions to erect new CSP plants are generally in desert regions of the earth’s Sun Belt, e.g. the Middle Eastern and North African region (MENA), with high values of direct normal irradiation (DNI) but also with water as scarce and expensive commodity. Here merges the need to investigate the economy and competitiveness of deploying dry cooling option for CSP power plants in attractive regions of the MENA region. Several studies by NREL showed that dry cooling could save more than 90% of water consumption (Turchi et al., 2010), on one hand. On the other hand, the overall performance of such a power plant is reduced under higher ambient temperatures. Such losses would be compensated entirely by higher DNI and the high efficiency when the ambient temperature is low. Hence the design of a CSP plant, especially its solar field, depends also on which cooling system is used; a bigger solar field for dry cooling than of that for wet cooling for the same power output. This results in higher investment costs for dry cooled power plants than those of wet cooled plants with the same power production capacity. CSP power plants require huge initial investments. While any additional costs are undesired and would threaten securing project finance. In this context a trade-off between all options should be made for each specific site to know whether to use dry cooling or not. For many locations, dry cooling is the only affordable option and therefore must be considered but after its economy has been evaluated.

The main objective of this work is to evaluate the use of dry and wet cooled CSP parabolic plants in Ma’an site in Jordan as an example for the MENA region. This will be done by establishing a comparison between both options based on a reference plant design of Andasol in southern Spain. The comparison is held for the technical performance and the economics of the plant options to show that high DNI could compensate for the efficiency losses of a dry cooled plant. 2. Methodology After selecting and assessing the case study site, the reference plant was selected and configured to have the main features of Andasol plant in southern Spain (Table 5.1). Thereafter, configurations of wet and dry cooling systems were fixed. In a following step, the reference power plant was simulated in its actual location and the simulation results have to be validated with the real power plant output to give good basis for further simulations and comparison in the case study site. To enable generating accurate simulation results, high precision ground measured meteorological data from enerMENA meteorological station in Ma’an (DLR, 2012) were obtained, analyzed and used in the simulation runs. For the simulation runs in Ma’an, the pre-design parameters of the reference power plant are fixed to have a good comparison base for dry and wet cooling options. Subsequently, only the weather data of the sites were varied. Three major steps were followed: simulation of Andasol design in Spain as a reference, simulation of the same plant design in Ma’an region, and finally performing assessment of dry cooled plant in study location. Finally, realistic simulation results of the economic feasibility for both the dry and wet cooling options were performed for the site. 3. Case study site The site chosen for this study is Ma’an development area near Ma’an city: 200 km south of the capital city of Amman, 9 km from the city of Ma’an and 100 km from the port city of Aqaba. The site coordinates are (N 30.17°E35.78°) and this represents an attractive location for CSP in MENA region, due to high DNI values (ca. 2693 kW h/m2 a) DLR, 2012 and because of the expected and desired socio-economic impacts on the area. On the study site, one enerMENA ground meteorological is mounted and has delivered reliable meteorological data since 2011. A Meteodata file was generated from these data to be used in Greenius plant simulations. Fig. 1 shows the monthly and daily distribution of average DNI values in the study site. The figure demonstrates the annual DNI values and their daily distribution at the study area. It is obvious from the figure that the highest DNI is always around noon reaching its extreme between June and August.

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Table 5.1 Technical specifications of the reference plant. Description

Value

Nominal capacity Net aperture area of the solar field Length of one single collector Focal length Aperture width Row spacing Average clearness  field availability Optical peak efficiency Heat loss factor piping including expansion vessel Solar field specific parasitics Steam temperature at field entrance Steam temperature at field exit Storage capacity (Assumption) Full load storage hours

50 MW 510,120 m2 148.5 m 1.71 m 5.77 m 17.31 m 0.93 0.75 15 W/m2 6.3 W/m2 (Arias et al., 2009) 293 °C 393 °C 97000 MW hth Wet 7.5 h Dry 6.3 h

Design point conditions for power block (Greenius library) Cooling system Wet Conversion efficiency 38% Design Back Pressure 0.08 bar Power block parasitics (reduced) 1679 kWel according to (Schenk and Eck, 2012; Turchi et al., 2010) Ambient pressure 0.9 bar Relative air humidity 20% Solar thermal heat 129,222.5 MWth

Dry 34% 0.144 2729 kWel 0.99 bar 60% 147,440 MWth

Fig. 1. Mean monthly diurnal of DNI according to enerMENA meteorological station.

4. Dry cooling as an attractive option The need for a dry cooled power block in general and specifically for a CSP parabolic trough plant originates from the scarcity water recourses in arid regions, e.g. Jordan, where solar irradiation is at its highest values. The availability of the highly accurate enerMENA metrological data, gave the chance to conduct a realistic investigations on possible CSP plants in such regions.

Dry bulb temperature is an important parameter in dry cooling plant designs that affects the performance of the plant. For CSP plant, high dry bulb temperatures will reduce the annual energy yield and extremely low temperatures will lead to freezing the heat transfer fluid (HTF), especially during the night hours. In such circumstances additional heater is used to heat up the HTF in the solar field to protect it from freezing. Freezing hours are distinguished by the plot of dry bulb temperature (Fig. 2).

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The overlook on Fig. 2 shows that Ma’an is a moderate hot area, and that it has very low probability of freezing hours thus the operation of plant is accepted. The fact that the efficiency is inversely proportional to the ambient temperature made us to check time of occurrence of such high temperatures. Referring to the figure again, it is clear that the highest temperatures occur at noon hours, when also highest DNI values are measured. Consequently, having such a DNI/temperature values combination at this certain time results is seen as an advantage for CSP installations at study site since the reduction in the plant performance caused by high temperatures decreases also the amount of dumped energy released to the environment caused by high DNI values. This allows a better sizing of the plant. The number of hours of temperature occurrence can be seen in the Histogram below, Fig. 3. It can be seen that around 12 h occur with temperature over 40 °C, only 79 h below zero, around 5628 h with temperature between 10 and 30 °C, and around 1168 h with temperatures between 30 and 40 °C. It could be concluded from this section that dry cooling option in Ma’an is a technically feasible option since the overall plant performance will not be affected significantly. In this regards, further investigations need to be done in the next sections. 5. Plant design specifications and simulation inputs In this section the Andasol plant design, which operates with a wet cooled power block, is taken as reference plant for this study. Simulations for the plant yield were carried out with the actual inputs. To enable simulating the reference plant with dry cooling option, dry cooling

specifications for a similar plant were driven from Greenius library. . . The thermal storage capacity also adjusted to reach 7.5 h. As we know this number is depending on the turbine thermal input. But for the dry case only the power block was changed, this led to lower full-load hours 6.5, it is supposed now to keep the comparison without optimization. The solar plant itself consists of 156 collector loops (model Eurotrough) with a total net collector area of 510,120 m2 (Schenk and Eck, 2012). The specifications of the plant are listed in Table 5.1. The investment costs of the reference power plant for the two cooling options are shown in Table 5.2 below together with project period and the discount rate considered. These costs are in accordance to (IRENA and Renewable energy technologies: cost analysis series., June 2012) and (Turchi, 2010). When the power block is changed to dry, the new investment cost is calculated by Greenius. 6. Results 6.1. Simulation of the reference power plant in its original location in Spain The purpose of this section is to validate the Greenius simulation outputs with real ones published by plant owner to demonstrate the reliability of the simulations. Published plant data were used as is from reference Herrmann et al. (2002). Moreover, the values of available meteorological data set, which is taken from meteonorm, differ from those used by the plant owner. To conduct plant simulations, all published plant specifications are inserted into Greenius. The meteorological file

Fig. 2. Annual distribution of hourly averaged dry bulb temperature in Ma’an.

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Fig. 3. Histogram of dry bulb temperature in Ma’an.

of site is loaded with an Annual DNI sum of ca. 2052 kW h/m2 a. The owners published DNI value is 2202 kW h/m2 a (Millennium, 2008). The simulation results in comparison with the owner’s published values are listed below in Table 6.1. It can be noticed in the table that both values’ sets are comparable with the fact that the simulation results are much conservative than real plant output values. From that we conclude that the reference plant model delivers reliable data that reflect conservatively the real situations. This plant will undergo simulations in the study region with two cooling options; wet and dry in the following sections. 6.2. Simulation of the reference power plant in the study area in Jordan Two simulation steps are done in this section: dry and wet cooling systems are attached to Andasol design and simulated in Jordan. Only switching between two power block modules, the metrological data were changed without changing any other parameters. Fig. 4 shows the net power production of the reference plant in the study site. It can be noticed that the wet cooling option has higher power production than the dry cooling option due to the higher efficiency of the turbine. Nevertheless, in the period between December and

February, the dry cooling and wet cooling have nearly the same output due to the lower ambient temperature, which improves the turbine performance and because of using the dumped energy in dry cooling instead of being lost in wet cooling. In Ma’an the DNI is higher than in southern Spain thus the net power of both dry and wet cooling is higher too. The summer ambient temperatures are higher in Ma’an. This increases the difference of power production between wet and dry cooling. The overall efficiency is shown in Fig. 5. The wet cooling option always has higher efficiency than the dry case. This difference is at minimum at a low ambient temperature. In summer (e.g. June), the overall efficiency for wet and dry is reduced because of the dumped solar energy. It is expected that the dry cooling will have more reduction because of the lower performance but the effect is reduced by using more solar energy instead of dumping it. It is found that because Ma’an area has higher DNI, the Andasol design does not fit with its configuration to the region and shall be optimized. This could be done by optimizing the solar field and the storage full load hours for dry cooling as the turbine requires higher thermal input. In Fig. 6, the red line represents the thermal power output of the solar field, it is coincident with the DNI. Furthermore, the green line represents the charging and discharging of TES during the day. Whereas, the blue line is the net electrical power output, which decreases during

Table 5.2 Economic simulation inputs. Project period (year) Discount rate % Specific power block cost with cooling system in €/kW (Turchi, 2010) Total investment cost (Wet) in € Total investment cost (Dry) in €

30 6 Wet 692.8 Dry 855 268,273,251 (IRENA and Renewable energy technologies: cost analysis series., June 2012) 278,236,316

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Table 6.1 Simulation validation data. Description

Unit

Simulated

Published data (Herrmann et al., 2002)

% (Simulated-published)/simulated

Annual DNI Annual electricity sold to the grid Annual parasitics received from the grid Thermal efficiency peak Mean annual field efficiency Overall efficiency peak Annual overall efficiency Full load hours

(kW h/m2 a) (MW h/a) (MW h/a) % % % % (h)

2052 141,110.1 0 69% 42% 24.3% 13.5% 3089

2202 157,206 4,307 70% 46.1% 25% 14.7% 3144

7.31% 11.4% Not applicable 1% 4.1% 0.7% 1.2% 1.78%

Fig. 5. Plant overall efficiency for reference plant in Ma’an.

Fig. 4. Monthly net power for reference plant in Ma’an.

the night, because the thermal storage cannot feed the turbine with full capacity, in order to keep the plant working for more hours. 7. Comparison and discussion

costs for dry cooling are about 29,995.58 €/a (assuming the cost of water 0.5 €/m3), whereas, the costs for the wet cooled plant are around 375,808.83 € per year. (see Tables 6.2 and 7.1). Table 7.2 demonstrates the average monthly water savings by implementing dry cooling option in comparison with a similar power plant operating with wet cooled power block. The values in the table were calculated by multiplying the specific water consumption per unit produced electricity provided by NREL (Turchi et al., 2010);

7.1. Technical comparison The table below show the three simulated cases, in (Spain/wet, Jordan/wet, Jordan/dry). All are shown in annual values. The table shows also a comparison between the cooling options at each site, and the dry cooled plant in comparison to the known Andasol wet cooled plant. 7.2. Water requirements An expected and important output of this work is the reduction of water consumption when implementing dry cooling option. The simulations show that for dry cooled plant less amount of water is required and is estimated to about 60,000 m3/a. For comparison, wet cooled similar CSP plant requires 751,617 m3/a. In other words, 92% of water saving could be reached by using the dry cooling system in our CSP power plant. In monetary terms, the water

Fig. 6. Main operational characteristics of the expected dry plant in Ma’an (23-Jun).

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Table 6.2 Monthly power production and overall efficiency for reference plant in Ma’an. Wet

Dry

Month

Net power (MW h)

Gross power (MW h)

Overall efficiency (%)

Net power (MW h)

Gross power (MW h)

Overall efficiency (Wnet/DNIaA) (%)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual Mean

9,009.5 8,142.3 1,4043 17,523.4 18,446.1 23,726.5 24,428.8 23,934 20,547 12,830 8,270.7 8,144.5 189045.8

10,096.6 9,202.9 15,972.1 19,935.7 20,993.8 27,026.5 27,842.7 27,340 23,443.9 14,493.1 9,280 9,120.6 214747.9

9.2 9.9 13.7 15.8 15.9 16 15.9 15.5 15.7 13 9.9 8.7 13.8

8,752.8 8,360.7 13,508.9 16,697.9 17,740.4 22,757.1 23,506.6 23,498.5 18,837.3 11,588.1 7,594.5 7,698.9 180541.7

9,754.7 9,404.4 15,297.1 19,032.9 20,237.1 26,086.1 26,950.5 26,983.9 21,530.7 13,079.7 8,488.3 8,569.7 205415.0

8.9 10.2 13.2 15 15.3 15.4 15.3 15.2 14.4 11.7 9.1 8.2 13.1

Table 7.1 Summary of technical comparison between all simulated cases with aperture area 510,120 m2, TES 97 GWhth, and power block capacity 50 MW for all cases. Description/cooling type

Annual thermal power from solar field (Qtha) Annual net electricity output (Wnet) Annual power block parasitics Annual solar field parasitics Annual total parasitics Annual DNI  net aperture area (DNIa  A) Annual DNI  net aperture area  cosine of incidence angle Mean annual field efficiency = (Qtha/DNIa  A) Annual overall efficiency = (Wnet/DNIa  A) Annual thermal dumping Annual power block full load hours Number of Turbine starts Annual water consumption Levelized electricity costs

Unit

Reference Plant in Spain (Andasol)

Reference Plant in Ma’an Jordan

Plus Ma’an (%)

Wet

Wet

Dry

Wet

Dry

MW hth MW hel MW hel MW hel MW hel MW h MW h

439,782.0 141,110.1 5,430.7 7,893.3 13,324.0 1,046,919.3 913,573.9

630,350.5 201,055.0 12,638.8 13,063.4 25,702.2 1,373,702.1 1,236,275.8

630,439.5 180,541.7 13,541.1 11,332.4 24,873.5 1,373,702.1 1,236,275.8

30.2 29.8 57.0 39.6 48.2 23.8 26.1

30.2 21.8 59.9 30.3 46.4 23.8 26.1

% % MW hth h 1/a m3/a c€/kW h

42.0 13.5 30,182.7 3,089.0 335.0 540,520.1 17.79

45.9 13.8 44,353.1 4,399.0 377.0 751,617.7 12.58

45.9 13.1 14,305.3 4,108.0 369.0 59,991.2 14.44

8.5 2.2 31.9 29.8 11.1 28.1 29.3

8.5 -3.1 -111 24.8 9.2 -88.9 -18.83

3.5 m3/MW h and 0.5 m3/MW h for both wet and dry cooling respectively. 8. Conclusions The technical simulations show very good results regarding dry cooled CSP power plants thanks to high DNI at the study site specifically and in the MENA region in general. If the Andasol CSP plant in southern Spain would be shifted to Ma’an in southern Jordan, and its wet cooling system would be replaced with a dry one, it should have higher energy yield by 21.8%. As a result the levelized cost of electricity will be lower by 18.8% (14.44 €c/kW he). Subsequently, the plant would be feasible based on the existing conventional power generation costs in Jordan.

It can also be concluded that for desert areas with water scarcity, CSP power plants still are a good option but with a dry cooling systems. The dry cooled CSP plants in desert regions are technically and economically competitive with wet cooled plants in other regions with lower solar resource. The results of this study are site specific. However, they represent the arid dry regions in MENA. Corresponding investigation shall be done for different locations with different boundary conditions.

9. Recommendations Despite of the optimization potential of the reference plant, this research paper preserved the original configura-

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Table 7.2 Expected water consumption for dry/wet 50 MW with 7.5 TES in Ma’anJordan. The calculation was based on NREL study (Turchi et al., 2010), specific water consumption multiplied by the produced energy. Month

Wet cooled (m3)

Water consumption 50 MW 7.5 TES January 35,338.10 February 32,210.15 March 55,902.35 April 69,774.95 May 73,478.30 June 94,592.75 July 97,449.45 August 95,690.00 September 82,053.65 August 50,725.85 November 32,480.00 December 31,922.10 Sum 751,617.65

ware available to conduct this work. Moreover, we are grateful for the Ma’an Development Authority (MDA) for their support.

Dry cooled (m3)

Saving (%)

References

2,744.31 2,486.40 4,375.44 5,592.21 5,882.01 7,764.96 8,013.21 8,039.97 6,349.89 3,833.58 2,468.31 2,440.86 59,991.15

92.23 92.28 92.17 91.99 91.99 91.79 91.78 91.60 92.26 92.44 92.40 92.35 92.02

DLR, 2001. Greenius Energy Yield Calculation Software. . DLR, 2012. enerMENA Meteorological Network: Ma’an/Jordan: . Turchi, C., Wagner, M., Kutscher, C., 2010. Water Use in Parabolic Trough Power Plants: Summary Results from WorleyParsons’ Analyses. National Renewable Energy Laboratory, Golden. Report number NREL/TP-5500-49468. Schenk, H., Eck, M., 2012. Yield analysis for parabolic trough solar thermal power plants – a basic approach. DLR-enerMENA project. . Arias, D.A., Gavila´n, A., Muren, R., 2009. Pumping power parasitics in parabolic trough solar fields, in solarpaces. Berlin. Report number NREL/TP-550-47605. Turchi, D., 2010. Parabolic Trough Reference Plant for Cost Modeling with the Solar Advisor Model (SAM). National Renewable Energy Laboratory, Golden. IRENA 2012. Renewable energy technologies: cost analysis series. Abu Dhabi: The International Renewable Energy Agency (IRENA), (June 2012), pp. 17. Solar Millennium 2008. The parabolic trough power plants Andasol 1 to 3- The largest solar power plants in the world-Technology premiere in Europe. Solar Millennium, Erlangen. Herrmann, U., Geyer, M., Kistner, R., 2002. The Andasol projectWorkshop on Thermal Storage for Trough Power systems. Solar Millennium AG;FLABEG Solar International GmbH.

tions of Andasol power plant and used them in the study region by only changing the cooling options. It is recommended to further optimize the plant and adapt it to the local conditions to reduce the investment costs and increase the plant efficiency. Accordingly, a more realistic comparison between wet and dry cooling options would be done. Further financial investigations for CSP plants for the region should be done based on free market data. This should be done together with financial evaluation using soft loans and grants from international and regional donors that makes such projects more economic. Acknowledgements Many thanks to the enerMENA project of the German Aerospace Center – (DLR) for making their data and soft-