Optimizing Solar Field Layout Georg Brakmann, Miroslav Dolejsi SolEngCo GmbH

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Excellence in Solar Engineering Optimizing Solar Field Layout Georg Brakmann, Miroslav Dolejsi SolEngCo GmbH CSP TODAY South Africa 2015 4th Concentrated Solar Thermal Power Conference & Expo Cape Town, 14-15 April 2015

Sun Tracking of Parabolic Trough Collector Northern hemisphere Looking South 2

DNI - Clear Sky South - noon DNI [W/m2] on 21 March 1000 900 East a.m. West p.m. 800 700 600 500 400 300 200 100 0 7 8 9 10 11 12 13 14 15 16 17 Solar Time North DNI 3

South-North Collector Axis South - noon DNI / Solar Heat [W/m2] on 21 March 1000 900 East a.m. West p.m. 800 700 S, γ = 180 most even performance 600 500 400 300 200 100 0 7 8 9 10 11 12 13 14 15 16 17 Solar Time North DNI S 4

East-West Collector Axis South - noon DNI / Solar Heat [W/m2] on 21 March 1000 900 East a.m. West p.m. 800 700 600 500 400 E, γ = 90 peaks noon 300 200 100 0 7 8 9 10 11 12 13 14 15 16 17 Solar Time North DNI S E 5

SE-NW Collector Axis South - noon DNI / Solar Heat [W/m2] on 21 March 1000 900 East a.m. West p.m. 800 700 600 SE, γ = 135 p.m. better 500 400 300 200 100 0 7 8 9 10 11 12 13 14 15 16 17 Solar Time North DNI S SE 6

SW-NE Collector Axis South - noon DNI / Solar Heat [W/m2] on 21 March 1000 900 East a.m. West p.m. 800 700 600 SW, γ = 225 a.m. better 500 400 300 200 100 0 7 8 9 10 11 12 13 14 15 16 17 Solar Time North DNI S SW 7

Solar Multiple SM=1.0 South - noon DNI / Solar Heat [W/m2] on 21 March 1000 900 East a.m. SE, γ = 135 p.m. better E, γ = 90 peaks noon S, γ = 180 most even performance West p.m. SW, γ = 225 a.m. better 800 700 600 500 400 300 200 100 North 0 7 8 9 10 11 12 13 14 15 16 17 Solar Time DNI S E SW SE 8

Solar Multiple SM=1.5 South - noon DNI / Solar Heat [W/m2] on 21 March 1000 900 East a.m. SE, γ = 135 p.m. better E, γ = 90 peaks noon S, γ = 180 most even performance West p.m. SW, γ = 225 a.m. better 800 700 600 500 400 300 200 100 North 0 7 8 9 10 11 12 13 14 15 16 17 Solar Time DNI S E SW SE 9

Solar Multiple = 1.0 10 Frequency Distribution of Load Factor [%] 3,8% Difference in Annual Solar Heat 5% 3,8% 8 2,8% 2,8% 6 1,2% 0,3% 0, 0,3% 1,2% 4 2 0 500 1.000 1.500 2.000 2.500 3.000 3.500 4.000 Hours / Year S E -5% 90 135 180 225 270 [deg] Solar Multiple 1,0 Load Factor (S-N) 20,5% Load Factor (E-W) 21,2% 10

Solar Multiple = 1.1 10 Frequency Distribution of Load Factor [%] Difference in Annual Solar Heat 5% 8 3, 2,2% 2,2% 3, 6 0,9% 0,3% 0, 0,3% 0,9% 4 2 0 500 1.000 1.500 2.000 2.500 3.000 3.500 4.000 Hours / Year S E S-SM1.0 E-SM1.0-5% 90 135 180 225 270 [deg] Solar Multiple 1,0 1,1 Load Factor (S-N) 20,5% 22,3% Load Factor (E-W) 21,2% 23, 11

Solar Multiple = 1.3 10 Frequency Distribution of Load Factor [%] Difference in Annual Solar Heat 5% 8 6 1, 0,1% -0,5% -0,1% 0, -0,1% -0,5% 0,1% 1, 4 2 0 500 1.000 1.500 2.000 2.500 3.000 3.500 4.000 Hours / Year S E S-SM1.0 E-SM1.0-5% 90 135 180 225 270 [deg] Solar Multiple 1,0 1,1 1,3 Load Factor (S-N) 20,5% 22,3% 25,5% Load Factor (E-W) 21,2% 23, 25,7% 12

Solar Multiple = 1.5 10 Frequency Distribution of Load Factor [%] Difference in Annual Solar Heat 5% 8 6 4 0, -0,6% -1,4% -1,8% -1,8% -0,6% -1,8% -1,8% -1,4% 2 0 500 1.000 1.500 2.000 2.500 3.000 3.500 4.000 Hours / Year S E S-SM1.0 E-SM1.0-5% 90 135 180 225 270 [deg] Solar Multiple 1,0 1,1 1,3 1,5 Load Factor (S-N) 20,5% 22,3% 25,5% 28,1% Load Factor (E-W) 21,2% 23, 25,7% 27,7% 13

Solar Multiple = 1.7 10 Frequency Distribution of Load Factor [%] Difference in Annual Solar Heat 5% 8 6 4 0, -1,2% -1,2% 2-3,5% -3,6% -3,1% -3,1% -3,6% -3,5% 0 500 1.000 1.500 2.000 2.500 3.000 3.500 4.000 Hours / Year S E S-SM1.0 E-SM1.0-5% 90 135 180 225 270 [deg] Solar Multiple 1,0 1,1 1,3 1,5 1,7 Load Factor (S-N) 20,5% 22,3% 25,5% 28,1% 30,2% Load Factor (E-W) 21,2% 23, 25,7% 27,7% 29,2% 14

Solar Multiple = 1.9 10 Frequency Distribution of Load Factor [%] Difference in Annual Solar Heat 5% 8 6 0, -1,6% -1,6% 4-5,2% -5,4% -4,2% -5% -4,2% -5,4% -5,2% 2 0 500 1.000 1.500 2.000 2.500 3.000 3.500 4.000 Hours / Year S E S-SM1.0 E-SM1.0-1 90 135 180 225 270 [deg] Solar Multiple 1,0 1,1 1,3 1,5 1,7 1,9 Load Factor (S-N) 20,5% 22,3% 25,5% 28,1% 30,2% 32, Load Factor (E-W) 21,2% 23, 25,7% 27,7% 29,2% 30,3% 15

Effect of Solar Multiple 6 5 4 3 2 1-1 1,0 1,2 1,4 1,6 1,8 Solar Multiple Energy Gain (S-N) Energy Gain (E-W) LCOE Change (S-N) LCOE Change (E-W) Cost Increase 16

Effect of Axis Orientation and Solar Multiple (SM) Larger Solar Multiple (SM) increases solar field. increases load factor and annual solar heat [kwh/a]. increases power specific investment cost [ /MW]. decreases levelized cost of energy [LCOE, c/kwh] up to the optimum SM. In the clear sky example LCOE decreases by about 9 % for SM=1.5. In case of SM=1.0 the E-W orientation results in about 4% more annual solar heat and a corresponding lower LCOE. Solar Multiple 1,0 1,1 1,3 1,5 1,7 1,9 Load Factor (S-N) 20,5% 22,3% 25,5% 28,1% 30,2% 32, " (E-W) 21,2% 23, 25,7% 27,7% 29,2% 30,3% Energy Gain (S-N) 9% 24% 37% 48% 57% " (E-W) 4% 12% 26% 35% 43% 48% Cost Increase 5% 15% 25% 35% 45% LCOE Change (S-N) 0, -3,7% -7,6% -8,9% -8,7% -7,3% LCOE Change (E-W) -3,7% -6,6% -8,5% -7,6% -5,3% -2,2% 17

Asymmetric irradiation: Sites in coastal areas Morning haze due to water evaporation Afternoon clear sky 18

Asymmetric irradiation: Sites in desert areas Morning clear Afternoon haze due to atmospheric thermal turbulence Pictures taken in Atacama Desert 19

Asymmetric irradiation: Sites in desert areas Afternoon clouds and atmospheric thermal turbulence 20

Theoretical DNI - Morning Fog / Afternoon Clear South - noon DNI [W/m2] on 21 March 1000 900 East a.m. West p.m. 800 700 600 500 400 300 200 100 0 7 8 9 10 11 12 13 14 15 16 17 Solar Time North DNI Theoretical morning fog / afternoon clear 21

Theoretical DNI - Morning Fog / Afternoon Clear South - noon DNI / Solar Heat [W/m2] on 21 March 1000 900 East a.m. SE, γ = 135 p.m. better E, γ = 90 peaks noon S, γ = 180 most even performance West p.m. SW, γ = 225 a.m. better 800 700 600 500 400 300 200 100 North 0 7 8 9 10 11 12 13 14 15 16 17 Solar Time DNI S E SW SE, SM=1.34 Theoretical morning fog / afternoon clear 22

Theoretical DNI - Morning Fog / Afternoon Clear 10 8 6 4 2 Frequency Distribution of Load Factor [%] 4,7% Difference in Annual Solar Heat 2 15,8% 13,7% 15% 10,1% 1 5% 0, -5% -1-9, -12,2% -6,6% 4,7% 0 500 1.000 1.500 2.000 2.500 3.000 3.500 4.000 Hours / Year S E SE SW -15% -2 90 135 180 225 270 [deg] SE (135 ) axis orientation increases the solar heat by 15.8%., SM=1.34 Theoretical morning fog / afternoon clear 23

Real Coastal Site - Measured DNI South - noon DNI [W/m2] typical day in Spring 1000 900 East a.m. West p.m. 800 700 600 500 400 300 200 100 0 7 8 9 10 11 12 13 14 15 16 17 Local Time North DNI Site at Gulf coast 24

Real Coastal Site - Measured DNI South - noon DNI / Solar Heat [W/m2] typical day in Spring 1000 900 East a.m. SE, γ = 135 p.m. better E, γ = 90 peaks noon S, γ = 180 most even performance West p.m. SW, γ = 225 a.m. better 800 700 600 500 400 300 200 100 North 0 7 8 9 10 11 12 13 14 15 16 17 Local Time DNI S E SW SE Site at Gulf coast SM = 1.34 25

Real Coastal Site - Measured DNI 10 Frequency Distribution of Load Factor [%] Difference in Annual Solar Heat 5% 8 2,8% 2,5% 6 4 1,2% 1,5% 0, -1,6% -1,9% 1,2% 2-2,9% 0 500 1.000 1.500 2.000 2.500 3.000 3.500 4.000 Hours / Year S E SE SW -5% 90 135 180 225 270 [deg] ESE (112 ) axis orientation increases the solar heat by 2.8%. Site at Gulf coast SM = 1.34 26

Conclusions Plants with Solar Multiple SM=1.0 have the smallest solar field and the lowest investment cost [ /MW], but also the lowest energy supply [kwh/a] and the highest energy cost [LCOE, c/kwh]. Increasing the size of the solar field (larger SM up to an optimum value) will increase the load factor and the energy supply [kwh/a] and lower the energy cost [LCOE, c/kwh]. In the shown clear sky example a 5 larger solar field will result in about 9% lower energy cost. East-West orientation of the parabolic trough collector axis will supply more energy in case of small SM; for larger SM the South-North orientation will supply more energy. An oblique orientation of the parabolic trough collector axis will improve the performance in case of asymmetric irradiation (e.g. morning haze in coastal areas or afternoon dust in desert areas). For the investigated coastal site at the Gulf and measured DNI the energy supply would improve by 2.8 %, if the collector axis were oriented in ESE-WNW (112 ) direction. That means a plant with an investment volume of 350 M would be some 10 M more valuable. Optimum layout of the solar field must be carefully evaluated based on the geographic latitude, irradiation profile, demand profile, available land, storage and relative cost of components. should be performed by an experienced solar engineer. 27

Our Credo: Excellence in Solar Engineering Completely independent, only dedicated to the interest of our Clients Consulting throughout all phases of a project: Project development and feasibility; engineering and tendering; construction, erection and commissioning; operation Staff references: over 25 years of solar engineering experience for fifty (50) large CSP projects on six continents Total investment volume of consulted CSP projects: exceeding twelve billion euro, thereof some seven billion euro for sixteen large solar thermal power plants under construction or in operation Headquarter in Germany, local presence in: China, India, Egypt, Czech Republic, Ghana www.solengco.com brakmann@solengco.com Tel +49 173 66 56 161 28

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