Simulation of parabolic trough concentrating solar power plants in North Africa

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1 Simulation of parabolic trough concentrating solar power plants in North Africa And its contribution to a 100% renewable energy supply for Germany in 2050 BY: Daniel Horst

2 Outline Simulation program CSP plants site evaluation in North Africa Performance simulation CSP plants Export scenario CSP for the year 2050 Residual load curve 2050 Installed capacity 2050 Optimisation (Curve fitting) Calculation of effective load carrying capacity (ELCC) and capacity credit (CC) Outline

3 Basic program structure CSP plants site evaluation for North Africa Optimization and ELCC calculation CSP performance simulation Simulation program / Program structure

4 Positioning CSP plants Possible CSP sites (grid and land use): CSP plant site evaluation process example for 2020: Simulation program / Site evaluation

5 CSP performance simulation Solar field: - Losses according to sun collector geometry - Optical efficiency -Thermal losses during transportation - Solar field reduction Thermal factor storage: λ storage = q nom η Poweblock 0 h if SM1 6 h if SM2 12 h if SM3 18 h if SM4 Simulation program / Performance simulation

6 Power block Electrical output CSP: Wet cooling system: Evaporation cooling system: Dry cooling system: Simulation program / Performance simulation

7 Feed in time series CSP Comparison between different SM configurations: Plant performance calculation at a location of 31 N 29 E for the meteorological year Collector orientation is north south. Comparison between different cooling system configurations: Simulation program / Performance simulation

8 SIM-EE model Residual load curve for Germany in 2050: Residual load curve from the UBA study 100% renewable electricity supply by 2050 for the meteorological year Export scenario 2050 / Residual load

9 Installed capacity Installed capacity CSP plants in North Africa Distribution CSP plants in North Africa: Export scenario 2050 / Installed capacity

10 Installed capacity Installed capacity CSP plants in North Africa Installed capacity power plant network in Germany: Export scenario 2050 / Installed capacity

11 Optimization Storage capacity (SM4): 22.5 GWh th Net capacity: MW el Time horizon: 72 hours Update frequency: 24 hours Export scenario 2050 / Optimization

12 Results (Optimization) 23.6 TWh/year 28.3 TWh/year New demand 39.4 TWh New demand 34.7 TWh Export energy : 30.5 TWh/year Export energy : 47.1 TWh/year 31.8 TWh/year New demand 31.2 TWh Storage capacity (SM2): 7.5 GWh th Storage capacity (SM3): 15.0 GWh th Storage capacity (SM4): 22.5 GWh th Time horizon: 72 hours Update frequency: 24 hours Export energy : 64.1 TWh/year Export scenario 2050 / Optimization

13 Results (Optimization) Export scenario 2050 / Optimization

14 Results (Optimization) Peak demand: 50 GW Peak demand: 47 GW Export scenario 2050 / Optimization Peak demand: 44 GW

15 ELCC calculation Recursive convolution example for 3 power plants : Export scenario 2050 / ELCC calculation

16 Results ELCC calculation Effective load carrying capacity chronological calculation for the meteorological year 2007 (Installed capacity CSP North Africa is 17.6 GW): Export scenario 2050 / ELCC calculation

17 Conclusion Simulation Program Positioning of plants can be seen as a first approach. Accurate reproduction of CSP energy production depending on storage and cooling system. Export Scenario for 2050 For SM3 or SM4 assumed import energy for Germany (Hydrogen scenario) can be seen as secured energy. Seasonality of CSP production limits support possibilities for Germany during winter times. Good smoothing results of demand during summer times. Reasonable results only with SM2 or higher. Conclusion

18 Outlook Simulation program Adding additional parameters for CSP plant position (e.g. HVDC export transmission grid). Adding condenser capacity curves and humidity data from COSMO-EU model to the performance calculation. More advanced storage models in the performance calculation. Export scenario for 2050 More accurate assumption regarding to available energy for export in North Africa. Removing mismatches in curve fitting process. Feed in time series for more the one year for ELCC and CC calculation. Power plant resource scheduling. Outlook

19 THE END Thanks for your attention

20 Seasonality CSP plant CSP plant SM1 COSMO-EU data 2007 Seasonality CSP plant

Dr : Mohamed Ahmed Ebrahim

Dr : Mohamed Ahmed Ebrahim Benha University Faculty of Engineering at Shoubra Electrical Engineering Dept. Postgraduate (Pre-master) Course E-mail: mohamedahmed_en@yahoo.com Web site: http://bu.edu.eg/staff/mohamedmohamed033 1 2

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