Solar forecasting for grid management with high PV penetration

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1 Solar forecasting for grid management with high PV penetration Lu ZHAO, Wilfred Walsh Solar Energy Research Institute of Singapore (SERIS) InterMET Asia 23 Apri !

2 Presentation outline About SERIS Solar forecasting! Motivation! Forecasting methods " 0 to 6 hours ahead (intra-day) " 6 hours to days ahead Point to area forecast! upscaling Solar potential and GIS Conclusion 2!

3 SERIS Solar Energy Research Institute of Singapore! Founded in 2008; focuses on applied solar energy research! Focuses on PV R&D (cells, modules, systems)! Two R&D pilot lines: Si wafer solar cells, Si thin-film solar cells in class 10,000 cleanroom! Solar irradiance and PV systems monitoring network 3!

4 Solar irradiance and meteorological network 25 meteorological stations deployed by SERIS A. Nobre et al., NI week 2012, Keynote Speech 4!

5 Motivation! High Penetration of PV Systems in Electricity Grids " Strong growth " Significant share of overall power production! Two main challenges " Variability " Uncertainty (Movement and evolution of the clouds)! Impact on grid stability and scheduling 5!

6 Solar irradiance forecasting Forecast on various time scales! Instantaneous supply-demand matching (a few minutes)! Network load balancing (10s of minutes ~ a few hours)! Scheduling for generators and futures markets (day-ahead) Beyer et al. (2009) S. Miller et al. Colorado State University 6!

7 Statistical forecasting short term Time series analysis! Persistence! ARIMA! Exponential smoothing! Spatio-temporal kriging! + exogenous inputs D. Yang et al., Energy 81 (2015) !

8 Artificial intelligence short term! Convective cloud formation is a nonlinear dynamic process captured in a high dimensional phase space! AI methods, such as ANN, SVM, etc., are capable of modeling such nonlinear dependencies 8!

9 Cloud advection short, medium term Surface-based trajectory! Identify Clouds! Cloud Positioning! Track Cloud Movement! Predict GHI/Power Satellite-based trajectory J. Kleissl et al., Solar Energy and Resource Assessment Chow et al. (2011) 9!

10 NWP long term (6h to days-ahead)! Requires data assimilation and post-processing " Spatio-temporal interpolation and smoothing " bias removal! Ensemble forecast (combining various NWP modes) 10!

11 Point to area forecast Regional forecast of PV power Modeling solar plant variability 11!

12 Solar potential, GIS data and analysis! Build a 3D+ representation of Singapore and other Asian cities.! Dynamic tool to forecast PV power and link it to the grid and demand! MIT Sustainable Design Lab 12!

13 Summary and outlook The field of solar and PV forecasting is rapidly evolving Various methods are applied, suitable for different time horizons ranging from a few minutes ahead to several days ahead SERIS! Built a solar irradiance monitoring network in Singapore! Targeted leadership in managing the variability of PV for grid management with high PV penetration! Building a integrated GIS system for solar potential assessment and as a rich information platform 13!

14 Thank you for your attention! More information 14!

15 Thursday, 23 April Thank You PROTECTING PEOPLE AND ASSETS 15 15!

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