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NCMRWF Forecast Products for Renewable Energy Applications

NCMRWF Forecast Products for Renewable Energy Applications. Raghavendra Ashrit /Sushant Kumar National Centre for Medium Range Weather Forecasting Ministry of Earth Sciences, Govt of India. Outline. Introduction A brief about Wind/Solar Energy Installed capacity over India Yearly growth

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NCMRWF Forecast Products for Renewable Energy Applications

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  1. NCMRWF Forecast Products for Renewable Energy Applications RaghavendraAshrit/Sushant Kumar National Centre for Medium Range Weather Forecasting Ministry of Earth Sciences, Govt of India

  2. Outline • Introduction • A brief about Wind/Solar Energy • Installed capacity over India • Yearly growth • Government future plan • Estimation of wind potential using Reanalysis Data • NCMRWF Unified Modelling System • Forecast Products for Wind and Solar Energy Applications • Statistical Post Processing &Bias Correction • A few interesting results • Conclusion

  3. Weather forecasts have long been important to the energy sector. • Anticipate demand. • Hot/cold weather can lead to significant spikes in consumption caused by increased air conditioning and heating. • Advance knowledge of the weather helps energy companies to predict such surges and to make sure they have adequate supplies.

  4. As solar and wind power share in the total energy increases, forecasting supply/demand is becoming increasingly important (load forecast). • The power output from these sources of energy is weather sensitive and highly variable.

  5. Policy makers : potential of wind power as part of the overall energy mix. • Wind farm operators: siting options, location and variability • Wind turbine operators: need to know when winds are forecast to be low to plan routine maintenance. • Grid operators: to estimate how much power will be generated from wind in the hours to come so they can balance the network. • Energy traders: the amount of power they will be able to trade on electricity markets on a given day.

  6. Energy is one of the priority areas in WMO’s Global Framework for Climate Services • By taking into account weather and climate information, energy systems can “considerably improve their resilience” to weather extremes, climate variability and climate change, the WMO says.

  7. Estimated Energy Shares 2014 2016

  8. Grid Interactive Installed Capacity from different sources as on 31 May 2018 Wind Power leads the Renewables (34.06 GW) but 76 % increase in Solar capacity (from 12.39 to 21.6 GW) in 2017 to 2018

  9. Mission 175 GW by 2022 India made a commitment in Paris Climate Agreement to reduce emission intensity of the economy and for having at least 40 % electric power installed capacity from clean energy sources by the year 2030

  10. Wind Energy in India Total Installed Capacity till March 2018: 34.04 GW In last 4 years capacity increased by ~1.5 times

  11. Growth of Solar Capacity in India 21600 in 2017-18 The sector has been registering exponential growth

  12. Wind Potential at 50 m estimated from MERRA Wind Potential at 50 m estimated by NIWE C B D A Higher Wind Potential States: West Rajasthan and Gujarat, West Maharashtra Karnataka, A.P. & Tamil Nadu NCMRWF, MoES, NOIDA

  13. May-Aug are more power productive months Wind Speed >3 m/s are suitable for wind power generation Evening and night have relatively stronger winds Assessment based on long term (1979-2015) MERRA 50m winds NCMRWF, MoES, NOIDA

  14. Short Term Up to 24 hrs (Power system management or energy trading) Very Short Term For few hours Turbine Active Control NWP Model Long Term Up to a few days planning the maintenance of wind farms Statistical Approach Forecasting of renewable energy Long Term Seasonal Scale

  15. Weather Forecasting at NCMRWF • The National Centre for Medium Range Weather Forecasting (NCMRWF) • Centre of Excellence in Weather and Climate Modelling under the Ministry of Earth Sciences. • The mission • To continuously develop advanced numerical weather prediction (NWP) systems • with increased reliability and accuracy over India and neighbouring regions • through research, development and demonstration of new and novel applications.

  16. Present Status: NCMRWF Unified Model (NCUM) • NCMRWF Unified Model (NCUM) - NCUM Global (12 km) - Hybrid 4D-Var DA - NCUM 4 km (Indian region) (using global analysis) – - High Resolution NCUM (1.5 km & 330 meter) (Delhi Region) - Selected periods/seasons • NCMRWF Global Ensemble Forecast System (NEPS) – 22 ensemble members (12 km resolution) (ETKF -perturbations) • Coupled ocean atmosphere system (Experimental for Extended Range Predictions) (Global Ocean DA using NEMO-Var– 0.25 Deg

  17. NCUM: Current operational models at NCMRWF NCUM-Global (~12km) 00 & 12 UTC run for 10 days Global Models Regional Models NCUM-IND Regional (4km grid) NEPS (~12km) 23 ensemble members (ETKF -perturbations) NCUM-DM (330m grid) Over Delhi NCUM-coupled (~60km)

  18. Vertical levels: 70 levels (model top 80 km) • NWP configurations has 70-levels (L70) • Hybrid vertical levels • An almost identical 50 levels below 18 km • A model lid at 80 km

  19. 23-levels in boundary layer 11-levels in 1km near ground

  20. NCMRWF Forecast Products for Wind/Solar Energy Applications • Data resolution Temporal : 1 hour (Global) & 15 minutes (Regional) Spatial : 0.125 x 0.125 deg for Global 0.04 x 0.04 deg for Regional • Vertical level : 10m, 50m & 8 Pr. Level between 1000 hPa - 925hPa for Global • Forecasting : Two cycles per day at 00 and 12 UTC up to 10 days for Global One cycle at 00UTC up to 3 days for Regional Solar Fluxes: GHI and DNI, Temperature and Cloud cover

  21. Users • NIWE Wind and Solar departments • Private agencies: (Experience > 5 years) • Manikaran Analytics Ltd • ReConnect Energy • Mittal Processors Private Limited • Leap Green Energy • AlgoEngines • Energon Power Resources Pvt Ltd

  22. Location Specific Forecasts

  23. Location Specific Forecasts Contd..

  24. Challenges in NWP

  25. Actual & Model Topography Elevation at this location from Model is 250-300m whereas in G Earth it's between 500-510m.

  26. Approximate terrain height, land use etc. • Imperfect model physics, initial conditions, and boundary conditions • systematic errors cause biases in first and second moments (Toth et al 2003)

  27. Statistical Post Processing • Post processing of forecasts is a necessary and important step for the daily operational runs at NWP centres. • Reliability, accuracy, and efficiency are the most important issues for daily operations.

  28. Moving Average Bias Correction • b1, b2 and b3 be biases for recent past three days (t-1, t-2 and t-3 respectively) • The mean bias- B=b1*(0.5)+ b2*(0.2)+ b3*(0.1) 2). Cui et al. 2012 and 2014

  29. NCMRWF Forecast 10m and 925 hPaWS over S IndiaAnalysis, Raw and Bias Corrected Forecasts

  30. Wind Speed 10m Two Wind Farm sites in AP NCUM Analysis and 12h FCST Mean (Apr-Jun)

  31. Wind Speed 925 hPa Two Wind Farm sites in AP NCUM Analysis and 12h FCST Mean (Apr-Jun)

  32. Wind Speed 10m Two Wind Farm sites in AP NCUM Analysis and 24h FCST Mean (Apr-Jun)

  33. Wind Speed 925 hPa Two Wind Farm sites in AP NCUM Analysis and 24h FCST Mean (Apr-Jun)

  34. Impact of BC : Reduced MAE in the 10m Wind Speed • MEA Reduction by- • 65%, 44% and 66% in 12h, 18h and 24h forecasts

  35. Impact of BC : Reduced MAE in the 925 hPa Wind Speed • MEA Reduction by- • 75%, 79% and 79% in 12h, 18h and 24h forecasts

  36. Observed wind at 80m Forecast wind at 50m Observed and Forecast Wind: Mean (Apr-Jun 2017) Diurnal Cycle Observed and Forecast Wind: MAE (Apr-Jun 2017) Diurnal Cycle

  37. Observed and Forecast Power Mean (Apr-Jun 2017) Diurnal Cycle 15% band

  38. Forecast on a Typical day

  39. Forecast on a Typical day

  40. Conclusions • NCMRWF is making continuous efforts to increase the model resolution and computational resources • In-house Modeling system is capable of providing specialized forecasting for Wind/Solar industry • Different Post processing techniques (AI, Statistical, ANN etc.) to be integrated with NWP forecasts • Collaborative projects between NWP centres and Power Forecasters

  41. Thanks

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