Renewable Energy

Advanced Technologies for Renewable Energy Prediction

NCAR is uniquely qualified to help support our nation's transition to renewable energy due to NCAR's breadth of atmospheric science knowledge, experience with technology transfer, and access to university researchers. These capabilities led NCAR to include a new research frontier in the 2009 NCAR Strategic Plan.

NCAR will develop methods to more accurately assess and predict wind and cloud cover (insolation) in support of renewable energy industries. Understand the impacts of biofuels and other renewable energy technologies on water resources and regional climate.

Shifting the nation's energy portfolio toward renewable energy sources, such as wind, solar power, and biofuels, is a national priority. Atmospheric science has a role in developing these resources: important meteorological and climatic factors influence the amount of energy available from these sources, and renewable energy developments themselves can have climate and environmental impacts.

Improved understanding of the atmospheric boundary layer and the interaction of flow regimes with variable topography is crucial for developing wind resources. There is now widespread recognition that poor characterization of the atmospheric conditions (e.g., wind shear, turbulence) in which wind turbines operate is hindering the development of their energy–generation potential: wind farms are under–producing by 15–20%, and turbines that are designed for a 20–year lifetime are failing in less than five years. The efficiency of future power grids can be substantially improved by using accurate and detailed short–term weather predictions to control renewable power generation systems. New sensors and weather prediction systems are needed for future grids that may include energy storage components. Finally, in the area of biofuels, cultivating new crops for scaled up production could significantly change land–use patterns, which, in turn, could negatively impact soil erosion, water resources, and regional climate. NCAR has significant expertise in all of these areas. We plan to work with collaborators to develop weather and climate research programs focused on:

  • Infrastructure planning and management, such as boundary layer studies and characterization of land use interactions with regional climate
  • Develop partnerships with the National Renewable Energy Laboratory, commercial weather service providers, and industries that are investing in wind power systems to develop, evaluate, and improve sensor technology, observational systems, and short-term wind prediction systems
  • Investigate the potential value of improved short–term and seasonal weather prediction for determining energy demands, management of energy supply, pricing and markets, system operations and regulatory compliance, and minimization of economic risk

Partners

  • Electric Power Research Institute
  • Government of Bangladesh (GOB)
  • Harness Energy  (USAID) 
  • New York Power Authority
  • New York State Energy Research and Development Authority
  • NREL
  • U.S. Department of Energy Office of Energy Efficiency & Renewable Energy
  • Vaisala
  • Xcel

Representative Projects

  • Boundary-Layer Processes That Affect Wind-Energy Production: Ran the WRF-based RTFDDA LES model to study the complex planetary boundary layer (PBL) processes and wind characteristics in the lowest 200–300 m at wind farms to gain fundamental knowledge for developing an innovative wind-energy forecasting capability.
  • NCAR's Contribution To Wind And Solar Energy Prediction: Supported the renewable-energy industry with improved weather prediction, precise spatial analysis of small-scale weather events, and greater understanding of how topography, surface roughness, ground cover, temperature inversions, foliage, gravity waves, low–level jets, clouds, and aerosols, all affect wind and solar-energy prediction skill.
  • Offshore Wind Resource Assessment for Alaska: Partnered with NREL to perform a wind resource assessment for Alaska’s offshore regionsto identify offshore wind development viability in identified locations
  • Solar Forecasting for New York State: Undertook a multi-phase, multi-agency effort to improve solar forecasting in New York State using a WRF-Solar® 10-member ensemble, a network of sky cameras, and observations to determine irradiance variability.
  • Solar Power Forecasting Partnership: Led a partnership to advance the state-of-the-science of solar power forecasting which involved cutting edge research, testing the forecasts in several geographically- and climatologically-diverse high-penetration solar utilities, and widely disseminating the research results to improve solar power forecasting technology
  • Wind Forecasting Improvement Project (WFIP2): Compared data from the Columbia River Gorge to develop and validate new modeling capabilities for high-resolution mesoscale flow simulations over complex terrain using the Weather Research and Forecasting (WRF) – Advanced Research WRF (ARW) to enhance wind forecasting skill in complex terrain
  • Wind Resource Modeling in Bangladesh: Partnered with NREL, Harness Energy (of USAID) to assist the Government of Bangladesh (GOB), to assess wind resource and feasibility to meet their goal of supplying 10% of its national electricity generation from renewable sources by 2021
  • WRF-Solar V2: An enhanced version of the Weather Research and Forecasting model – WRF-Solar® modeL was developed to improve forecasting of solar irradiance at the surface in hour-ahead to day-ahead range for renewable energy applications

Search through all publications in NCAR's OpenSky Library.

  • Haupt, S. E., B. Kosovic, T. Jensen, J. K. Lazo, J. A. Lee, P. A. Jiménez, J. Cowie, G. Wiener, T. C. McCandless, M. Rogers, S. Miller, M. Sengupta, Y. Xie, L. Hinkelman, P. Kalb, and J. Heiser, 2018: Building the Sun4Cast system: Improvements in solar power forecasting. Bull. Amer. Meteor. Soc. 99, 121-135, https://doi.org/10.1175/BAMS-D-16-0221.1.
  • Jiménez, P. A., J. P. Hacker, J. Dudhia, S. E. Haupt, J. A. Ruiz-Arias, C. A. Gueymard, G. Thompson, T. Eidhammer and A. Deng, 2016: WRF-Solar: Description and clear-sky assessment of an augmented NWP model for solar power prediction. Bull. Amer. Meteor. Soc., 97, 1249-1264, https://doi.org/10.1175/BAMS-D-14-00279.1.
  • Yang, J., J. H. Kim, P. A. Jimenez, M. Sengupta, J. Dudhia, Y. Xie, A. Golnas and R. Giering, 2020: An efficient method to identify uncertainties of WRF-Solar variables in forecasting solar irradiance using a tangent linear sensitivity analysis. Solar Energy (In press)

  • Yang, J., Sengupta, M., Xie, Y., Jimenez, P.A. and Kim, J.H., 2019. Adjoint Sensitivity of FARMS to the Forecasting Variables of WRF-Solar. In 36th European Photovoltaic Solar Energy Conference and Exhibition.
  • Kim, J.H., Jimenez, P.A., Dudhia, J., Yang, J., Sengupta, M., Xie, Y., 2020, “Probabilistic Forecast of All-sky Solar Radiation Using Enhanced WRF-Solar”, In 37th European Photovoltaic Solar Energy Conference and Exhibition.
  • Lee, J. A., S. Dettling, S. E. Haupt, and T. Brummet, 2019: Advancing solar irradiance nowcasts on Long Island: Blending WRF-Solar with observations. 10th Conf. on Weather, Climate, and the New Energy Economy, Phoenix, AZ, Amer. Meteor. Soc., 4.2, https://ams.confex.com/ams/2019Annual/meetingapp.cgi/Paper/353194.

 

Book Chapters

  • Haupt, S. E., W. P. Mahoney, and K. Parks, 2013: Wind power forecasting. In: Troccoli, A., L. Dubus, and S. E. Haupt (Eds.), Weather matters for energy, Springer, 528 pp., https://doi.org/10.1007/978-1-4614-9221-4.
  • Haupt, S. E., P. A. Jiménez, J. A. Lee, and B. Kosović, 2017: Principles of meteorology and numerical weather prediction. In:  Kariniotakis, G. (Ed.), Renewable energy forecasting: From models to applications. Woodhead Publishing, Cambridge, MA, 373 pp., https://doi.org/10.1016/B978-0-08-100504-0.00001-9.
  • Haupt, S. E., B. Kosović, J. A. Lee, and P. A. Jiménez, 2019: Mesoscale modeling of the atmosphere. In: Veers, P. (Ed.), Wind power modelling: Atmosphere and wind plant flow. IET Publishing, Stevenage, UK, in press (expected release 26 Jun 2019).
  • Jiménez, P. A., J. A. Lee, S. E. Haupt, and B. Kosović, 2019: Solar resource evaluation with numerical weather prediction models. In: J. Polo et al. (Eds.), Solar resources mapping: Fundamentals and applications. Green Energy and Technology, Springer Nature, Cham, Switzerland, 367 pp., https://doi.org/10.1007/978-3-319-97484-2_7.

Journal Articles and Conference Proceedings

  • Doubrawa, P., G. Scott, W. Musial, L. Kilcher, C. Draxl, and E. Lantz, 2017: Offshore wind energy resource assessment for Alaska. NREL Tech. Report NREL/TP-5000-70553, 29 pp., https://www.nrel.gov/docs/fy18osti/70553.pdf.
  • Lee, J. A., P. Doubrawa, L. Xue, A. J. Newman, C. Draxl, and G. Scott, 2019: Wind resource assessment for Alaska's offshore regions: Validation of a 14-year high-resolution WRF dataset. J. Renew. Sustain. Energy, submitted.
  • Lee, J. A., L. Xue, A. J. Newman, A. J. Monaghan, P. Doubrawa, C. Draxl, L. Kilcher, and G. Scott, 2018: Validation of a 14-year high-resolution WRF dataset for wind resource assessment over Alaska. 9th Conf. on Weather, Climate, and the New Energy Economy, Austin, TX, Amer. Meteor. Soc., 1.3, https://ams.confex.com/ams/98Annual/webprogram/Paper327317.html.
  • Monaghan. A. J., M. P. Clark, M. P. Barlage, A. J. Newman, L. Xue, J. R. Arnold, and R. M. Rasmussen, 2016: High-resolution climate simulations over Alaska: A community dataset, version 1. National Center for Atmospheric Research/Earth System Grid, https://doi.org/10.5065/D61Z42T0.
  • Monaghan, A. J., M. P. Clark, M. P. Barlage, A. J. Newman, L. Xue, J. R. Arnold, and R. M. Rasmussen, 2018: High-resolution climate simulations over Alaska. J. Appl. Meteor. Climatol., 57, 709–731, https://doi.org/10.1175/JAMC-D-17-0161.1.
  • Gueymard, C. A., and P. A. Jiménez, 2018: Validation of real-time solar irradiance simulations over Kuwait using WRF-Solar. 12th Int. Conf. on Solar Energy for Buildings and Industry (EuroSun 2018). Rapperswill, Switzerland, Int. Solar Energy Soc., 2.A-1,https://doi.org/10.18086/eurosun2018.09.14.
  • Haupt, S. E., and B. Kosović, 2017: Variable generation power forecasting as a big data problem. IEEE Trans. Sustain. Energy, 8, 725–732, https://doi.org/10.1109/TSTE.2016.2604679.
  • Haupt, S. E., and W. P. Mahoney, 2015: Taming wind power with better forecasts. IEEE Spectrum, 52, 47–52, https://doi.org/10.1109/MSPEC.2015.7335902.
  • Haupt, S. E., B. Kosovic, T. Jensen, J. K. Lazo, J. A. Lee, P. A. Jiménez, J. Cowie, G. Wiener, T. C. McCandless, M. Rogers, S. Miller, M. Sengupta, Y. Xie, L. Hinkelman, P. Kalb, and J. Heiser, 2018: Building the Sun4Cast system: Improvements in solar power forecasting. Bull. Amer. Meteor. Soc.99, 121-135, https://doi.org/10.1175/BAMS-D-16-0221.1.
  • Jiménez, P. A., J. P. Hacker, J. Dudhia, S. E. Haupt, J. A. Ruiz-Arias, C. A. Gueymard, G. Thompson, T. Eidhammer and A. Deng, 2016: WRF-Solar: Description and clear-sky assessment of an augmented NWP model for solar power prediction. Bull. Amer. Meteor. Soc., 97, 1249-1264, https://doi.org/10.1175/BAMS-D-14-00279.1.
  • Jiménez, P. A., S. Alessandrini, S. E. Haupt, A. Deng, B. Kosović, J. A. Lee, and L. Delle Monache, 2016b: The role of unresolved clouds on short-range global horizontal irradiance predictability. Mon. Wea. Rev., 144, 3099–3107, https://doi.org/10.1175/MWR-D-16-0104.1.
  • Lee, J. A., S. E. Haupt, P. A. Jiménez, M. A. Rogers, S. D. Miller, and T. C. McCandless, 2017: Solar irradiance nowcasting case studies near Sacramento. J. Appl. Meteor. Climatol., 56, 85–108, https://doi.org/10.1175/JAMC-D-16-0183.1.
  • Mahoney, W. P., K. Parks, G. Wiener, Y. Liu, W. L. Myers, J. Sun, L. Delle Monache, T. Hopson, D. Johnson, and S. E. Haupt, 2012: A wind power forecasting system to optimize grid integration. IEEE Trans. Sustain. Energy, 3, 670–682, https://doi.org/10.1109/TSTE.2012.2201758.
  • Lee, J. A., M. Jacobson, T. Capozzola, C. Draxl, F. Vandenberghe, T. Jimenez, and S. E. Haupt, 2019: Assessment of the wind energy potential in Bangladesh. 10th Conf. on Weather, Climate, and the New Energy Economy, Phoenix, AZ, Amer. Meteor. Soc., 9.2, https://ams.confex.com/ams/2019Annual/meetingapp.cgi/Paper/353210.


Technical Reports

  • Haupt, S. E., A. Anderson, L. Berg, B. Brown, M. J. Churchfield, C Draxl, B. L. Ennis, Y. Fang, B. Kosovic, R. Kotamarthi, R. Linn, J. D. Mirocha, P. Moriarty, D. Munoz-Esparaza, R. Rai, and W. J. Shaw, 2015: First Year Report of the A2e Mesoscale to Microscale Coupling Project. Pacific Northwest National Laboratory Report PNNL-25108, 124 pp.
  • Haupt, S. E., A. Anderson, R. Kotamarthi, J. J. Churchfield, Y. Feng, C. Draxl, J. D. Mirocha, E. Quon, E. Koo, W. Shaw, R. Linn, L. Berg, B. Kosovic, R. Rai, B. Brown, and B. L. Ennis, 2017: Second Year Report of the Atmosphere to Electrons Mesoscale to Microscale Coupling Project: Nonstationary Modeling Techniques and Assessment. Pacific Northwest National Laboratory Report PNNL-26267, 156 pp., https://www.pnnl.gov/main/publications/external/technical_reports/PNNL-26267.pdf.
  • Haupt, S. E., A. Anderson, L. Berg, B. Brown, M. Churchfield, C. Draxl, C. Kalb, E. Koo, B. Kosovic, R. Kotamarthi, L. Mazzaro, J. Mirocha, E. Quon, R. Rai, and G. Sever, 2017: Third Year Report of the Atmosphere to Electrons Mesoscale to Microscale Coupling Project. Pacific Northwest National Laboratory Report PNNL-28259, 137 pp.
  • Haupt, S. E., D. Allaerts, L. Berg, M. Churchfield, A. DeCastro, C. Draxl, E. Koo, B. Kosovic, R. Kotamarthi, B. Kravitz, L. Mazzaro, J. Mirochoa, E. Q uon, R. Raj, J. Sauer, and G. Sever, 2019: Fourth Year Report of the Atmosphere to Electrons Mesoscale to Microscale Coupling Project. Pacific Northwest Laboratory Report PNNL-28259.
  • Jacobson, M., C. Draxl, T. Jimenez, B. O’Neill, T. Capozzola, J. A. Lee, F. Vandenberghe, and S. E. Haupt, 2018: Assessing the wind energy potential in Bangladesh. NREL Tech. Report NREL/TP-5000-71077, 136 pp., https://www.nrel.gov/docs/fy18osti/71077.pdf.

Renewable Energy