The challenge of integrating offshore wind power in the U.S. electric grid. Part I: Wind forecast error

The purpose of this two-part study is to model the effects of large penetrations of offshore wind power into a large electric system using realistic wind power forecast errors and a complete model of unit commitment, economic dispatch, and power flow. The chosen electric system is PJM Interconnection, one of the largest independent system operators in the U.S. with a generation capacity of 186 Gigawatts (GW). The offshore wind resource along the U.S. East Coast is modeled at five build-out levels, varying between 7 and 70 GW of installed capacity, considering exclusion zones and conflicting water uses.

[1]  Benjamin K. Sovacool,et al.  The intermittency of wind, solar, and renewable electricity generators: Technical barrier or rhetorical excuse? , 2009 .

[2]  Mark Z. Jacobson,et al.  The Potential of Intermittent Renewables to Meet Electric Power Demand: Current Methods and Emerging Analytical Techniques , 2012, Proceedings of the IEEE.

[3]  Robert M. Banta,et al.  Doppler Lidar–Based Wind-Profile Measurement System for Offshore Wind-Energy and Other Marine Boundary Layer Applications , 2012 .

[4]  S. Oren,et al.  Smart Flexible Just-in-Time Transmission and Flowgate Bidding , 2011, IEEE Transactions on Power Systems.

[5]  Carlos Guedes Soares,et al.  Assessing mesoscale wind simulations in different environments , 2014, Comput. Geosci..

[6]  E. Kalnay,et al.  Ensemble Forecasting at NCEP and the Breeding Method , 1997 .

[7]  Marc Keyser,et al.  Knowledge Is Power: Efficiently Integrating Wind Energy and Wind Forecasts , 2013, IEEE Power and Energy Magazine.

[8]  Willett Kempton,et al.  Electric power from offshore wind via synoptic-scale interconnection , 2010, Proceedings of the National Academy of Sciences.

[9]  Luca Delle Monache,et al.  A Weather and Climate Enterprise Strategic Implementation Plan for Generating and Communicating Forecast Uncertainty Information , 2011 .

[10]  F. Molteni,et al.  The ECMWF Ensemble Prediction System: Methodology and validation , 1996 .

[11]  O. Alsaç,et al.  DC Power Flow Revisited , 2009, IEEE Transactions on Power Systems.

[12]  Luca Delle Monache,et al.  Meteorology For Coastal/Offshore Wind Energy In The United States: Recommendations And Research Needs For The Next 10 Years , 2014 .

[13]  C. L. Archer,et al.  Spatial and temporal distributions of U.S. winds and wind power at 80 m derived from measurements , 2003 .

[14]  Moncho Gómez-Gesteira,et al.  A sensitivity study of the WRF model in wind simulation for an area of high wind energy , 2012, Environ. Model. Softw..

[15]  Alex Hall,et al.  Evaluation of the WRF PBL Parameterizations for Marine Boundary Layer Clouds: Cumulus and Stratocumulus , 2013 .

[16]  Blaise Sheridan,et al.  Calculating the offshore wind power resource: Robust assessment methods applied to the U.S. Atlantic Coast , 2012 .

[17]  John R. Birge,et al.  Introduction to Stochastic programming (2nd edition), Springer verlag, New York , 2011 .

[18]  Sue Ellen Haupt,et al.  Taming wind power with better forecasts , 2015, IEEE Spectrum.

[19]  Jay Apt,et al.  Reduction of wind power variability through geographic diversity , 2016 .

[20]  Mark Z. Jacobson,et al.  US East Coast offshore wind energy resources and their relationship to peak‐time electricity demand , 2013 .

[21]  David L. Woodruff,et al.  Toward scalable, parallel progressive hedging for stochastic unit commitment , 2013, 2013 IEEE Power & Energy Society General Meeting.

[22]  Chanan Singh,et al.  A quantitative approach to wind farm diversification and reliability , 2011 .

[23]  P. Pinson,et al.  A Transmission-Cost-Based Model to Estimate the Amount of Market-Integrable Wind Resources , 2012, IEEE Transactions on Power Systems.

[24]  Moncho Gómez-Gesteira,et al.  Sensitivity of the WRF model wind simulation and wind energy production estimates to planetary boundary layer parameterizations for onshore and offshore areas in the Iberian Peninsula , 2014 .

[25]  C. M. Kishtawal,et al.  Multimodel Ensemble Forecasts for Weather and Seasonal Climate , 2000 .

[26]  A. Hahmann,et al.  The wind energy potential of Iceland , 2014 .

[27]  Antonio Alonso Ayuso,et al.  Introduction to Stochastic Programming , 2009 .

[28]  L. Fita,et al.  Seasonal dependence of WRF model biases and sensitivity to PBL schemes over Europe , 2013 .

[29]  Kara Clark,et al.  Western Wind and Solar Integration Study , 2011 .

[30]  E. Lorenz.  Deterministic nonperiodic flow , 1963 .

[31]  Eugenia Kalnay,et al.  Atmospheric Modeling, Data Assimilation and Predictability , 2002 .

[32]  Jay Apt,et al.  The variability of interconnected wind plants , 2010 .

[33]  Cristina L. Archer,et al.  Baseload electricity from wind via compressed air energy storage (CAES) , 2012 .

[34]  Henrik Madsen,et al.  Ensemble-based Probabilistic Forecasting at Horns Rev , 2009 .

[35]  Willett Kempton,et al.  ssessing the wind field over the continental shelf as a resource for electric power , 2008 .

[36]  Mark Z. Jacobson,et al.  California offshore wind energy potential , 2010 .

[37]  W.L. Kling,et al.  Impacts of Wind Power on Thermal Generation Unit Commitment and Dispatch , 2007, IEEE Transactions on Energy Conversion.

[38]  C. Leith.  Theoretical Skill of Monte Carlo Forecasts , 1974 .

[39]  Andrew D. Stern,et al.  Forecasting the Wind to Reach Significant Penetration Levels of Wind Energy , 2011 .

[40]  Luca Delle Monache,et al.  Probabilistic Wind and Solar Power Predictions , 2014 .

[41]  G. Powers,et al.  A Description of the Advanced Research WRF Version 3 , 2008 .

[42]  C. L. Archer,et al.  Evaluation of global wind power , 2005 .

[43]  Willett Kempton,et al.  Cost-minimized combinations of wind power, solar power and electrochemical storage, powering the grid up to 99.9% of the time , 2013 .

[44]  Mark Z. Jacobson,et al.  Supplying Baseload Power and Reducing Transmission Requirements by Interconnecting Wind Farms , 2007 .

[45]  Martin Greiner,et al.  Validation of Danish wind time series from a new global renewable energy atlas for energy system analysis , 2014, 1409.3353.

[46]  L. Soder.  Simulation of wind speed forecast errors for operation planning of multiarea power systems , 2004, 2004 International Conference on Probabilistic Methods Applied to Power Systems.

[47]  H. Madsen,et al.  Benefits and challenges of electrical demand response: A critical review , 2014 .

[48]  Aoife Foley,et al.  Current methods and advances in forecasting of wind power generation , 2012 .

[49]  Eugenia Kalnay,et al.  Ensemble Forecasting at NMC: The Generation of Perturbations , 1993 .

[50]  Henrik Madsen,et al.  Impact of Wind Power Generation on European Cross-Border Power Flows , 2013, IEEE Transactions on Power Systems.

[51]  P. Jaramillo,et al.  Variable Renewable Energy and the Electricity Grid , 2014 .

[52]  L Glascoe,et al.  Impact of WRF Physics and Grid Resolution on Low-level Wind Prediction: Towards the Assessment of Climate Change Impact on Future Wind Power , 2010 .

[53]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[54]  Cristian Mattar,et al.  Offshore wind power simulation by using WRF in the central coast of Chile , 2016 .

[55]  M. Lange,et al.  Physical Approach to Short-Term Wind Power Prediction , 2005 .

[56]  Arianna Naimo,et al.  A Novel Approach to Generate Synthetic Wind Data , 2014 .

[57]  Mark Z. Jacobson,et al.  Where is the ideal location for a US East Coast offshore grid? , 2012 .

[58]  C. F. Ratto,et al.  Optimization of the Regional Spatial Distribution of Wind Power Plants to Minimize the Variability of Wind Energy Input into Power Supply Systems , 2008 .

[59]  Thomas J. Overbye,et al.  A comparison of the AC and DC power flow models for LMP calculations , 2004, 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the.

[60]  W. Kempton,et al.  Large CO2 reductions via offshore wind power matched to inherent storage in energy end‐uses , 2007 .