The Potential of Intermittent Renewables to Meet Electric Power Demand: Current Methods and Emerging Analytical Techniques

Renewable electric power sources like wind and solar have been shown from a resource perspective to have significant potential to reduce the carbon dioxide emissions associated with the electric power sector. However, the intermittency of these resources is often cited as a barrier to their large scale integration into the grid. In this review, we provide a framework for understanding the body of literature that has been devoted to the behavior and reliability of intermittent renewables and discuss recent grid integration analyses within this framework. The modeling approaches required for system characterization are found to depend on the energy penetration of the intermittent technology and recent simulations reveal substantially different behavior in low- and high-penetration regimes. We describe an analytical approach that addresses both penetration regimes and can be used to incorporate the results of grid integration studies into decarbonization strategy analyses.

[1]  E. Gilder,et al.  The Authors , 1977 .

[2]  Edward Kahn,et al.  The reliability of distributed wind generators , 1979 .

[3]  G. L. Johnson,et al.  Wind energy systems , 1985 .

[4]  R. D. Richardson,et al.  Wind energy systems , 1993, Proc. IEEE.

[5]  Joachim Luther,et al.  Power fluctuations in spatially dispersed wind turbine systems , 1993 .

[6]  S. Watson,et al.  Application of wind speed forecasting to the integration of wind energy into a large scale power system , 1994 .

[7]  M. Milligan,et al.  Estimating the Economic Value of Wind Forecasting to Utilities , 1995 .

[8]  Turner,et al.  A realizable renewable energy future , 1999, Science.

[9]  A. Sahin Applicability of Wind?Solar Thermal Hybrid Power Systems in the Northeastern Part of the Arabian Peninsula , 2000 .

[10]  K. Kiefer,et al.  Power characteristics of PV ensembles: experiences from the combined power production of 100 grid connected PV systems distributed over the area of Germany , 2001 .

[11]  Ulrich Focken,et al.  A statistical analysis of the reduction of the wind power prediction error by spatial smoothing effects , 2001 .

[12]  G. Masters,et al.  Exploiting Wind Versus Coal , 2001, Science.

[13]  Ulrich Focken,et al.  Short-term prediction of the aggregated power output of wind farms—a statistical analysis of the reduction of the prediction error by spatial smoothing effects , 2002 .

[14]  Michael Milligan,et al.  Output Power Correlation Between Adjacent Wind Power Plants , 2003 .

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

[16]  Gilbert M. Masters,et al.  Renewable and Efficient Electric Power Systems: Masters/Electric Power Systems , 2004 .

[17]  N. Jenkins,et al.  Comparison of the response of doubly fed and fixed-speed induction generator wind turbines to changes in network frequency , 2004, IEEE Transactions on Energy Conversion.

[18]  Koji Yamaguchi,et al.  Smoothing effects of distributed wind turbines. Part 2. Coherence among power output of distant wind turbines , 2004 .

[19]  Koji Yamaguchi,et al.  Smoothing effects of distributed wind turbines. Part 1. Coherence and smoothing effects at a wind farm , 2004 .

[20]  S Pacala,et al.  Stabilization Wedges: Solving the Climate Problem for the Next 50 Years with Current Technologies , 2004, Science.

[21]  Gilbert M. Masters,et al.  Renewable and Efficient Electric Power Systems , 2004 .

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

[23]  Joseph F. DeCarolis,et al.  The Costs of Wind's Variability: Is There a Threshold? , 2005 .

[24]  Andrew Boone,et al.  Simulation of Short-term Wind Speed Forecast Errors using a Multi-variate ARMA(1,1) Time-series Model , 2005 .

[25]  Henrik Lund,et al.  Electric grid stability and the design of sustainable energy systems , 2005 .

[26]  H. Holttinen Impact of hourly wind power variations on the system operation in the Nordic countries , 2005 .

[27]  Hannele Holttinen,et al.  Hourly wind power variations in the Nordic countries , 2005 .

[28]  M. Milligan,et al.  The Capacity Value of Wind in the United States: Methods and Implementation , 2006 .

[29]  Joseph F. DeCarolis,et al.  The economics of large-scale wind power in a carbon constrained world , 2006 .

[30]  Henrik Lund,et al.  Large-scale integration of optimal combinations of PV, wind and wave power into the electricity supply , 2006 .

[31]  G. Sinden Characteristics of the UK wind resource: Long-term patterns and relationship to electricity demand , 2007 .

[32]  D. Berg,et al.  Wind Energy Resources , 2007 .

[33]  Paul Denholm,et al.  Evaluating the limits of solar photovoltaics (PV) in electric power systems utilizing energy storage and other enabling technologies , 2007 .

[34]  P. Meibom,et al.  Wind power integration studies using a multi-stage stochastic electricity system model , 2007, 2007 IEEE Power Engineering Society General Meeting.

[35]  Henrik Lund,et al.  Renewable energy strategies for sustainable development , 2007 .

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

[37]  N. Duić,et al.  Two energy system analysis models: A comparison of methodologies and results , 2007 .

[38]  N. Stern The Economics of Climate Change: Implications of Climate Change for Development , 2007 .

[39]  H. G. Beyer,et al.  Qualified Forecast of Ensemble Power Production by Spatially Dispersed Grid-Connected PV Systems , 2008 .

[40]  J. Apt,et al.  The character of power output from utility‐scale photovoltaic systems , 2008 .

[41]  Robert Gross,et al.  The costs and impacts of intermittency: An ongoing debate: "East is East, and West is West, and never the twain shall meet." , 2008 .

[42]  F. Hocaoglu,et al.  Hourly solar radiation forecasting using optimal coefficient 2-D linear filters and feed-forward neural networks , 2008 .

[43]  R. Barthelmie,et al.  The economic benefit of short-term forecasting for wind energy in the UK electricity market , 2008 .

[44]  Jiacong Cao,et al.  Study of hourly and daily solar irradiation forecast using diagonal recurrent wavelet neural networks , 2008 .

[45]  G. Papaefthymiou,et al.  MCMC for Wind Power Simulation , 2008, IEEE Transactions on Energy Conversion.

[46]  Vassilios G. Agelidis,et al.  Wind-solar resource complementarity and its combined correlation with electricity load demand , 2009, 2009 4th IEEE Conference on Industrial Electronics and Applications.

[47]  Brian Vad Mathiesen,et al.  Energy system analysis of 100% renewable energy systems-The case of Denmark in years 2030 and 2050 , 2009 .

[48]  Abraham Ellis,et al.  Understanding Variability and Uncertainty of Photovoltaics for Integration with the Electric Power System , 2009 .

[49]  Heike Brand,et al.  WILMAR: A Stochastic Programming Tool to Analyze the Large-Scale Integration of Wind Energy , 2009 .

[50]  Henrik Lund,et al.  Renewable Energy Systems: The Choice and Modeling of 100% Renewable Solutions , 2009 .

[51]  P. Sauer,et al.  Wind power day-ahead uncertainty management through stochastic unit commitment policies , 2009, 2009 IEEE/PES Power Systems Conference and Exposition.

[52]  J. Kiviluoma,et al.  Global potential for wind-generated electricity , 2009, Proceedings of the National Academy of Sciences.

[53]  Birgitte Bak-Jensen,et al.  Markov model of wind power time series using Bayesian inference of transition matrix , 2009, 2009 35th Annual Conference of IEEE Industrial Electronics.

[54]  Brian Vad Mathiesen,et al.  Comparative analyses of seven technologies to facilitate the integration of fluctuating renewable energy sources , 2009 .

[55]  M. O'Malley,et al.  Unit Commitment for Systems With Significant Wind Penetration , 2009, IEEE Transactions on Power Systems.

[56]  G. Cornelis van Kooten,et al.  Wind integration into various generation mixtures , 2009 .

[57]  Jay Apt,et al.  Air emissions due to wind and solar power. , 2009, Environmental science & technology.

[58]  Jian Ma,et al.  Operational Impacts of Wind Generation on California Power Systems , 2009, IEEE Transactions on Power Systems.

[59]  Mukesh Pandey,et al.  Correlation analysis of small wind–solar–biomass hybrid energy system installed at RGTU Bhopal, MP (India) , 2010 .

[60]  A. Mills,et al.  Implications of Wide-Area Geographic Diversity for Short- Term Variability of Solar Power , 2010 .

[61]  Orhan Ekren,et al.  Size optimization of a PV/wind hybrid energy conversion system with battery storage using simulated annealing , 2010 .

[62]  Wei Zhou,et al.  Current status of research on optimum sizing of stand-alone hybrid solar–wind power generation systems , 2010 .

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

[64]  Francesco Fusco,et al.  Variability reduction through optimal combination of wind/wave resources – An Irish case study , 2010 .

[65]  Brian Vad Mathiesen,et al.  A review of computer tools for analysing the integration of renewable energy into various energy systems , 2010 .

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

[67]  Mark Z. Jacobson,et al.  Power output variations of co-located offshore wind turbines and wave energy converters in California , 2010 .

[68]  Nicholas Jenkins,et al.  Statistics of wind farm power output: methods and applications , 2010 .

[69]  Tai Nengling,et al.  Review of contribution to frequency control through variable speed wind turbine , 2011 .

[70]  Kristina Hamachi LaCommare Power and Frequency Control as it Relates to Wind-Powered Generation , 2011 .

[71]  Andrew D. Mills,et al.  Implications of geographic diversity for short-term variability and predictability of solar power , 2011, 2011 IEEE Power and Energy Society General Meeting.

[72]  Mark Z. Jacobson,et al.  A Monte Carlo approach to generator portfolio planning and carbon emissions assessments of systems with large penetrations of variable renewables. , 2011 .

[73]  Joseph H. Eto,et al.  Use of Frequency Response Metrics to Assess the Planning and Operating Requirements for Reliable Integration of Variable Renewable Generation , 2011 .

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

[75]  W. Marsden I and J , 2012 .

[76]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.