The Costs of Electricity Systems with a High Share of Fluctuating Renewables: A Stochastic Investment and Dispatch Optimization Model for Europe

Renewable energies are meant to produce a large share of the future electricity demand. However, the availability of wind and solar power depends on local weather conditions and therefore weather characteristics must be considered when optimizing the future electricity mix. In this article we analyze the impact of the stochastic availability of wind and solar energy on the cost-minimal power plant mix and the related total system costs. To determine optimal conventional, renewable and storage capacities for dierent shares of renewables, we apply a stochastic investment and dispatch optimization model to the European electricity market. The model considers stochastic feed-in structures and full load hours of wind and solar technologies and dierent correlations between regions and technologies. Key ndings include the overestimation of uctuating renewables and underestimation of total system costs compared to deterministic investment and dispatch models. Furthermore, solar technologies are - relative to wind turbines - underestimated when neglecting negative correlations between wind speeds and solar radiation.

[1]  C. Weber.  Uncertainty in the electric power industry , 2004 .

[2]  Eduardo Marco Modiano,et al.  Derived Demand and Capacity Planning Under Uncertainty , 1987, Oper. Res..

[3]  Ninghong Sun,et al.  Model-based long-term electricity generation system planning under uncertainty , 2008, 2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies.

[4]  Alberto J. Lamadrid,et al.  The Hidden System Costs of Wind Generation in a Deregulated Electricity Market , 2012 .

[5]  Michael Beenstock.  The stochastic economics of windpower , 1995 .

[6]  John R. Birge,et al.  Introduction to Stochastic Programming , 1997 .

[7]  Matthias Fripp,et al.  Optimal investment in wind and solar power in California , 2008 .

[8]  Amany von Oehsen,et al.  Langfristszenarien und Strategien für den Ausbau der erneuerbaren Energien in Deutschland bei Berücksichtigung der Entwicklung in Europa und global , 2012 .

[9]  Michael Schlesinger,et al.  Szenarien für ein Energiekonzept der Bundesregierung , 2010 .

[10]  Jan Richter,et al.  DIMENSION - A Dispatch and Investment Model for European Electricity Markets , 2011 .

[11]  Martin Greiner,et al.  Seasonal optimal mix of wind and solar power in a future, highly renewable Europe , 2010 .

[12]  P. Joskow.  Comparing the Costs of Intermittent and Dispatchable Electricity Generating Technologies , 2011 .

[13]  Cosima Jägemann.  A Note on the Inefficiency of Technology- and Region-Specific Renewable Energy Support: The German Case , 2014 .

[14]  Frieder Borggrefe,et al.  The potential of demand-side management in energy-intensive industries for electricity markets in Germany , 2011 .

[15]  Cornelius Pieper,et al.  Electricity storage: Making large-scale adoption of wind and solar energies a reality , 2012 .

[16]  R. Belmans,et al.  Transmission investment problems in Europe: Going beyond standard solutions , 2011 .

[17]  D. J. Swider,et al.  The costs of wind's intermittency in Germany: application of a stochastic electricity market model , 2007 .

[18]  David Hinkley,et al.  Bootstrap Methods: Another Look at the Jackknife , 2008 .

[19]  George B. Dantzig,et al.  Linear Programming Under Uncertainty , 2004, Manag. Sci..

[20]  Andreas Ehrenmann,et al.  Space and Time: Wind in an Investment Planning Model , 2006 .

[21]  N. Gülpınar,et al.  Analysis of relationship between forward and spot markets in oligopolies under demand and cost uncertainties , 2014, Comput. Manag. Sci..

[22]  Benjamin F. Hobbs,et al.  A decision analysis of the effect of uncertainty upon electric utility planning , 1990 .

[23]  W. Ziemba,et al.  Hedging electricity portfolios via stochastic programming , 2002 .

[24]  Jacques F. Benders,et al.  Partitioning procedures for solving mixed-variables programming problems , 2005, Comput. Manag. Sci..

[25]  M. Thring.  World Energy Outlook , 1977 .

[26]  Otto Rentz,et al.  Model-based analysis of effects from large-scale wind power production , 2007 .

[27]  G. Giebel,et al.  Europe's onshore and offshore wind energy potential : An assessment of environmental and economic constraints , 2009 .

[28]  Julia L. Higle,et al.  An Introductory Tutorial on Stochastic Linear Programming Models , 1999, Interfaces.

[29]  B DantzigGeorge.  Linear Programming Under Uncertainty , 1955 .

[30]  Dominik Möst,et al.  A survey of stochastic modelling approaches for liberalised electricity markets , 2010, Eur. J. Oper. Res..

[31]  Dorota Kurowicka,et al.  Integration of stochastic generation in power systems , 2006 .

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

[33]  Wolf-Peter Schill,et al.  Power System Transformation Toward Renewables: Investment Scenarios for Germany , 2014 .

[34]  A. Soyster,et al.  Electric Utility Capacity Expansion Planning with Uncertain Load Forecasts , 1982 .