Natural wind variability triggered drop in German redispatch volume and costs from 2015 to 2016

Avoiding dangerous climate change necessitates the decarbonization of electricity systems within the next few decades. In Germany, this decarbonization is based on an increased exploitation of variable renewable electricity sources such as wind and solar power. While system security has remained constantly high, the integration of renewables causes additional costs. In 2015, the costs of grid management saw an all time high of about € 1 billion. Despite the addition of renewable capacity, these costs dropped substantially in 2016. We thus investigate the effect of natural climate variability on grid management costs in this study. We show that the decline is triggered by natural wind variability focusing on redispatch as a main cost driver. In particular, we find that 2016 was a weak year in terms of wind generation averages and the occurrence of westerly circulation weather types. Moreover, we show that a simple model based on the wind generation time series is skillful in detecting redispatch events on timescales of weeks and beyond. As a consequence, alterations in annual redispatch costs in the order of hundreds of millions of euros need to be understood and communicated as a normal feature of the current system due to natural wind variability.

[1]  Mike Hulme,et al.  A COMPARISON OF LAMB CIRCULATION TYPES WITH AN OBJECTIVE CLASSIFICATION SCHEME , 1993 .

[2]  N Oreskes,et al.  Verification, Validation, and Confirmation of Numerical Models in the Earth Sciences , 1994, Science.

[3]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[4]  George Makrides,et al.  Modeling the photovoltaic potential of a site , 2010 .

[5]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[6]  Martin Greiner,et al.  Storage and balancing synergies in a fully or highly renewable pan-European power system , 2012 .

[7]  Karen Smith Stegen,et al.  The winds of change: How wind firms assess Germany's energy transition , 2013 .

[8]  John Methven,et al.  Implications of the North Atlantic Oscillation for a UK–Norway Renewable power system , 2013 .

[9]  J. Peinke,et al.  Turbulent character of wind energy. , 2013, Physical review letters.

[10]  R. Trigo,et al.  The Impact of the North Atlantic Oscillation on Renewable Energy Resources in Southwestern Europe , 2013 .

[11]  R. Trigo,et al.  Spatio-temporal Complementarity between Solar and Wind Power in the Iberian Peninsula☆ , 2013 .

[12]  G. Luderer,et al.  System LCOE: What are the Costs of Variable Renewables? , 2013 .

[13]  Hans-Josef Allelein,et al.  Impacts of the transformation of the German energy system on the transmission grid , 2014 .

[14]  C. Frantzidis,et al.  Response to Reviewers Reviewer #1 , 2010 .

[15]  Johan Lilliestam,et al.  Potential for concentrating solar power to provide baseload and dispatchable power , 2014 .

[16]  Thomas Hamacher,et al.  Integration of wind and solar power in Europe: Assessment of flexibility requirements , 2014 .

[17]  Mark Z. Jacobson,et al.  Features of a fully renewable US electricity system: Optimized mixes of wind and solar PV and transmission grid extensions , 2014, 1402.2833.

[18]  J. Pinto,et al.  Statistical–dynamical downscaling for wind energy potentials: evaluation and applications to decadal hindcasts and climate change projections , 2014 .

[19]  G. Magnusdottir,et al.  Forcing of the wintertime atmospheric circulation by the multidecadal fluctuations of the North Atlantic ocean , 2014 .

[20]  Martin Greiner,et al.  Transmission needs across a fully renewable European power system , 2013, 1306.1079.

[21]  G. Luderer,et al.  Energy system transformations for limiting end-of-century warming to below 1.5 °C , 2015 .

[22]  J. Pinto,et al.  Future changes of wind energy potentials over Europe in a large CMIP5 multi‐model ensemble , 2015 .

[23]  Dirk J. Cannon,et al.  Using reanalysis data to quantify extreme wind power generation statistics: A 33 year case study in Great Britain , 2015 .

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

[25]  David Kleinhans,et al.  Integration of Renewable Energy Sources in future power systems: The role of storage , 2014, 1405.2857.

[26]  M. A. Cameron,et al.  Low-cost solution to the grid reliability problem with 100% penetration of intermittent wind, water, and solar for all purposes , 2015, Proceedings of the National Academy of Sciences.

[27]  Mark Z. Jacobson,et al.  Renewable build-up pathways for the US: Generation costs are not system costs , 2015 .

[28]  Kara Clark,et al.  Alternatives No More: Wind and Solar Power Are Mainstays of a Clean, Reliable, Affordable Grid , 2015, IEEE Power and Energy Magazine.

[29]  Baoping Shang,et al.  How Large Are Global Energy Subsidies , 2016 .

[30]  Lion Hirth,et al.  Carpe diem: A novel approach to select representative days for long-term power system modeling , 2016 .

[31]  Joeri Rogelj,et al.  Science and policy characteristics of the Paris Agreement temperature goal , 2016 .

[32]  J. Rogelj,et al.  Paris Agreement climate proposals need a boost to keep warming well below 2 °C , 2016, Nature.

[33]  J. Pinto,et al.  Decadal predictability of regional scale wind speed and wind energy potentials over Central Europe , 2016 .

[34]  L. Shaffrey,et al.  Quantifying the increasing sensitivity of power systems to climate variability , 2016 .

[35]  S. Pfenninger,et al.  Using bias-corrected reanalysis to simulate current and future wind power output , 2016 .

[36]  Patrick Sullivan,et al.  System Integration of Wind and Solar Power in Integrated Assessment Models: A Cross-Model Evaluation of New Approaches , 2017 .

[37]  William D'haeseleer,et al.  Impact of the level of temporal and operational detail in energy-system planning models , 2016 .

[38]  Dirk Witthaut,et al.  More Homogeneous Wind Conditions Under Strong Climate Change Decrease the Potential for Inter-State Balancing of Electricity in Europe , 2017 .

[39]  Martin Greiner,et al.  The benefits of cooperation in a highly renewable European electricity network , 2017, 1704.05492.

[40]  Maximilian Auffhammer,et al.  North–south polarization of European electricity consumption under future warming , 2017, Proceedings of the National Academy of Sciences.

[41]  Robert C. Pietzcker,et al.  Decarbonizing global power supply under region-specific consideration of challenges and options of integrating variable renewables in the REMIND model , 2017 .

[42]  Stefan Pfenninger,et al.  Dealing with multiple decades of hourly wind and PV time series in energy models: A comparison of methods to reduce time resolution and the planning implications of inter-annual variability , 2017 .

[43]  N. Nakicenovic,et al.  A roadmap for rapid decarbonization , 2017, Science.

[44]  Thomas Huld,et al.  Simulating European wind power generation applying statistical downscaling to reanalysis data , 2017 .

[45]  Adam A. Scaife,et al.  The relationship between wind power, electricity demand and winter weather patterns in Great Britain , 2017 .

[46]  I. Staffell,et al.  The impact of climate change on the levelised cost of wind energy , 2017 .

[47]  Christian Breyer,et al.  Electricity system based on 100% renewable energy for India and SAARC , 2017, PloS one.

[48]  S. Pfenninger,et al.  Balancing Europe’s wind power output through spatial deployment informed by weather regimes , 2017, Nature climate change.

[49]  Francesco Panzeri,et al.  Multispot single-molecule FRET: High-throughput analysis of freely diffusing molecules , 2016, bioRxiv.

[50]  Christian Breyer,et al.  Hydro, wind and solar power as a base for a 100% renewable energy supply for South and Central America , 2017, PloS one.

[51]  Dirk Witthaut,et al.  Modeling long correlation times using additive binary Markov chains: Applications to wind generation time series. , 2018, Physical review. E.