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Stephan Nagl and Michaela Fürsch International Association for Energy Economics

The impact of the stochastic feed-in of wind and solar technologies on the optimal electricity generation mix in high RES-E scenarios. Stephan Nagl and Michaela Fürsch International Association for Energy Economics Stockholm, June 19 – 23, 2011. Content. Motivation and model approach

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Stephan Nagl and Michaela Fürsch International Association for Energy Economics

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  1. The impact of the stochastic feed-in of wind and solar technologies on the optimal electricity generation mix in high RES-E scenarios • Stephan Nagl and Michaela Fürsch • International AssociationforEnergy Economics • Stockholm, June 19 – 23, 2011

  2. Content • Motivation and model approach • Scenario generation: feed-in of wind and solar technologies • Stochasticoptimization model: cost-minimal electricity mix • Conclusion

  3. Motivation • Feed-in structuresof wind and solar technologiesarestochastic • Amountofyearlyelectricitygenerationisstochastic (+/- 20%) • Negative correlationbetween wind and solar radiation • Extreme events such astwoweekswithhardlyany wind

  4. Model approach I. Scenario generationtool • wind speedand solar radiationfor 8760 hours • localconditionsconsideredbymodelingseveralregions in Germany andits European neighbours Feed-in structuresforphotovoltaicsand wind turbines II. Stochasticinvest- anddispatch model • usingfeed-in structuresasinputparameters Cost-minimal electricity mix (capacitiesandgeneration) III. Comparisonto an averagescenario (deterministic model) • modeling an averagefeed-in scenario 4

  5. Scenario generationtool: characteristics Characteristicsof wind • Approximation byWeibulldistributions (scaleandshapeparameter) • Change of wind speeddepends on wind level • Seasonaldifferences (e.g. higher wind speeds in fall/winter) • Localdifferences (e.g. higher wind speedsatthecoastlines) Characteristicsof solar radiation • Typicaldailystructure • Higher solar radiation in summerthan winter months • Localdifferences (e.g. higherfullloadhours in Southern Germany) Unlimitedamountofcombined wind and solar scenarios

  6. Scenario generationtool: 100 scenariosas an example Scenario I Distribution offullloadhours Scenario II

  7. Fluctuating RES-E in a stochasticoptimization model- stochastic model - 1-stage: investmentdecision • Investments in conventional (CCS), renewable, storageand CHP technologies 2-stage: electricitysupply (depending on wind and solar feed-in scenario) • Electricitysupply in eachhour • Restrictions: ramp-uptimes, storageequations • RES-E quota on grosselectricitydemand (averagequota) Cost minimal electricity mix for different RES-E quotas

  8. Fluctuating RES-E in a stochasticoptimization model- stochastic model results -

  9. Fluctuating RES-E in a stochasticoptimization model- comparisontodeterministic model results -

  10. Conclusion Effectsofmodelingstochasticfeed-in structures • Lowervalueoffluctuating RES-E; higherinvestments in biomassand geothermal • Negative wind/PV correlationleadstoinvestments in PV • Higher total costsforelectricitysupply Model resultsforthe German electricitysystem • Model resultssuggest a great mix oftechnologies • Biomassplays a significantrole in high RES-E scenarios • Higher costsforelectricitysupplythan in politicalplans

  11. Thank you for your audience. Questions, comments? Contact: Stephan Nagl Stephan.Nagl@uni-koeln.de

  12. Feed-in scenariosof wind technologies Characteristicsof wind • Approximation byWeibulldistributions (scaleandshapeparameter) • Change of wind speeddepends on wind level • Seasonaldifferences (e.g. higher wind speeds in fall/winter) • Localdifferences (e.g. higher wind speedsatthecoastlines) Model approach  Calculationof wind speeds in Germany central: vcentral=scalecentral* [ -ln(1-Xcentral*)1/shape] 0 < Xcentral≤ 1 Xcentralis a stochastic variable, whichdepends on thehours h-1. Northern and southern Germany as well as 3 EU regionsbased on Germany central Scalingof wind speedsfrom 10 meterstoturbineheight Calculationoffeed-in structuresforturbines (standardturbine power curves)

  13. Feed-in scenariosof solar technologies Characteristicsof solar radiation • Typicaldailystructure • Higher solar radiation in summerthan winter months • Localdifferences (e.g. higherfullloadhours in Southern Germany) Model approach  Long-termmonthlyaverageof solar radiation (1996-2000) usedasbasis Stochastic variable asdifferencefromlong- termaveragebytakingthe negative correlation wind/solar intoaccount (depends on wind level) Calculationoffeed-in structuresofphotovoltaics (same regionsasfor wind)

  14. Fluctuating RES-E in a stochasticoptimization model- stochastic model results- 80 % RES-E quota

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