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Michaela Fürsch , Stephan Nagl

The Impact of Uncertain Future RES-E Deployment on the Electricity System - An Evaluation for Germany. Michaela Fürsch , Stephan Nagl. IAEE Conference Stockholm, 22/6/2011. Content. Motivation 2. Methodology Multistage- Stochastic Investment and Dispatch Model Scenario tree

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Michaela Fürsch , Stephan Nagl

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  1. The Impact of Uncertain Future RES-E Deployment on the Electricity System -An Evaluation for Germany Michaela Fürsch, Stephan Nagl • IAEE Conference Stockholm, 22/6/2011

  2. Content • Motivation 2. Methodology • Multistage-Stochastic Investment andDispatch Model • Scenario tree 3. Results • Optimal electricity mix underuncertainty • Costsofuncertainty 4. Conclusions

  3. Motivation – RES-E deployment in Germany since 1990

  4. Motivation – Underestimationofcurrent RES-E deployment

  5. Motivation – Future RES-E deploymentisuncertain

  6. Research question: Consequencesofuncertainty Uncertaindeploymentof RES-E (policy-drivenpartofelectricitysystem) Uncertaintyaboutlevelandstructureof residual load Competitivepartofelectricitysystem: longplanning, construction, amortizationtimes Consequencesofuncertainty Multistage stochastic model: Optimal electricity mix underuncertainty Deterministicsolutionsofbranches: Optimal electricity mix undercertainty

  7. Multistage stochastic Investment andDispatch Model • linear dynamicinvestmentanddispatch model • minimizes total (discounted) systemcosts • neededtofulfill residual demand in eachscenarionode • consideringoccurenceprobabilityofeachnode • Optimal electricity mix underuncertainty

  8. Multistage stochastic Investment andDispatch Model Determination ofInstalledCapacity in node n iftechnicallifetime exceeded n2, n1 n1 n (…) n1 n: „current“ node n2: directancestorof n n1: all directandindirectancestorsof n (…) (…)

  9. Scenario treebased on RES-E scenariosandforecastsfor Germany high high Adjustedinvestments high high low low > NREAP 2020 high high high high low low low low NREAP2015 NREAP 2020 (…) high high < NREAP 2020 (…) low low Optimal investmentunderuncertainty 2010 high high high high low low high high NREAP 2020 high high low low low low < NREAP 2015 < NREAP 2020 (…) low low

  10. Scenario tree – resulting residual loadcurve (2050)(Scenario 1 (max. RES-E) vs. Scenario 20 (min RES-E)) Res. Load = load – fluctuating RES-E

  11. Deterministic vsstochastic: - 5 GW Coal - 2 GW CCGT Results Stochastic Solution InstalledCapacity > NREAP 2020 ++ NREAP2015 NREAP 2020 ++ CAP_ADD 6.9 lignite 5,1 coal 3.4 CCGT 0.5 OCGT < NREAP 2020 + Fullloadhours 2010 • Deterministic • vsstochastic: • + 10 GW Coal • - 4 GW CCGT < NREAP 2015

  12. Results - Costs Extra costsofstoch. solution SevereConsequencesofuncertainty? • Extra costsseem not verysignificant, • compared e.g. to RES-E promotioncosts in 2010 • BUT: • Extra costsdepend e.g. on gas-coalspread, discount rate etc. • onlycostsif form ofuncertaintyisknown

  13. Conclusions 1 Uncertain RES-E deploymentpaths => Uncertaintyaboutlevel + structureof residual load 2 Uncertaintyleadsto suboptimal electricity mix 3 Uncertaintyinduces extra costs (depending e.g. on gas-coalspread) Benefitofflexibility in RES-E policyhastobeweightedagainstcostsofuncertainty

  14. Thank you for your audience. Questions, comments? Contact: Michaela Fürsch Michaela.Fuersch@uni-koeln.de

  15. BACK-UP Präsentationstitel | Datum | Autor

  16. Fuel and CO2-price assumptions Präsentationstitel | Datum | Autor

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