IAEE European Conference, September 7-10, 2009 Johannes Schmidt, Erwin Schmid (BOKU University, Austria) Sylvain Leduc, Georg Kindermann

Transcription

1 IAEE European Conference, September 7-10, 2009 Johannes Schmidt, Erwin Schmid (BOKU University, Austria) Sylvain Leduc, Georg Kindermann (International Institute of Applied Systems Analysis, Austria) Erik Dotzauer (Mälardalen University, Sweden)

2 Introduction Modeling Approach Uncertainty Results, Sensitivity & Uncertainty Analysis Further modeling steps Conclusions

3 Forest cover Austria: 47% Not harvested to maximum sustainable yield

4 New technology biomass integrated combined cycle power plant with combined heat and power production (BIGCC-CHP) Higher efficiency than steam engine only Technology not yet commercially available Assessment of potentials of technology to contribute to Kyoto/2020 targets

5 Determination of Potentials of biomass based BIGCC in Austria Considering economic and technical constraints Forest wood availability and costs Transportation distances and costs CHP production efficiencies and investment/production costs Spatial and temporal distribution of heat demand Low heat demand densities imply high distribution costs (exponential function) Seasonal variations in heat demand limit heat use of CHP plants Energy and CO -Prices

6 Spatially explicit optimization model of CHP production Facility Location Problem (as spatial factors matter), Mixed Integer Program Static, 1 year of operation with several heat demand seasons

7 Forest Grid Power Power Distribution Local Heat Transportation CHP plant Peak Demand Boiler Heat transportation pipeline Heat distribution network Building Settlement 7

8 Maximum sustainable yields (MSY) are modelled with G4-model MSY reduced by current wood consumption Only small diameter trees are considered for bioenergy production 8

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10 Bottom up modelling of heat demand Spatial distribution of dwellings and of commercial/industrial activity Data from census Calibrated to national consumption known from the energy balance 10

11 11 Heating Demand Estimation Heating Demand MWh/km2/a

12 Input parameters (e.g. energy prices, wood supply, district heating infrastructure costs) not known exactly Instead of using mean values, input parameters are therefore assumed to be normally and independently distributed 9 input parameters stochastically modeled Used in a Monte Carlo simulation (1000 runs)

13 Optimization model is replaced by linear meta model Linear regression of results on input parameters The meta model is used to calculate Elasticities between input parameters and output variables Contribution to model uncertainty by single parameters. Metamodel 9 p tot =! + 0! l parl l = 1 13

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17 Power Price Power Price Biomass Supply Biomass Supply Local Heat. Cost Local Heat. Cost Connection Rat. Connection Rat. CO 2- Price CO 2 -Price Trans. Cost Trans. Cost DH Cost DH Cost CHP Inv. Cost CHP Inv. Cost Biomass Cost Biomass Cost MdAPE(%) - Power MdAPE(%) - Heat

18 Market feedbacks on biomass prices Fuel switch in existing district heating networks Consider correlation of parameters in the MCsimulation. Energy (power, heat) and wood prices may be strongly correlated 18

19 BIGCC promising technology 3% (with 90% Probability) of Austrian power production supplied at current power prices Around 1 MtCO 2 emission offsets. Up to 12% at higher prices 28% of renewable energy goal at current demand levels 4.3 MtCO 2 offsets Heat to power ratio decreasing due to limited district heating demand Highest uncertainty due to power price volatility Model uncertainty reductions through more research on district heating costs

20 Doctoral School Sustainable Development, University of Natural Ressources and Applied Life Sciences, Vienna International Institute of Applied Systems Analysis, Laxenburg, Austria Mälardalen University, Sweden