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Using Monte Carlo Simulation to Account for Uncertainties in the Spatial Explicit Modeling of Biomass Fired Combined Heat and Power Potentials in Austria

Author

Listed:
  • Johannes Schmidt

    () (Doctoral School Sustainable Development (dokNE), University of Natural Resources and Applied Life Sciences, Vienna)

  • Sylvain Leduc

    (International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361 Laxenburg, Austria)

  • Erik Dotzauer

    (Mälardalen University, Box 883, SE-72123 Västerås, Sweden)

  • Georg Kindermann

    (International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361 Laxenburg, Austria)

  • Erwin Schmid

    () (Institute for Sustainable Economic Development, Department of Economics and Social Sciences, University of Natural Resources and Applied Life Sciences, Vienna)

Abstract

Austria aims at increasing its share of renewable energy production by 11% until 2020. Combined Heat and Power (CHP) plants fired by forest wood can significantly contribute to attaining this target. However, the spatial distribution of biomass supply and of heat demand limits the potentials of CHP production. This paper assesses CHP potentials using a mixed integer programming model that optimizes locations of bioenergy plants. Investment costs of district heating infrastructure are modeled as a function of heat demand densities, which can differ substantially. Gasification of biomass in a combined cycle process is assumed as production technology. Some model parameters have a broad range according to a literature review. Monte-Carlo simulations have therefore been performed to account for model parameter uncertainty in our analysis. Optimal locations of plants are clustered around big cities in the East of Austria. At current power prices, biomass based CHP production allows producing around 3% of Austria’s total current energy demand. Yet, the heat utilization decreases when CHP production increases due to limited heat demand that is suitable for district heating.

Suggested Citation

  • Johannes Schmidt & Sylvain Leduc & Erik Dotzauer & Georg Kindermann & Erwin Schmid, 2009. "Using Monte Carlo Simulation to Account for Uncertainties in the Spatial Explicit Modeling of Biomass Fired Combined Heat and Power Potentials in Austria," Working Papers 432009, Institute for Sustainable Economic Development, Department of Economics and Social Sciences, University of Natural Resources and Life Sciences, Vienna.
  • Handle: RePEc:sed:wpaper:432009
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    References listed on IDEAS

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    2. Styles, David & Jones, Michael B., 2007. "Current and future financial competitiveness of electricity and heat from energy crops: A case study from Ireland," Energy Policy, Elsevier, vol. 35(8), pages 4355-4367, August.
    3. Sjödin, Jörgen & Henning, Dag, 2004. "Calculating the marginal costs of a district-heating utility," Applied Energy, Elsevier, vol. 78(1), pages 1-18, May.
    4. Streicher, Gerhard & Schmid, Erwin & Salhofer, Klaus, 2006. "Testing for the Efficiency of a Policy Intended to Meet Objectives: General Model and Application," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 31(02), August.
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    6. Marbe, Å & Harvey, S & Berntsson, T, 2004. "Biofuel gasification combined heat and power—new implementation opportunities resulting from combined supply of process steam and district heating," Energy, Elsevier, vol. 29(8), pages 1117-1137.
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    Cited by:

    1. repec:eee:appene:v:206:y:2017:i:c:p:1088-1101 is not listed on IDEAS
    2. Ba, Birome Holo & Prins, Christian & Prodhon, Caroline, 2016. "Models for optimization and performance evaluation of biomass supply chains: An Operations Research perspective," Renewable Energy, Elsevier, vol. 87(P2), pages 977-989.
    3. Awudu, Iddrisu & Zhang, Jun, 2012. "Uncertainties and sustainability concepts in biofuel supply chain management: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1359-1368.

    More about this item

    Keywords

    Combined Heat and Power; District Heating; Bioenergy; Biomass; Mixed Integer Programming; Monte-Carlo Simulation;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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