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High-Resolution Modeling of Energy-Services Supply Systems Using deeco: Overview and Application to Policy Development

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  • Thomas Bruckner
  • Robbie Morrison
  • Chris Handley
  • Murray Patterson

Abstract

Contemporary energy policy problems typically involve issues of (1) technology selection, placement, and scheduling, (2) energy-services demand modification by location and time-of-use, and/or (3) new sourcing options including emerging renewables. The high-resolution energy systems modeling environment deeco (dynamic energy, emissions, and cost optimization) naturally captures interactions between these components. deeco can assist with the search for policy sets which reduce CO 2 and/or displace depletable resource use and which take advantage of cost-effective system integration synergies. The network management objective may be treated as an exogenous variable and process performance can depend on the thermodynamic intensive state of the system. Numerical studies indicate that multiple policy interventions cannot be assumed to be independent and that staging can be significant. Copyright Kluwer Academic Publishers 2003

Suggested Citation

  • Thomas Bruckner & Robbie Morrison & Chris Handley & Murray Patterson, 2003. "High-Resolution Modeling of Energy-Services Supply Systems Using deeco: Overview and Application to Policy Development," Annals of Operations Research, Springer, vol. 121(1), pages 151-180, July.
    • Handle: RePEc:spr:annopr:v:121:y:2003:i:1:p:151-180:10.1023/a:1023359303704
    DOI: 10.1023/A:1023359303704
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    Citations

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    Cited by:

    1. Scheller, Fabian & Bruckner, Thomas, 2019. "Energy system optimization at the municipal level: An analysis of modeling approaches and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 444-461.
    2. Keirstead, James & Samsatli, Nouri & Shah, Nilay & Weber, Céline, 2012. "The impact of CHP (combined heat and power) planning restrictions on the efficiency of urban energy systems," Energy, Elsevier, vol. 41(1), pages 93-103.
    3. Scheller, Fabian & Burgenmeister, Balthasar & Kondziella, Hendrik & Kühne, Stefan & Reichelt, David G. & Bruckner, Thomas, 2018. "Towards integrated multi-modal municipal energy systems: An actor-oriented optimization approach," Applied Energy, Elsevier, vol. 228(C), pages 2009-2023.
    4. Yazdanie, Mashael & Densing, Martin & Wokaun, Alexander, 2017. "Cost optimal urban energy systems planning in the context of national energy policies: A case study for the city of Basel," Energy Policy, Elsevier, vol. 110(C), pages 176-190.
    5. Keirstead, James & Calderon, Carlos, 2012. "Capturing spatial effects, technology interactions, and uncertainty in urban energy and carbon models: Retrofitting newcastle as a case-study," Energy Policy, Elsevier, vol. 46(C), pages 253-267.
    6. Henning, Dag & Amiri, Shahnaz & Holmgren, Kristina, 2006. "Modelling and optimisation of electricity, steam and district heating production for a local Swedish utility," European Journal of Operational Research, Elsevier, vol. 175(2), pages 1224-1247, December.

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