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Energy-efficient building retrofits: An assessment of regulatory proposals under uncertainty

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  • Rocha, Paula
  • Kaut, Michal
  • Siddiqui, Afzal S.

Abstract

Improving energy efficiency in European Union buildings will require retrofitting much of the existing stock due to limited new construction opportunities. Given uncertainty in energy prices and technology costs stemming from deregulation, a stochastic optimisation framework is desirable for long-term decision support. We synthesise treatment of uncertainty and risk management to obtain insights about the impact of retrofits on energy consumption, costs, CO2 emissions, and risk at real buildings in Austria and Spain. The optimal strategy for the Spanish site is to invest in photovoltaic and solar thermal technologies. This lowers expected costs by 8.5% and reduces expected primary energy consumption and CO2 emissions by 20% relative to using existing equipment. By limiting exposure to volatile energy prices, the strategy also yields a nearly 10% reduction in risk. We obtain similar results also for the Austrian site. Via this framework, tradeoffs among competing objectives and the effectiveness of proposed regulation may be assessed. Specifically, we find that more stringent restrictions on energy efficiency are economically viable if regulation also facilitates enhanced operational decision support for buildings. Indeed, primary energy consumption can be lowered only through more on-site generation such as combined heat and power, which is more complex for building managers to deploy.

Suggested Citation

  • Rocha, Paula & Kaut, Michal & Siddiqui, Afzal S., 2016. "Energy-efficient building retrofits: An assessment of regulatory proposals under uncertainty," Energy, Elsevier, vol. 101(C), pages 278-287.
  • Handle: RePEc:eee:energy:v:101:y:2016:i:c:p:278-287
    DOI: 10.1016/j.energy.2016.01.037
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    References listed on IDEAS

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    3. Ascione, Fabrizio & Bianco, Nicola & De Stasio, Claudio & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2017. "Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach," Energy, Elsevier, vol. 118(C), pages 999-1017.
    4. Copiello, Sergio & Gabrielli, Laura & Bonifaci, Pietro, 2017. "Evaluation of energy retrofit in buildings under conditions of uncertainty: The prominence of the discount rate," Energy, Elsevier, vol. 137(C), pages 104-117.
    5. Jie, Pengfei & Zhang, Fenghe & Fang, Zhou & Wang, Hongbo & Zhao, Yunfeng, 2018. "Optimizing the insulation thickness of walls and roofs of existing buildings based on primary energy consumption, global cost and pollutant emissions," Energy, Elsevier, vol. 159(C), pages 1132-1147.
    6. Michal Kaut, 2024. "Handling of long-term storage in multi-horizon stochastic programs," Computational Management Science, Springer, vol. 21(1), pages 1-26, June.
    7. Luan Santos & Karl Steininger & Marcelle Candido Cordeiro & Johanna Vogel, 2022. "Current Status and Future Perspectives of Carbon Pricing Research in Austria," Sustainability, MDPI, vol. 14(15), pages 1-28, August.

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