Computational Management Science

, Volume 11, Issue 1–2, pp 25–44 | Cite as

Energy efficiency and risk management in public buildings: strategic model for robust planning

  • Emilio L. Cano
  • Javier M. Moguerza
  • Tatiana Ermolieva
  • Yuri Ermoliev
Original Paper


Due to deregulations of the energy sector and the setting of targets such as the 20/20/20 in the EU, operators of public buildings are now more exposed to instantaneous (short-term) market conditions. On the other hand, they have gained the opportunity to play a more active role in securing long-term supply, managing demand, and hedging against risk while improving existing buildings’ infrastructures. Therefore, there are incentives for the operators to develop and use a Decision Support System to manage their energy sub-systems in a more robust energy-efficient and cost-effective manner. In this paper, a two-stage stochastic model is proposed, where some decisions (so-called first-stage decisions) regarding investments in new energy technologies have to be taken before uncertainties are resolved, and some others (so-called second-stage decisions) on how to use the installed technologies will be taken once values for uncertain parameters become known, thereby providing a trade-off between long- and short-term decisions.


Decision making under uncertainty Stochastic programming  Energy optimisation Risk management 



This work is partially supported by the European Commission’s Seventh Framework Programme via the “Energy Efficiency and Risk Management in Public Buildings” (EnRiMa) project (Number 260041). We acknowledge the rest of the partners of the project, whose contributions to the project have somehow influenced the authors: Stockholm University (Sweden), University College London (UK), SINTEF Group (Norway), International Institute for Applied Systems Analysis-IIASA (Austria), Center for Energy and Innovative Technologies-CET (Austria), Tecnalia Research and Innovation (Spain), HC Energia (Spain), and Minerva Consulting and Communication (Belgium). We also acknowledge national projects OPTIMOS3 (MTM2012-36163-C06-06), RIESGOS-CM (code S2009/ESP-1685), AGORANET (IPT- 430000-2010-32) CONTENT & INTELIGENCE (IPT-2012-0912-430000), HAUS (IPT-2011-1049-430000), EDUCALAB (IPT-2011-1071-430000), DEMOCRACY4ALL (IPT-2011-0869-430000) and CORPORATE COMMUNITY (IPT-2011-0871-430000).


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Emilio L. Cano
    • 1
  • Javier M. Moguerza
    • 1
  • Tatiana Ermolieva
    • 2
  • Yuri Ermoliev
    • 2
  1. 1.Universidad Rey Juan Carlos MadridSpain
  2. 2.International Institute for Applied Systems Analysis (IIASA) LaxenburgAustria

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